TW201908895A - Method and apparatus for health assessment of a transport apparatus - Google Patents

Abstract

A method for health assessment of a system including a transport apparatus including registering predetermined operating data embodying at least one dynamic performance variable output by the transport apparatus, determining a base value (CpkBase) characterized by a probability density function of each of the dynamic performance variable output, resolving from the transport apparatus in situ process motion commands of the apparatus controller and defining another predetermined motion set of the transport apparatus, registering predetermined operating data embodying the at least one dynamic performance variable output by the transport apparatus and determining with the processor another value (CpkOther) characterized by the probability density function of each of the dynamic performance variable output by the transport apparatus, and comparing the other value and the base value (CpkBase) for each of the dynamic performance variable output by the transport apparatus respectively corresponding to the predetermined motion base set and the other predetermined motion set.

Description

Method and apparatus for health assessment of a transport device

CROSS-REFERENCE TO RELATED APPLICATIONS This application is hereby incorporated by reference in its entirety in its entirety the entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire entire all all all all all all all all each The exemplary embodiments relate generally to automated processing systems.

1. FIELD: Exemplary embodiments relate more specifically to health assessment and predictive diagnosis of automated processing systems.

2. Brief description of related developments: Material damage and unplanned downtime due to failure of robotic manipulators and other electromechanical components used in automated manufacturing tools such as automated material handling platforms for the production of semiconductor components are common problems, which are usually Represents a huge cost burden for end users of manufacturing tools.

Many health-monitoring and fault-diagnostic (HMFD) methods have been developed for industrial, automotive and aerospace applications. Existing systems typically implement fault detection to indicate an error in some places in the monitored system, fault isolation to determine the exact location of the fault (ie, the component of the fault), and fault identification to determine the fault size.

This isolation, along with this identification task, is often referred to as fault diagnosis. Many existing systems only perform fault detection and isolation phases.

Although these fault diagnosis schemes contribute to fault detection, where isolation and adaptive recovery still allow components, tools, FABs (eg, manufacturing facilities/factories) or other automated devices to have a limited or substantially non-existent prediction range Essentially respond to the way to run. The prediction method is recognized as an attempt to add a prediction range to a fault diagnosis system, such as mathematical modeling of an automated device, in which the sensing measurements of the automation device variables are associated with various variables (eg, Newton dynamics models or classes from automated devices) The calculated values of the neural network dynamics model are compared and the mathematical model represents the nominal condition. This approach is affected by non-conservative factors such as signal noise and model errors that unpredictably and adversely affect the difference in results between the analytical (nominal) value and the value measured by the sensing measurements, and The fault diagnosis system makes further investments in processing capabilities and/or repetitive/redundant sensing systems and data systems to address this non-conservative factor.

It would be advantageous to have a fault diagnosis system that provides fault prediction without mathematical modeling associated with non-conservative factors.

and

Although the disclosed embodiments will be described with reference to the drawings, it is understood that the aspects of the disclosed embodiments may be embodied in many forms. In addition, any suitable size, shape or type of element or material may be used.

Aspects of the disclosed embodiments described herein provide a method and apparatus for quantifying health status and predictive diagnostics of an automated system, such as those described herein with respect to Figures 1-4E, using available variables, which are available The variables are monitored by any suitable controller of the automated system (wherein the controller includes non-transitory computer software code for implementing aspects of the disclosed embodiments). The measurement of the state of health is achieved by the unique aspect of the disclosed embodiments by unique statistical processing of the collected variables, the collected variables being uniquely associated with a given device and/or system of the plurality of devices and A given device and/or system of the plurality of devices is uniquely characterized as a quantity of health status that provides a predictive diagnosis of the given device and/or system. Aspects of the disclosed embodiments may allow a controller of an automated system to determine the statistics of the monitored variables (of the unique elements) using the concept of "baseline" (which includes basic values and/or basic motions as described herein). Characteristics (a unique feature of this unique component) and facilitate further comparison of future performance with these baselines. As a result, the method and apparatus of the disclosed embodiments may allow the controller of the automated system to perform predictions based on trend analysis, allowing the controller of the automation system to make recommendations for preventive maintenance based on material that is distinct from the monitored automation system. . Aspects of the disclosed embodiments may also allow for the identification of expected limits for operation of variables that are difficult to determine acceptable and feasible specifications.

Although the embodiment of the disclosed embodiment of a semiconductor robot (also referred to herein as a robotic manipulator) having three degrees of freedom (theta rotation, R extension, and Z lifting motion) will be described herein, in other aspects, the semiconductor The robot can have more or less than three degrees of freedom. In still other aspects, the disclosed embodiments can be applied to other components of a semiconductor processing system having a single motion (eg, robotic transport, load port, aligner, pump, fan, valve, etc.) degrees of freedom. It should also be understood that aspects of the disclosed embodiments can be applied to any automated and/or powered component or system (including, for example, a combination of the above-described devices and/or components), any of the automated and/or powered components or The system is capable of sampling similar or related performance monitoring data, wherein the performance monitoring data uniquely relates to each unique device, component, and/or system and uniquely characterizes each unique device, component, and/or system.

Aspects of the disclosed embodiments provide a metric based on a version of statistical parameter normalization that allows for direct comparison of variables of different physical meanings (eg, temperature versus maximum torque). Such comparisons allow calculation of the effect of these extraneous variables on the overall health of the monitored automation system.

1 shows an exemplary controller 100 for an automated device including an automated device health assessment and predictive diagnostics in accordance with aspects of the disclosed embodiments. Aspects of the disclosed embodiments can operate in hardware or software. For example, aspects of the disclosed embodiments may reside in a component controller, a controller that directs operation of multiple components, a controller of a control subsystem, or a system controller. Aspects of the disclosed embodiments can also be implemented with dedicated hardware or software.

The controller 100 can be any suitable controller of an automated device, such as the automated material processing platform 300 shown in FIG. 3, and can generally include a processor 105, a read-only memory 110, and a random access memory 115. The program storage 120, the user interface 125, and the network interface 130. The processor 105 can include built-in cache memory 135 and is generally operable to access a computer program product, such as a computer usable medium (eg, such as built-in cache memory 135, read only memory 110, random access). The memory 115 and the program storage 120) read information and programs.

When the power is turned on, the processor 105 can start operating the program found in the read-only memory 110, and after initialization, the instructions from the program memory 120 can be loaded into the random access memory 115 and controlled in those programs. Under the operation. Frequently used instructions can be temporarily stored in the built-in cache memory 135. Both read-only memory 110 and random access memory 115 may utilize semiconductor technology or any other suitable materials and techniques. The program storage 120 may include a magnetic disk, a computer hard disk, a compact disk (CD), a digital compact disk (DVD), an optical disk, a chip, a semiconductor, or any computer capable of storing a program in the form of a computer readable code. Other devices.

The built-in cache memory 135, the read-only memory 110, the random access memory 115, and the program storage 120 may include an operating system program alone or in any combination. The operating system program can be supplemented with an optional real-time operating system to improve the quality of the material provided by the function controller 100 and to allow the function controller 100 to provide a guaranteed response time.

In particular, the built-in cache 135, the read-only memory 110, the random access memory 115, and the program storage 120, either alone or in any combination, may be included to cause the processor 105 to perform according to the following text. A program for troubleshooting and fault prediction of the disclosed embodiments. Network interface 130 may generally be adapted to provide an interface between controller 100 and other controllers or other systems. The network interface 130 is operable to receive data from one or more additional function controllers and to communicate the data to the same or other function controllers. The network interface 130 can also provide an interface to a global diagnostic system that can provide remote monitoring and diagnostic services.

Communication network 190 may include a public switched telephone network (PSTN), an internet, a wireless network, a wired network, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), and the like, and Other types of networks may also be included, including X.25, TCP/IP, ATM, and the like.

The controller 100 can include a user interface 125 having a display 140 and an input device such as a keyboard 155 or a mouse 145. The user interface can be operated by the user interface controller 150 under the control of the processor 105 and can provide a graphical user interface to the user to visualize the results of health monitoring and troubleshooting. The user interface can also be used to instruct maintenance personnel to perform troubleshooting routines or repair procedures. In addition, the user interface controller can also provide a connection or interface 155 for communicating with other function controllers, external networks, another control system, or host.

An exemplary material processing platform for fabricating semiconductor components is illustrated diagrammatically in FIG. 2, along with an illustrative description of the main components listed in Table 1, wherein aspects of the disclosed embodiments may be in the semiconductor component achieve. One or more controllers of the material processing platform of FIG. 2 may include a controller as described herein with respect to FIG.

Table 1: An explanatory illustration of the automated material handling platform 300 (also referred to as a processing tool) of Figure 2.

The automated material handling platform 300 has an atmospheric portion 301, a vacuum portion 302, and one or more processing modules 303.

The atmospheric portion 301 can include a housing 304, one or more loadports 305, one or more robotic manipulators 306, one or more substrate aligners 307, and a fan filter unit 308. It may also include one or more ionization units (not shown). The vacuum portion may include a vacuum chamber 309, one or more load locks 310, one or more robotic manipulators 311, one or more vacuum pumps 312, and a plurality of slit valves 313, which are typically located at atmospheric portion 301 and load lock 310 The interface is between the load lock 310 and the vacuum chamber 309 and between the vacuum chamber 309 and the processing module 303.

The operation of the platform is coordinated by a tool controller 314 that monitors the atmospheric portion controller 315, a vacuum portion controller 316, and one or more processing controllers 317. The atmospheric portion controller 315 is responsible for one or more load port controllers 318, one or more atmospheric robot controllers 319, one or more aligner controllers 320, and a fan filter unit controller 321 . Each of load port controller 318, atmospheric robot controller 319, and aligner controller 320 is in turn responsible for one or more motor controllers 322. The vacuum section controller 316 is responsible for one or more vacuum robot controllers 323, controls the vacuum pump 312, and operates the slit valve 313. The role of the processing controller 317 depends on the operations performed in the processing module 303.

In some cases, it may be useful to combine two or more control layers into one controller. For example, the atmospheric robot controller 319 and the corresponding motor controller 322 can be combined in a single centralized robot controller, or the atmospheric portion controller 315 can be combined with the atmospheric robot controller 319 to eliminate the pair of separate controller units. demand.

A five-axis direct drive robotic manipulator 400 may be employed in the automated material processing platform 300 of FIG. 2, wherein one or more of the atmospheric robotic manipulator 306 and the vacuum robotic manipulator 311 are substantially similar to the robotic manipulator 400. A simplified schematic of one such robotic manipulator 400 is provided in FIG. Explanatory notes for the main components are listed in Table 2. In one aspect, aspects of the disclosed embodiments can be implemented within the robotic manipulator 400; however, it should be understood that while the aspects of the disclosed embodiments are described with respect to robotic manipulators, Aspects of the disclosed embodiments can be implemented in any suitable automated portion of automated material handling platform 300, including but not limited to shipping robots, load ports, aligners, pumps, fans, valves, and the like. It is noted that the controller 800 of Figure 8A is a general representation of a controller for any of the automated devices described above. It is noted that the robotic manipulator 400 is illustrated as a five-axis direct drive robotic manipulator for exemplary purposes only, and in other aspects, the robotic manipulator (or other automated portion of the processing tool including aspects of the disclosed embodiments) It can have any suitable number of drive shafts, with any suitable degree of freedom, and with direct or indirect drive systems.

Table 2: An explanatory illustration of the robotic manipulator 400 of FIG.

Referring to FIG. 3, the robotic manipulator 400 is constructed around an open cylindrical block 401 suspended from a circular mounting flange 402. The block 401 includes a vertical rail 403 having a linear bearing 404 to provide guidance to the carrier 405 driven by the brushless DC motor 406 via the ball screw mechanism 407. Carrier 405 houses a pair of coaxial brushless DC motors 408, 409 equipped with optical encoders 410, 411. The upper motor 408 drives a hollow outer shaft 412 that is coupled to the first link 414 of the robotic arm. The lower motor 409 is coupled to a coaxial inner shaft 413 that is coupled to the second link 416 via a belt drive 415. The first link 414 houses a brushless DC motor 417A that drives the upper terminator 420A via a two-stage belt configuration 418A, 419A. The lower terminator 420B is actuated using another DC brushless motor 417B and two stage belt drivers 418B, 419B. Each stage 418A, 418B, 419A, and 419B is designed to have a ratio of 1:2 between the input and output pulleys. Substrates 421A and 421B remain attached to terminators 420A and 420B, respectively, by vacuum actuating edge contact holders, surface contact suction grippers, or passive gripper tools.

Throughout the text, first link 414, second link 416, upper terminator 420A, and lower terminator 420B are also referred to as upper arm, forearm, terminator A, and terminator B, respectively. Point A, point B and point C represent the rotational coupling of the shoulder joint, the elbow joint and the wrist joint, respectively. Point D represents a reference point that indicates the desired position of the center of the substrate on the corresponding terminator.

The control system of the example robotic manipulator can be distributed. It includes a power supply 429, a main controller 422, and motor controllers 423A, 423B, and 423C. The main controller 422 is responsible for supervising tasks and trajectory planning. Each of the motor controllers 423A, 423B, and 423C performs a position and current feedback loop of one or two motors. In FIG. 3, controller 423A controls motors 408 and 409, controller 423B controls motors 417A and 417B, and controller 423C controls motor 406. In addition to performing the feedback loop, the motor controller collects data such as motor current, motor position, and motor speed, and transmits the data to the main controller. Motor controllers 423A, 423B, and 423C are coupled to the main controller via a high speed communication network 425. Since connector A is an infinitely rotating joint, communication network 425 is routed through slip ring 426. Additional electronics units 424A and 424B can be used to support the edge contact holders of terminators 420A and 420B, respectively.

Referring now to Figures 4A-4E, the robotic manipulator 400 of Figure 3 can include any suitable arm linkage mechanism. Suitable examples of arm linkages are, for example, U.S. Patent No. 7,578,649, issued August 25, 2009, 5,794,487, issued on August 18, 1998, 7,946,800, issued on May 24, 2011, November 26, 2002 U.S. Patent Application Nos. 13/293, 717 and 13/, entitled "Dual Arm Robot", filed on Apr. 22, 2011, issued on Jul. 22, 2011, issued to 861, 693, filed on September 5, 2013, entitled "Linear Vacuum Robot with Z Motion and Articulated Arm", the disclosure of which is incorporated herein by reference in its entirety. In the aspect of the disclosed embodiment, at least one transfer arm, boom arm 143, and/or linear slide 144 of each transport unit module 104 can be from a conventional SCARA arm 315 (selectively compliant articulated robot arm) The design of the type (Fig. 4C) is derived from an upper arm 315U, a driven front arm 315F and a constrained terminator 315E, or may be designed from a telescopic arm or any other suitable arm, such as a Cartesian linear sliding arm 314. (Fig. 4B). A suitable example of a transport arm can be found in, for example, U.S. Patent Application Serial No. 12/117,415, filed on May 8, 2008, entitled "Substrate Transport Apparatus with Multiple Movable Arms Utilizing a Mechanical Switch Mechanism" Patent No. 7,648,327, the disclosure of which is incorporated herein in its entirety by reference. The operation of the transport arms can be independent of each other (eg, the extension/retraction of each arm is independent of the other arms), can be operated by an idle switch, or can be operatively coupled in any suitable manner such that the arms share at least one common Drive shaft. Also in other aspects, the transport arm can have any other desired arrangement, such as a frog leg arm 316 (Fig. 4A) configuration, a frog arm 317 (Fig. 4E) configuration, a dual symmetrical arm 318 (Fig. 4D) configuration, and the like. Examples of suitable transport arms are U.S. Patent 6,231,297, issued May 15, 2001, 5,180,276, issued Jan. 19, 1993, 6,464,448 issued on October 15, 2002, and 6,224,319, issued on May 1, 2001. US Patent 5,447,409, issued September 5, 1995, US Patent 7, 578,649, published on August 25, 2009, 5,794,487, published on August 18, 1998, 7,946,800, published on May 24, 2011, November 26, 2002 Published on 6, 485, 250, 7,891,935, published on February 22, 2011, and US Patent Application No. 13/293, 717, filed on November 10, 2011, entitled "Dual Arm Robot", and on November 11, 2011, the name of the application is "Coaxial Drive Vacuum Robot" found in 13/270,844. The entire content of this is incorporated herein by reference.

Still referring to Figures 2 through 4E, the robotic manipulators 306, 311, 400 described herein transport the substrate S (see Figures 4A and 4B) between points in space, such as the substrate holding stations STN1 - STN6 shown in Figure 5A. To complete the transport of the substrate S, the motion control algorithm operates in any suitable controller of the automated material processing platform 300, such as a robot controller (also known as a robotic manipulator controller) 319, 323, 422, 423A-423C, 810 (see Figures 2, 3 and 8A), which is coupled to the robotic manipulators 306, 311, 400. The motion control algorithm defines a desired base path in space, and the position control loop calculates a desired control torque (or force) to apply to each of the robot actuators responsible for the individual robot degrees of freedom in the mobile space.

Robotic manipulators 306, 311, 400 (which may be referred to as automated systems) are desired to perform the repetitive tasks of continuously transporting substrate S, and the robotic manipulators are subject to environmental conditions associated with processing such substrates. Having methods and apparatus as provided by aspects of the disclosed embodiments monitors the performance of a robotic manipulator (or any other automated device of automated material processing platform 300) over time and determines (predicts diagnostics) individual robotic manipulators 306, 311, Whether 400 can operate within the desired parameters to handle its primary tasks, such as carrying and transporting substrates between substrate holding stations STN1 - STN6, is beneficial.

In accordance with aspects of the disclosed embodiments, for example, health assessments for robotic manipulators 306, 311, 400 are performed by generating basic statistical characteristics (eg, baselines of behaviors for a given variable operating in typical environmental conditions). Or statistically performed to perform a set of basic movements/motions (the terms movement and motion are used interchangeably herein) 820, 820A, 820B, 820C for the robotic manipulators 306, 311, 400 (see figure) 8A) Characterize each dynamic performance variable output by the robotic manipulators 306, 311, 400. The basic statistical characteristics are by utilizing any suitable controller of the recording system 801R that is communicatively coupled to the automated material processing platform 300, such as controllers 319, 323, 422, 423A, 423B, 423C, 810, etc. (which may be by any Suitable memory is formed or present in any suitable memory, such as reservoir 801, for example, by recording predetermined operational data that reflects at least the output of robotic manipulators 306, 311, 400. A dynamic performance variable, wherein the predetermined operational data implements a predetermined set of motion basics for a predetermined basic motion.

Each dynamic performance variable is specific to an automated system (eg, robotic manipulators 306, 311, 400) that can be obtained from a set of different automation systems, such as a group of automated systems that form an automated material handling platform 300. Dynamic performance variables. Thus, since each dynamic performance variable is specific to a respective one of the automation systems (of the group of automation systems), the basic statistical characteristics of the respective automation system are associated with the respective automation system. For example, the robotic manipulator 306 located in the atmospheric portion 301 of the automated material handling platform 300 has opposing basic statistical features, and the robotic manipulator 311 located in the vacuum portion 302 has relatively basic statistical features. If the robot manipulator 311 is placed in the atmospheric portion 301, the basic statistical characteristics of the robot manipulator 311 can still be applied to the robot manipulator 311 when placed in the atmospheric portion 301. In one aspect, the basic statistical characteristics are related to the relative automation system located in the controller of the memory and/or automation system. In addition, each robotic manipulator can have unique operational characteristics that affect the underlying statistical characteristics of the robotic manipulator. For example, the robotic manipulator 311 and another robotic manipulator can be manufactured as robotic manipulators of the same make and model. However, due to manufacturing tolerances such as those found in robotic drive systems and arm structures, the basic statistical characteristics of robotic manipulator 311 may not be applicable to other similar robotic manipulators, and vice versa. Thus, the basic statistical characteristics of each robotic manipulator are associated with the respective robotic manipulator (eg, the basic statistical feature C _pkbase of the robotic manipulator 311 moves with the robotic manipulator 311 and is unique to it, and the robotic manipulator The basic statistical feature C _{pkbase of} 306 moves with the robotic manipulator 306 and is unique to it). Thus, each device, such as robotic manipulator 311, is unique and each normalized value or basic statistical feature/value of each predetermined basic movement 501, 502, 503 of predetermined motion basic groups 820, 820A-820C _C pkbase other for each group of a predetermined motion in situ mapping process 830,830A ~ 830C mobile 501 ', 502', 503 'of each of the other values _C pkother only uniquely associated with the unique device, and The determined rate of performance degradation (eg, as indicated by the linear trend model LTM - see Figure 11) is uniquely related to unique devices.

In one aspect, a system (such as the automated material processing platform 300 shown in FIG. 3) includes or otherwise is provided with a plurality of different interconnected unique devices (eg, aligner 307, robotic manipulator 306, fan filter) Units 308 and the like are listed in Table 1 and shown in Figure 2), and for example, a transport device 311, wherein from a plurality of different unique devices App(i) (represented in Figure 2A as Each of the different unique devices of App1 to Appn has different corresponding normalized values C _pkBasei for each of the basic movements 501, 502, 503 of the predetermined motion basic groups 820, 820A to _820C (which includes C _{pkBase( 1-n)} ), and other normalized values C _pkOtheri of the in-situ process movements 501 ', 502 ', 503 ′ for _each of the other predetermined motion groups 830 , _{830A - 830C} , the other predetermined motion groups 830 830A-830C uniquely associated with the different corresponding unique devices App1 - Appn from no more than the plurality of different unique devices App(i). In one aspect, each (or at least one) different unique device App1 - Appn from a plurality of different unique devices App(i) has a configuration common to the other of the different unique devices App1 - Appn. For example, the robotic manipulator 306 can have a configuration in common with the robotic manipulator 311. In other aspects, each (or at least one) different unique device App1 - Appn from a plurality of different unique devices App(i) has a different configuration than the one from a different unique device App(i). For example, aligner 307 has a different configuration than robotic manipulator 306.

Dynamic performance variables (ie, continuous monitoring variables) for each automation device and/or system can be directly measured or derived from available measurement results (ie, derived variables). Examples of dynamic performance variables include:

Mechanical or electrical power;

Mechanical work;

Robot terminator acceleration;

Motor PWM duty cycle: The PWM duty cycle of the motor is the percentage of the input voltage supplied to each motor phase at any given time. Health monitoring and fault diagnosis systems can be used at the duty cycle of each motor phase;

Motor Current: Motor current represents the current flowing through each of the three phases of each motor. The motor current can be obtained in absolute value or as a percentage of maximum current. If obtained in absolute terms, its unit is amps. The motor current value can be reversed by using the motor torque-current relationship to calculate the motor torque;

Actual position, speed and acceleration: These are the position, velocity and acceleration of each motor shaft. For the rotary axis, the position, velocity and acceleration values are in degrees, degrees/seconds and degrees/second ^{2 respectively} . For the translation axis, the position, velocity and acceleration values are in mm, mm / s and ² mm / sec ² units;

Desired position, speed and acceleration: These are the position, velocity and acceleration values that the controller of the command motor has. These attributes have similar units to the actual position, velocity, and acceleration above;

Position and speed tracking error: These are the differences between the various expected and actual values. These attributes have similar units to the actual position, velocity, and acceleration above;

Settling time: This is the time taken by the automation device and/or system to determine the position and speed tracking error in the specified window at the end of the movement;

Encoder analogy and absolute position output: The motor position is determined by the encoder, which outputs two types of signal-analog signals and absolute position signals. The analog signal is a sine and cosine signal in mVolts. The absolute position signal is a non-volatile integer value that indicates the number of analog sinusoidal cycles or an integer multiple of the analogous sinusoidal period that has elapsed. Normally, the digital output is read when the power is turned on, after which the axis position is determined only by the analog signal;

Gripper status: This is the state of the gripper - open or closed. Where the vacuum actuated edge contacts the holder, it is a blocked/unblocked state of one or more of the sensors;

Vacuum system pressure: This is the degree of vacuum measured by the vacuum sensor. This is an analog sensor whose output is digitized by an analog digital converter. In the case of sucking the gripper, the degree of vacuum indicates whether the wafer is clamped;

The substrate presence sensor state: In a passive clamp terminator, the wafer presence sensor output is a binary output. In a vacuum actuated edge contact clamp terminator, wafer presence is determined from the output states of two or more sensors, each sensor being binary;

Mapper sensor state: This is the state of the mapper sensor - blocked or unimpeded in any given case;

Basemapper/Aligner Detector Light Intensity: This is a measure of the intensity of light detected by the photodetector of the substrate mapper or aligner. The signal is typically provided as an integer value (eg, it can range from 0 to 1024);

Basemapper sensor position capture data: This is an array of robot axis position values for which the mapper sensor changes state;

Vacuum valve status: This is the command status of the vacuum valve. It specifically points out whether the electromagnetic coil that operates the vacuum valve should be energized;

Voltage at the fuse output terminal: Monitors the voltage at each fuse output terminal in the motor control circuit. Fusing the fuse results in a low output terminal voltage;

Substrate alignment data: this is the substrate eccentricity vector and angular positioning of the alignment reference of the substrate reported by the aligner;

Location data when the external substrate sensor is switched: In some cases, the atmospheric and vacuum portions of the tool may be equipped with optical sensors for detecting the leading and trailing edges of the substrate carried by the robot. The robot position data corresponding to these events is used for the instant identification of the eccentricity of the base on the robot terminator;

Substrate cycle time: This is the time it takes for an automated device and/or system to be processed by a tool for a single substrate, typically measured under steady flow conditions;

Small ambient pressure: This is the pressure measured by the pressure sensor in the atmospheric part of the tool.

Specific examples of continuous monitoring variables include:

Table 3: Continuous monitoring variables

Where T1 and T2 are robot manipulators that drive the rotary axis (possibly with more or less than two rotary drive axes); Z is the robot drive Z axis; CPU is the robot controller (eg controllers 319, 323, 422, 423A ~ 423C, 800). Fan 0, fan 1 are various fans of the robot manipulator; theta is the rotation of the robot manipulator arm; and R is the extension of the robot manipulator arm.

Specific examples of derived variables include:

Table 4: Derived Variables

These dynamic performance variables are calculated from raw or direct measurements such as motor position, speed, acceleration, and control torque.

The predetermined basic movements 501, 502, 503 of the predetermined motion basic groups 820, 820A-820C include at least one common basic movement defining a basic motion type (eg, a motion forming a baseline, and which is created from sufficient sample movement, the A sufficient number of sample movements are collected to define a statistically significant number of statistically significant batches. For example, for each of the basic movements 501, 502, 503 (eg, the basic movement 501 has a motion basic group 820A, the basic movement 502 has a motion basic group 820B, and the basic movement 503 has a motion basic group 820C) (each) motion the basic group 820,820A ~ 820C (see FIG. 8A) based substantially for a given convergence criterion based on the statistical amount sufficient to provide a meaningful standard deviation mobile N _min (see FIG. 6) (e.g. sample size) the minimum number, The motion basic groups (or mobile groups) 820, 820A-820C are then characterized for specific robotic manipulators 306, 311, 400. Thus, each dynamic performance variable is specific to and output by the respective robotic manipulators 306, 311, 400.

The predetermined basic movements 501, 502, 503 of the respective predetermined motion basic sets 820, 820A-820C comprise a plurality of different basic motion types, each of which is carried by the transport means 306, 311, 400 for each basic motion The type produces the effect of the number of statistical features of the common motion. Each of the different basic motion types has a different corresponding at least one torque command feature and a position command feature that define different common motions associated with each of the basic motion types. In one aspect, the predetermined basic motion groups 820, 820A-820C may be of one or more motion/motion types. For example, each of the basic motion groups 820, 820A-820C may be simple moving or complex (eg, mixed) movements that are characterized by torque and position commands that define respective movements. Chemical.

A simple movement is a linear movement between two points (from point 0 to point 1 as shown in Figure 5C) or along a circular arc between two points (from point 1 to point 2 as shown in Figure 5C). One of the theta axis, the extension axis, or the Z axis of the robotic manipulators 306, 311, 400 (eg, a single degree of freedom of movement).

As shown in FIG. 5B, the composite or hybrid movement is a movement in which more than two simple movements are mixed together, and FIG. 5B illustrates that the movement extends from point 0 to point 2 and has a mixing path adjacent to point 1, which The path mixes two linear movements from point 0 to point 1 and point 1 to point 2, along at least two of the theta, extension or Z axis of the robotic manipulators 306, 311, 400 (eg, two or More than one degree of freedom of movement).

Each of the motion basic groups 820, 820A-820C can also be moved by the location within the group (eg, the starting and ending points of the movement), the load parameters of the movement within the group (eg, the robotic manipulators 306, 311, 400) Dynamic conditions (eg motion/stop, stop/stop, stop/motion, motion/motion, etc.) at the initial position and/or final position for loading (carrying the substrate) or not loading (not carrying the substrate) and/or moving . For example, referring to the complex movement in Figure 5B, the dynamic condition point 0 is stopped and the dynamic condition at point 2 is stopped. Referring to the two simple movements in Figure 5C, the dynamic condition at point 0 is stopped and the dynamic condition at point 1 is moving; the dynamic condition of point 2 is stopped. As described above, although the type of movement is described in terms of one, two or three degrees of freedom with respect to robotic manipulator arm movement, it should be understood that the type of movement may include any suitable number of degrees of freedom or a single degree of freedom. Move (for example, using a vacuum pump, a substrate aligner, etc.).

Minimum number of moving each transfer type will affect statistically characterizing each mobile type N _min. For example, each dynamic performance variable or type of motion can be represented historically as:

Where s is the basic motion/motion signal provided in Tables 5 through 7. The signals s ₀ to s _n are signals with a scalar output and should be able to be compared across different stencil movements (which may also be referred to as basic movements), ie comparing the motor energy associated with the baseline across different movement types. The signals s _n+1 to s _n+1+mi are vector output signals from Table 8 and cannot be compared between different template motion types, which are denoted by i.

Table 5: Derived signals for basic movement with scalar output (per motor)

Table 6: Derived signals for basic movement with scalar output (per arm or terminator)

Table 7: Derivative system signals for basic movement with scalar output

Table 8: Derived signals with basic output for basic movement

These vector output signals have signals at each time sample along the trajectory, so the number of these signals differs between different movements, and one movement in one time sample and another movement are not evaluated. Any physical meaning. The basic mobile (type) index is denoted by i, and the history of the given index is denoted by j.

The last basic movement evaluated in this example is

And the third last evaluated basic move in this example is

Referring to Figures 5A-5C, the basic movements, e.g., the basic movements 501, 502, 503 of the respective set of basic movements 820, 820A-820C, may also be referred to as template movements. The basic movements 501, 502, 503 are repeated movements along a unique path. The basic movements 501, 502, 503 can consist of simple movements or complex movements as described above.

To assess system performance degradation and performance trends, the profile is analyzed along a unique path to the basic movement of the baseline. The basic movements 501, 502, 503 can be defined theoretically and/or empirically. For example, the theoretical basic movement is based on the desired design configuration and processing of the processing tool to resolve the expected movement in operation, and then executed at any time before or after the in-situ process tool installation.

An empirical basic movement can be generated from the in-situ process movement command as the movement of the desired common occurrence to produce sufficient statistical characteristics with meaningful statistical values that are stable between predetermined convergence limit rate of change as shown in FIG. (where N _min in Figure 6 is the minimum amount of movement (eg, sample size) sufficient to provide a statistically significant standard deviation based on a given convergence criterion). The generation of empirical basic movement can be a two-part process (similar to the empirical generation of basic statistical features). For example, generating an empirical basic movement may include accessing an in-situ movement command histogram 700 (see FIG. 7) and utilizing commands (eg, torque, position, boundary parameters, command trajectory paths (including speed and movement duration), load The condition, etc.) identifies the in-situ movement, the command mapping to the base movement 501, 502, 503 (eg, the home position movement is within the configurable tolerance of the base movement match); and from any suitable recording system 801R for the mapped action The record table 840 accesses each dynamic performance variable output by the respective robotic manipulators 306, 311, 400, which records predetermined operational data that reflects at least one dynamic output by the robotic manipulator The performance variable is used to determine the determination of another predetermined motion group 830 (as detailed below).

The generation of empirical basic movements can be performed in near-immediate execution, running in the background, and accessing the record table 840 without accessing the controllers 319, 323, 422, 423A, 423B, 423C, 800 of the automated material processing platform 300 and associated Two-way communication / data channel. The in-situ movement command histogram 700 includes motion commanded by a robotic manipulator controller (eg, controllers 319, 323, 422, 423A, 423B, 423C, 800) that is included by a respective robotic manipulator 306 In-situ process motion achieved by 311, 400. The home position movement command histogram 700 can be any suitable one of, for example, a robotic manipulator controller (eg, controller 319, 323, 422, 423A, 423B, 423C, 810) or any other suitable controller of the automated material processing platform 300. Recorded in the record table 700R (see Fig. 8A). As described herein, the robotic manipulator controller decomposes the mapping motion from the periodic access of the motion histogram 700 in the record table 700R.

For example, still referring to FIG. 8A, the motion resolver 800 decomposes the in-situ process motion commands of the robot controllers 319, 323, 422, 423A-423C, 810 from the robotic manipulators 306, 311, 400 (see FIGS. 2 and 3) ( The in-situ process motions 501', 502', 503' (see FIG. 5A) implemented by the transport device are mapped to predetermined basic motions 501, 502, 503 of a predetermined set of motion basics (described in detail below) (each of which defines a corresponding The template motion causes the in-situ process motion to map onto the respective template motion) and defines the robot controllers 319, 323, 422, 423A-423C, 810 with the mapped in-situ process motions 501 ', 502', 503' Another scheduled exercise group (details as described below). For example, the in-situ process motion 501' maps to the base motion 501, the home position process motion 502' maps to the base motion 502, and the home position process motion 503' maps to the base motion 503. Note that in a manner similar to that described above, each of the in-situ process motions 501'-503' is characterized by at least one of a torque command and a position command from the device controller, wherein at least one of the torque command and the position command An in-situ process motion in at least one degree of freedom of motion of the characterization robotic manipulators 306, 311, 400.

The motion resolver 800 can be included in the robot controllers 319, 323, 422, 423A-423C, 800 as a module, and the motion resolver 800 can be communicably coupled to the robot controllers 319, 323, 422, 423A~ The remote processor or motion resolver 800 of 423C, 810 may be a different processor communicatively linked to the robot controllers 319, 323, 422, 423A-423C, 810.

The motion resolver 800 iterates through all of the in-situ process movements 501 ', 502 ', 503 ' to identify those in-situ processes having the minimum number of movements N _min required by, for example, the standard deviation convergence shown in FIG. Move 501', 502', 503'. For example, as described above, in order to create a baseline (eg, establish basic movements 501, 502, 503), sufficient samples must be collected to define a statistically significant batch. The number of samples required to create a baseline depends on the physical properties of the variable being analyzed. For example, a typical (average and standard deviation) statistic that defines the mechanical work of a given motion axis of the robotic manipulators 306, 311, 400 takes longer than the peak control torque of the same axis that performs the same motion. To correct this situation, the size of the baseline is defined based on a statistical analysis of the collected data. For example, the standard deviation can be calculated during the collection of baseline data to a point whose value is stabilized at a certain limit (as shown in Figure 6). In Figure 6, the standard deviation of a given variable is plotted against the sample size. As the sample size increases, the standard deviation tends to converge within certain limits. Depending on the actual data set, these limits can be defined a priori or calculated, for example, when the rate of change of the graph is less than about +/- 10%; however, any suitable method of convergence and/or percentage change can be used. .

Still referring to FIGS. 8A, 5, and 8B, the movement constituting at least the required minimum number of movements _Nmin (eg, for defining the movement of the baseline) may be referred to as a predetermined motion basic set 820. Each of the basic movements 501, 502, 503 has a respective predetermined set of motion basic groups 820A, 820B, 820C that are unique to the basic movements 501, 502, 503. An exemplary process flow for determining and updating the respective predetermined motion basic groups 820A, 820B, 820C is illustrated in Figures 8A and 8B.

Still referring to Figures 5, 8A and 8B, in one aspect, once motion resolver 800 identifies and decomposes predetermined motion base sets 820A, 820B, 820C for respective base movements 501, 502, 503, mapping (as described above) In-situ process movements 501 ', 502', 503' to respective ones of the basic movements 501, 502, 503 are included among the opposing predetermined motion basic groups 820A, 820B, 820C to update the relative predetermined motion basic set 820A , 820B, 820C. In other aspects, the in-situ process motions 501', 502', 503' of the predetermined motion base sets 820A, 820B, 820C mapped to the opposite of the base movements 501, 502, 503 may be formed with the predetermined motion base set 820A. 820B, 820C A different group of different sports type groups. The updated predetermined motion basic group and/or the different group motion type group may be referred to as another predetermined motion group 830. As will be described herein, other predetermined motion basic sets 830A, 830B, 830C for respective in-situ process movements 501 ', 502', 503' are basic to motion for respective basic movements 501, 502, 503. Groups 820A, 820B, 820C make a comparison of health assessments and predictive diagnoses of the automated system being monitored, such as robotic manipulator 300 (as described herein).

As noted above, health assessments of, for example, robotic manipulators 306, 311, 400 (or other suitable automation devices of automated material processing platform 300) generate basic statistical characteristics (eg, given variables of a given environmental condition under typical environmental conditions) Performed by a baseline or statistical representation of the behavior, the basic statistical features are characterized by a set of basic movements 820, 820A, 820B, 820C (see FIG. 8A) of the robotic manipulators 306, 311, 400 by the robotic manipulators 306, 311 , each dynamic performance variable of the 400 output.

In one aspect, any suitable processor 810P (which is substantially similar to processor 105 in one aspect) is used to retrieve/determine baseline metrics using automated material processing platform 300. The processor 810P may be included in the robot controllers 319, 323, 422, 423A-423C, 810 as a module, and the processor 810P may be communicably coupled to the robot controllers 319, 323, 422, 423A-423C The remote processor of 810 (and motion decomposer 800), or processor 810P may be a different process communicatively linked with robot controllers 319, 323, 422, 423A-423C, 810 (and motion decomposer 800) Device. Processor 810P is coupled to recording system 801R in any suitable manner, while in other aspects, processor 810P includes recording system 801R.

The baseline metric is retrieved/determined by, for example, calculating a probability density function (PDF) of the underlying statistical characteristics, wherein the probability function can be expressed as:

Where μ is the data set mean, x is the dynamic performance variable, and σ is the standard deviation. Figure 9 shows a typical Gaussian distribution with mean and standard deviation. The upper and lower specification limits (USL and LSL, respectively) are also defined in Figure 9.

The basic statistical characteristics of each dynamic performance variable of the respective robotic manipulators 306, 311, 400 (see Figures 2 and 3) are normalized for each different basic movement type (moving type group to base value), which The basic movement type characterizes the nominal/baseline of each dynamic performance variable specific to the respective robotic manipulators 306, 311, 400 for each different basic movement type/moving type group. For example, a base value (eg, a processing capability index C _pkBase ) characterized by a respective probability density function PDF of each dynamic performance variable for each motion of the predetermined motion basic set is determined, wherein the dynamic performance variable is determined by The robot manipulators 306, 311, 400 output.

In general, the processing power index C _pk can be defined as:

Where σ is the standard deviation and μ is the average of the samples collected for the corresponding variables. The processing power index C _pk can be used as a measure to represent the baseline of the corresponding dynamic performance variable because the processing power index C _pk draws the mean and standard deviation of the overall sample that is large enough to provide meaningful statistics. The upper and lower specification limits USL, LSL may be determined in any suitable manner, for example by defining the upper and lower specification limits USL, LSL as the measured standard deviation of the respective measured robotic manipulators 306, 311, 400. The function. E.g:

Where N may be an integer greater than 3 such that C _pk may be a number greater than one. As an example, if N=6, then the baseline processing power index C _pkBase can be defined as:

In one aspect, C _pkBase can be set to 2.0 and theoretically or empirically based on a +/- 6 sigma baseline data set mean μ to identify upper and lower limit specification limits USL, LSL, such that 99.9% of the moving samples It is captured (as shown in Figures 9 and 10). In other aspects, when the limits are well established, such as peak torque limits, maximum settling times, etc., the upper and lower specification limits USL, LSL can be configured on a per signal basis.

In one aspect, please also refer to FIG. 2A for the corresponding normalized value C _pkBase(1-n) and other values C _pkOther(1-n) for each of the relatively different unique devices App1 to Appn. It is recorded in any suitable controller, such as a controller of a corresponding one of the devices App1 to Appn. A normalized value C _pkBase(1-n) uniquely associated with a corresponding one of the different unique devices App1 - Appn is _compared with other values C _pkOther(1-n) for each different unique device App1~Appn determine the corresponding performance degradation rate on a device based on the device (as indicated by, for example, the corresponding linear trend model LTM, see Figure 11), the corresponding performance degradation rate will be more in this paper. describe in detail. For example, each of the respective devices App1 to Appn has a corresponding linear trend model LTM1 to LTMn as shown in FIG.

Once a baseline metric is established for each measurement variable (original and derived), a batch of in-situ process movements 501'-503' are sampled during operation of the respective robotic manipulators 306, 311, 400. For example, in-situ process movements 501 ', 502', 503' are generated by controllers, such as controllers 319, 323, 422, 423A, 423B, 423C, 810, to identify pairs of monitored robotic manipulators 306, 311, 400 are specific statistical features. As described above, each dynamic performance variable of the group for in-situ process movement is mapped to a corresponding basic movement (eg, basic movement type/type group - see equations 1, 2, and 3). As described above, the mapped in-situ process motions 501', 502', 503' are used to define other predetermined motion groups 830, 830A-830C of the respective robotic manipulators 306, 311, 400.

As with the baseline movements 501-503, for each different in-situ (other) movement type/type group (eg, other predetermined motion groups 830, 830A-830C), each of the respective robotic manipulators 306, 311, 400 The in-situ process movements 501'-503' of the dynamic performance variables (other) statistical features are mapped to the respective predetermined motion basic groups 820, 830A-830C and normalized to the home position (other) value C _pkOther The in-situ (other) value C _pkOther characterizes each dynamic performance variable of the respective robotic manipulator 306, 311, 400 for each of a different in-situ movement type (which may be a simple movement or a complex movement) In-situ performance. The in-situ (other) value C _pkOther is a processing capability index that is characterized by a probability density function PDF of each dynamic performance variable output by the robotic manipulators 306, 311, 400, which is in situ (another) The value C _pkOther implements the mapped in-situ process motions 501'-503' of the other predetermined motion groups 830, 830A-830C. The in-situ (other) values C _pkOther reference the upper and lower limits of the baseline USL, LSL to locate other predetermined motion groups relative to the predetermined motion base group, as shown in Figure 10 (where the other predetermined motion groups are identified as "new" The batch "and the predetermined motion basic group is identified as "baseline"). C _pkOther is a processing power index that can be defined as:

Where i is an iteration of the evaluated C _pkOther . The normalized in-situ (other) value C _pkOther is compared to the normalized base value C _pkBase for each respective dynamic performance variable being monitored (eg, for each movement type and traverse type).

The comparison between the in-situ (other) value C _pkOther and the base value C _pkBase may be performed by the processor 810P or any other suitable controller of the automated material processing platform 300, wherein the respective robotic manipulators 306, 311, 400 It is a common transport device that pre-determines the motion basic groups 820, 820A-820C and other predetermined motion groups 830, 830A-830C (and corresponding in-situ (other) values C _pkOther and base values C _pkBase ). The comparison between the in-situ (other) value C _pkOther and the base value C _pkBase is directed to a particular device (eg, corresponding robot manipulation by providing a measure of how much each dynamic performance variable deviates or drifts its baseline (see Figure 10). The 306, 311, 400) implements a health assessment of each dynamic performance variable being monitored. The health assessment for each performance variable can be defined as the relative deviation from its baseline, defined as follows:

This means that a 100% evaluation represents a perfect statistical match between the in-situ (other) value C _pkOther and the base value C _pkBase . Equation (10) above represents an example of an evaluation of a given dynamic performance variable. In other aspects, other measurement evaluation methods can be used, such as measuring the number of occurrences of the upper and lower limits of the USL and LSL beyond the baseline. Figure 10 illustrates an example of a health assessment calculation based on a given dynamic performance variable. In the example shown in Figure 10, 20% of the bulk data samples are outside the baseline range, and the in-situ (other) value C _pkOther is placed in an unfavorable position.

Still referring to FIG. 10, also referring to FIG. 11 and FIG. 12, each dynamic performance variable of the respective robotic manipulators 306, 311, 400 can be defined according to the extent to which the in-situ (other) value C _pkOther deviates from the base value C _pkBase . Health assessment. The degree of change can be defined according to a prescribed threshold, such as, for example, "warning" and "error", where "warning" can mean "need to be noted" and "error" can mean "requiring immediate action", which will be described below. Another way to track the in-situ (other) value C _pkOther (and the base value C _pkBase ) is that such a trace provides a trend analysis that can be evaluated when the corresponding dynamic performance variable is expected to reach a different level of change. Or infer.

Determining the amount by which each dynamic performance variable deviates or drifts from its baseline provides a trend data TD for each dynamic performance variable, wherein the trend data TD characterizes the deteriorating trend of the corresponding dynamic performance variable. The trend data TD can be recorded in any suitable register TDR of the automated material processing platform 300. 11 depicts an exemplary trend profile for an exemplary dynamic performance variable; where the evaluation of the in-situ (other) value C _pkOther and the base value C _pkBase from a predetermined time point of a different batch of samples is evaluated by A1 - An Draw on the chart.

The diagonal lines in Figure 11 represent linear trend models LTM, LTM1 - LTMn, which may be obtained in any suitable manner, such as by using a least squares method; in other aspects, any suitable trend model may be used. Characterization, for example, trending data for performance degradation trends of robotic manipulator 306 (or any other suitable device of automated material handling platform 300 (see FIG. 2)) and several different unique devices App1 of automated material handling platform 300 Each of ~Appn (see FIG. 2A) is recorded on the automated material processing platform 300, for example, any suitable controller/processor (such as, for example, a controller or tool controller 314 of the corresponding device or processing) 810P) in the record table. In one aspect, processor 810P combines performance degradation trends corresponding to shipping devices (eg, shipping device 306) and each of a plurality of different unique devices App1 - Appn of automated material processing platform 300 to determine characterization automation The performance of the material processing platform 300 deteriorates as the system performance deteriorates.

Referring to the linear trend model LTM, this linear trend model LTM (which can represent unique devices such as robotic manipulator 306, robotic manipulator 311, aligner 304, power supply PS of automated material handling platform 300, etc.) One) can be used to predict the time t _warn as the estimated time (or period) for evaluating the measurements to reach the specified warning threshold. Likewise, the time t _error can be estimated as the time (or period) at which the point at which the operation of the robotic manipulators 306, 311, 400 is not recommended to continue. As shown in FIG. 11, the linear trend models LTM1 to LMn determined for each of the different unique devices App1 to Appn. The linear trend models LTM1 - LTMn may indicate the overall health of the system (eg, automated material handling platform 300) and the health of each of the different unique devices App1 - Appn. Referring also to FIG. 2, for example, the linear trend model LTM1 may correspond to the power supply PS, the linear trend model LTM2 may correspond to the robot manipulator 306, the linear trend model LTM3 may correspond to the robot manipulator 311 and the linear trend model LTMn may correspond to Aligner 307.

As seen in Figures 11 and 12, the trend data TD can also be provided to the health of the operator to be supplied to, for example, the robotic manipulators 306, 311, 400, for example, by any suitable display 140. Evaluation warning. For example, any suitable controller that may be separate from controller 319, 323, 422, 423A, 423B, 423C, 810 or included in automated material processing platform 300 (eg, processor 810P) may include trend/evaluation unit 870 (FIG. 8A), the trend/evaluation unit 870 is configured to transmit a predetermined signal to indicate to the operator the health assessment of the robotic manipulators 306, 311, 400. In other aspects, trend/evaluation unit 870 can be part of controllers 319, 323, 422, 423A, 423B, 423C, 810. For example, when the trend profile TD reaches the first predetermined assessment value WS, the processor 810P may send or cause a "warning" indication to be visually displayed, for example, in yellow, when the trend profile TD reaches a second predetermined assessment value ES (eg, below The "error" indication may be presented in red when the first predetermined evaluation value WS), and may be presented in green when the trend data is higher than the first predetermined evaluation value WS (eg, all dynamic performance variables are in a predetermined operation) Within the limits). In other aspects, the operational state of the automated system (eg, normal, warning, and error) can be presented audibly, visually, or in any other suitable manner.

In one aspect, processor 810P aggregates the dynamic performance variables of the at least one dynamic performance variable output by the transport device with the highest degradation trend (eg, the lowest percentage estimate) and predicts the occurrence of the transport device having a performance below a predetermined performance state. . For example, the overall health of the robotic manipulators 306, 311, 400 can be measured as the worst case assessment of all dynamic performance variables monitored in a given batch of data samples. For example, it is envisaged to measure five dynamic performance variables Var1 to Var5 (such as, for example, T1 position_actual, Z acceleration_actual, bus motor voltage, T2 temperature, and theta command position for explaining the different variables being compared) and Compare them to their respective baselines, where the results are evaluated as:

Table 9: Evaluation values

In the above example, the evaluation of the dynamic performance variable Var5 is the lowest of the five dynamic performance variables Var1 to Var5 and can be used to represent the overall current health assessment of the robotic manipulators 306, 311, 400, the manipulator 306, The health of 311, 400 was monitored by all five dynamic performance variables Var1 - Var5. This can be done independently from the physical properties and meaning of each of these dynamic performance variables Var1 to Var5, since based on the fact that these evaluations are relative measurements of their respective baselines, the assessment can be performed directly on all of these entities. Comparison.

As an example of the above-described performance variable comparison, the processor 810P compares the performance deterioration tendency of the transport device 306 with the performance deterioration tendency of each of the plurality of different unique devices App1 to Appn, and determines the performance deterioration tendency of the transport device 306. Or whether the performance deterioration tendency of the other of the plurality of different unique devices App1 to Appn is a determining factor of the control performance deterioration tendency and the control performance deterioration tendency as a performance deterioration tendency of the system. For example, at time t _s , the linear trend model LTM2 for the robotic manipulator 306 has a minimum estimate, which is considered to be the overall health of the automated material handling platform 300 described with respect to Table 9. Over time, other linear trend models (like the linear trend model LTM1) may show a faster rate of performance degradation. In this case, for example, the overall health of the automated material handling platform can be determined based on, for example, the linear trend model LTM1 at time t ₀ , where the warning is generated based on the linear trend model LTM1 at time t _warnLTM1 And the error is generated based on the linear trend model LTM1 at time t _errorLTM1 .

While the overall health of an automated material handling system can be determined by a linear trend model with a minimum estimate at any given time, the linear trend model provides information about which device App1 - Appn is causing a system error or warning or The primary source of fingerprints or instructions. For example, the power supply PS may affect other devices App1 - Appn, for example, by not providing sufficient voltage to, for example, the robotic manipulator 306 (corresponding to the linear trend model LTM2). As shown in FIG. 11, a warning may be generated at time t _warnLTM1 as a result of deterioration of performance of the power supply PS. For performance degradation of the robotic manipulator 306, a warning may be generated at time _twarnLTM2 ; however, if there is no insufficient voltage provided by the power supply PS to the robotic manipulator 306, the robotic manipulator 306 may operate normally. . These two warnings indicate that the power supply PS and the robot manipulator 306 should be checked for repair, and suggest that there may be some correlation between the performance degradation of the power supply PS and the performance degradation of the robotic manipulator 306.

In another aspect, as shown in Figures 5A and 8A, aspects of the disclosed embodiments can provide the health of the system as a combination of aggregated characterization and health prediction. It should be noted that the combination of characterization and health prediction of the aggregation of the system is different from the different deterioration trends of the combination/aggregation system components to determine the deterioration trend of the entire system. For example, a combination of aggregated characterization and health prediction can be considered similar to determining a deterioration trend for a system with μ devices, where the system and its multiple devices are treated as a single unique device, while also separately determining as described above The deterioration trend of each unique device of the system. In this aspect, as described above, the basic movements 501-503 and the in-situ process motions 501'-503' are uniquely related to the respective unique devices. The basic movements 501 - 503 and the in-situ process motions 501 ' - 503 ' may be different for each different device of the general type (eg, basic movements 501 - 503 of the robotic manipulator 306 and in situ process motions 501 ' - 503 ' It may be different from the basic movements 501 to 503 of the robot manipulator 311 and the in-situ process motions 501' to 503'). The basic motion groups 820, 820A-820C and other predetermined motion groups 830, 830A-830C for a unique system (eg, automated material processing platform 300) may be determined by the basic motion group 890 (see FIG. 8A), where the basic motion The basic motion of group 890 is determined by combining a plurality of one or more basic motions 501-503, wherein each of the plurality of one or more base motions is associated with a unique device (such as Table 1 and Table 1 above). The devices described in 2 are uniquely related, the unique devices being communicatively coupled (eg, a power supply, robotic manipulator, wafer sensor, etc.) to form a single aggregated motion 890AG. A single aggregate motion is uniquely associated with the associated dynamic performance variables of a unique combination of unique systems (eg, automated material processing platform 300) and (for each device operating in a single aggregate motion) (eg, ), where S _{0, μ} is a scalar value, and S _μ+1 is a vector value to produce a system performance normalized value And for the mapped motion to produce another value that is uniquely related to the system of the μ devices .

In one aspect, referring to Figure 14, where one of the components (e.g., the components listed in Tables 1 and 2) is replaced in a system (e.g., automated material handling platform 300), it may be determined by repeating the system health To generate a system of health measurements (block 14 1400 of Figure 14), wherein the repeating system health assay includes (1) a deterioration trend of each component of the repeating system (or at least the device being replaced) (eg, by the linear trend model LTM, LTM1~) The determination of the LTMn) and the deterioration trend of the combined elements determine the overall system health from one of the controlled trends associated with, for example, Table 9 (Fig. 14, block 1401); (2) Determining the deterioration trend of the aggregated characterization of new system aggregates as described above (Fig. 14, block 1402); (3) identifying whether the replaced component improves or reduces the overall deterioration trend of the system, and if new components Reducing the tendency to deteriorate, the components are replaced again, and/or the mixing and matching components are used to improve the overall system degradation trend (Fig. 14, block 1403).

In one aspect, the importance of weighing the deterioration trend (Fig. 15, block 1500) can be applied to the linear trend model LTM, LTM1~ of each component of the system by any suitable processor of the system (e.g., tool controller 314). LTMn. For example, when the application balances the importance, the tool controller 314 can determine whether the deterioration trend of any one or more of the components is being controlled (eg, to the greatest extent worse) or otherwise displayed at the desired failure time (FIG. 15, block 1501). The predicted failure time outside the predetermined time range; or any of the more components may be otherwise identified as the first component predicted to be faulty, and may be the component predicted to be the first failure A range (e.g., time range) is determined between the component predicted to be the last fault (Fig. 15, block 1502). The history of past faults (if any) can also be determined and stored in the memory of the system and viewed by tool controller 314 to determine which components, if any, have a tendency to become the first fault ( Figure 15, block 1503). Based on the determination made above, it may be determined via tool controller 314 whether the frequency of failure of the component is inconsistent with the system (e.g., the frequency of failure of other components) (Fig. 15, block 1504). The tool controller 314 can also identify component characteristics related to system performance (eg, whether the system is available for operation with or without the failed component) (FIG. 15, block 1505). In one aspect, component characteristics related to system performance can be classified as critical (eg, when the system cannot operate without components) or routinely (the system can operate without components). Component characteristics may include, but are not limited to, the primacy of the component, the difficulty of finding replacement of the component, the accessibility of components within the system (whether the component is easily accessible for replacement/hard to access, and difficult to replace), the packaging of the component (eg, If the motor in the robotic manipulator fails, the robotic manipulator needs to be replaced. However, if the faulty person is a power supply, only the power supply needs to be replaced) or other factors that may affect the system downtime and/or the availability of component replacement.

The trade-off importance given to the deterioration trend of each component can be determined by, for example, the tool controller 314 based on the component's failure frequency and component characteristics associated with system performance. The trade-off of the deteriorating trend of components increases or decreases the impact of component degradation on the overall deterioration of the system, where the assessment of overall system health is based on the deteriorating trend of the trade-offs of each component in the system. .

As a non-limiting example, a linear trend model corresponding to an element that has just been replaced/repaired may have less weight than a component that has been serving for a period of time, such that the component that has just been replaced/repaired has a judgment of the health of the entire system. It has less impact than components that have been serving for a long time. In another aspect, the linear trend models LTM, LTM1~LTMn can be weighed so that the linear trend model of components that are known to be frequently ineffective does not affect the overall system's health judgment, or only the impact of the limit. . In other aspects, the health assessment of the system may not include any weighting factors applied to the linear trend models LTM, LTM1 - LTMn.

Referring now to Figures 2, 3, 5A, 8A, 8B and 13, the operation of an exemplary health assessment will be described in accordance with aspects of the disclosed embodiments. The predetermined operational data is recorded using a recording system 801R communicatively coupled to the device controllers 319, 323, 422, 423A-423C, 810 (Fig. 13, block 1300). The predetermined operational data embodies at least one dynamic performance variable output by the transport device that implements a predetermined set of basic motions 820, 820A, 820B, 820C of the predetermined basic motion. The base value C _pkBase is determined using, for example, a processor 810P communicatively coupled to the recording system _801R (FIG. 13, block 1310). The base value C _pkBase is characterized by a probability density function PDF of each dynamic performance variable output by the transport device 306, 311, 400 for each motion of the predetermined motion base set 820, 820A, 820B, 820C.

The commands for the in-situ process motions 501'-503' are decomposed by, for example, the motion resolver 800 communicatively coupled to the device controllers 319, 323, 422, 423A-423C, 810 (FIG. 13, block 1320). ). The in-situ process motions 501'-503' corresponding to the resolved in-situ process motion commands and implemented by the transport devices 306, 311, 400 map to predetermined predetermined motions 501 of the predetermined motion base groups 820, 820A, 820B, 820C. 503. Another predetermined motion group 830, 830A, 830B, 830C of the transport device is defined along with the mapped in-situ process motions 501'-503' (Fig. 13, block 1330).

The predetermined operational data of at least one dynamic performance variable output by the transport device is embodied by, for example, recording system 801R recording (Fig. 13, block 1340), which implements the other predetermined motion groups. Processor 810P determines another value C _pkOther (Fig. 13, block 1350) that is characterized by a probability density function PDF of each dynamic performance variable output by the transport device, the other value C _pkOther implementing the other The mapped in-situ process motions 501'-503' of the motion groups 830, 830A-830C are scheduled.

The other value C _pkOther and the base value C _{pkBase are} transmitted, for example, the processor 810P compares each dynamic performance variable output by the transport device respectively corresponding to the predetermined motion basic group and the other predetermined motion groups. 13. Block 1360) wherein the transport device is a common transport device for both the predetermined motion base group and the other predetermined motion group. The health of the transport device is evaluated based on the comparison as described above, and any suitable health assessment notifications can be sent to the operator of the automated material handling platform 300 as described above.

In accordance with one or more aspects of the disclosed embodiments, a method for health assessment of a system includes a transport device:

Predetermining operational data embodying at least one dynamic performance variable output by the transport device using a recording system communicatively coupled to the device controller, the predetermined operational data achieving a predetermined set of motion basic groups of predetermined basic motions;

A base value (C _pkBase ) is determined by a processor communicatively coupled to the recording system, the base value being a probability density function for each dynamic performance variable output by the transport device for each motion of the predetermined motion base set Characterize

An in-situ process motion command of the device controller is decomposed from the transport device using a motion resolver communicatively coupled to the device controller, wherein the in-situ process motion implemented by the transport device maps to a predetermined basic motion of the predetermined set of motion basics, and Defining another predetermined motion group of the transport device with the mapped in-situ process motion;

Recording system records a predetermined operational profile embodying at least one dynamic performance variable output by the transport device, the predetermined operational data implementing another predetermined set of motions, and utilizing a processor to determine another value (C _pkOther ), the other The value is characterized by a probability density function for each dynamic performance variable output by the transport device, the other value (C _pkOther ) implementing the mapped in-situ process motion of another predetermined set of motions;

Using the processor to compare another value and a base value (C _pkBase ) for each of the dynamic performance variables output by the transport devices respectively corresponding to the predetermined motion base group and the other predetermined motion group, wherein the transport device Both the predetermined motion basic group and the other predetermined motion group are a unique transport device and are common, and the health of the transport device is evaluated based on the comparison.

In accordance with one or more aspects of the disclosed embodiments, each predetermined base motion defines a template motion, and each in situ process motion is substantially mapped onto a corresponding one of the template motions.

In accordance with one or more aspects of the disclosed embodiments, each template motion is characterized by at least one of a torque command and a position command from a device controller.

In accordance with one or more aspects of the disclosed embodiments, at least one of the torque command and the position command characterizes the template motion among at least one degree of freedom of movement of the transport device.

In accordance with one or more aspects of the disclosed embodiments, the method further includes recording, in a record table of the device controller, a motion histogram commanded by the device controller, the motion histogram including an in-situ process implemented by the transport device Motion, and wherein the processor decomposes the motion of the mapping from the periodic access motion histograms located in the record table.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a statistical feature number defining at least one common base motion of the base motion type.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a plurality of different basic motion types, wherein each of the basic motion types is co-moving by the transport device for each of the basic motion types Implemented in the number of statistical features.

In accordance with one or more aspects of the disclosed embodiments, each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining and each The basic movement types correspond to different common movements.

In accordance with one or more aspects of the disclosed embodiments, the method further includes recording, by the recording system, trend data for each of the dynamic performance variables, wherein the trend data characterizes a trend of deterioration of the corresponding dynamic performance variable.

In accordance with one or more aspects of the disclosed embodiments, the method further includes utilizing a processor to aggregate a dynamic performance variable having a highest degradation trend of at least one dynamic performance variable output by the transport device, and predicting having a lower than predetermined performance state The performance of the transport device occurs in the event.

In accordance with one or more aspects of the disclosed embodiments, the method further includes utilizing a processor to provide an operator of the transport device with an occurrence of an event regarding the transport device having a performance below a predetermined performance state based on the aggregation of the dynamic performance variables. The indication of the forecast.

In accordance with one or more aspects of the disclosed embodiments, a method for health assessment of a system including a delivery device is provided. The method includes:

Predetermined operational data embodying at least one dynamic performance variable output by the transport device is recorded by a recording system communicatively coupled to the device controller, the predetermined operational data implementing a predetermined motion set to define a statistical characteristic of the predetermined basic motion Basic group

A normalized value is determined using a processor communicatively coupled to the recording system, the normalized value statistically characterizing each of the dynamic performance variables output by the transport device for each motion of the predetermined set of motion basics Nominal performance

Determining an in-situ process motion command of the device controller from the transport device using a motion resolver communicatively coupled to the device controller, wherein the in-situ process motion achieved by the transport device maps to a predetermined basic motion of the predetermined set of motion basics, And defining another predetermined motion group of the transport device using the mapped in-situ process motion;

Recording system records a predetermined operational profile embodying at least one dynamic performance variable output by the transport device, the predetermined operational data implementing another predetermined set of motions, and utilizing a processor to determine another normalized value, the other returning The normalized value statistically characterizes the in-situ process performance of each of the dynamic performance variables output by the transport device, the another normalized value effecting the mapped in-situ process motion of another predetermined set of motions;

Using the processor to compare another normalized value and a normalized value for each dynamic performance variable of the transport device corresponding to the predetermined basic motion group and the other predetermined motion group, respectively, and based on the comparison The performance determines the performance degradation rate of the transport device, wherein the device is unique, and each normalized value (C _pkBase ) of each predetermined basic motion of the predetermined motion basic group and each of the other predetermined motion groups Each of the other values of the mapped in-situ process motion (C _pkOther ) is uniquely related only to the unique device, and the determined rate of performance degradation is uniquely related only to the unique device.

In accordance with one or more aspects of the disclosed embodiments, the method further includes providing the system with a plurality of different unique devices and transport devices connected to each other, wherein each of the plurality of different unique devices (i) is different The unique device has different corresponding normalized values (C _pkBasei ) for each basic motion of the predetermined basic motion group and other normalized values of the in-situ process motion for each mapping of another predetermined motion group (C _pkOtheri ), the normalized value (C _pkBasei ) and the other normalized value (C _pkOtheri ) are uniquely related at most to the corresponding unique device (i) from the plurality of different unique devices Union.

In accordance with one or more aspects of the disclosed embodiments, the method further includes recording, for each different unique device (i), the corresponding normalization to a controller that is separately coupled to the different corresponding unique device The value (C _pkBasei ) and other normalized values (C _pkOtheri ), the corresponding normalized value (C _pkBasei ) and the other normalized value (C _pkOtheri ) are different from the corresponding unique device (i) unique Relevant, and for each different unique device (i), based on device-by-device (i = 1...n), from this uniquely correlated normalized value (C _pkBasei ) and the different unique device (i A comparison between other normalized values (C _pkOtheri ) to determine a corresponding performance degradation rate for the different unique device (i).

In accordance with one or more aspects of the disclosed embodiments, each distinct unique device from the plurality of different unique devices has a common configuration with the transport device.

In accordance with one or more aspects of the disclosed embodiments, each distinct unique device from the plurality of different unique devices has a different configuration than the transport device.

In accordance with one or more aspects of the disclosed embodiments, the method further includes recording, in a record table of the controller, a performance degradation trend characterizing each of the plurality of different unique devices of the transport device and system Trend data.

In accordance with one or more aspects of the disclosed embodiments, the method further includes utilizing a processor to combine the performance degradation trends of the plurality of different unique devices corresponding to the transport device and the system to determine that the feature is characterized The performance of the system deteriorates and the performance of the system deteriorates.

In accordance with one or more aspects of the disclosed embodiments, the method further includes utilizing a processor to compare a performance degradation trend of the transport device with a performance degradation trend of each of the plurality of different unique devices, and utilizing the process The determination of whether the performance deterioration trend of the transport device or the performance deterioration trend of the other of the plurality of different unique devices is a trend of controlling performance deterioration and whether the trend of control performance deterioration is degrading to the performance deterioration trend of the system.

In accordance with one or more aspects of the disclosed embodiments, each predetermined base motion defines a template motion, and each in-situ process motion is substantially mapped onto a corresponding one of the template motions.

In accordance with one or more aspects of the disclosed embodiments, each of the template motions is characterized by at least one of a torque command and a position command from a device controller.

In accordance with one or more aspects of the disclosed embodiments, the at least one torque command and position command characterizes template motion among at least one degree of freedom of movement of the transport device.

In accordance with one or more aspects of the disclosed embodiments, the method further includes recording, in a record table of the device controller, a motion histogram commanded by the device controller, the motion histogram including an in-situ process implemented by the transport device Motion, and wherein the processor decomposes the motion of the mapping from the periodic access motion histograms located in the record table.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a statistical feature number defining at least one common base motion of the base motion type.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a plurality of different basic motion types, wherein each of the basic motion types is co-moving by the transport device for each of the basic motion types Implemented in the number of statistical features.

In accordance with one or more aspects of the disclosed embodiments, each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining and each The basic movement types correspond to different common movements.

In accordance with one or more aspects of the disclosed embodiments, the method further includes recording, by the recording system, trend data for each of the dynamic performance variables, wherein the trend data characterizes a trend of deterioration of the corresponding dynamic performance variable.

In accordance with one or more aspects of the disclosed embodiments, the method further includes utilizing a processor to aggregate a dynamic performance variable having a highest degradation trend of at least one dynamic performance variable output by the transport device, and predicting having a lower than predetermined performance state The performance of the transport device occurs in the event.

In accordance with one or more aspects of the disclosed embodiments, the method further includes utilizing a processor to provide an operator of the transport device with an occurrence of an event regarding the transport device having a performance below a predetermined performance state based on the aggregation of the dynamic performance variables. The indication of the forecast.

In accordance with one or more aspects of the disclosed embodiments, a health assessment device for assessing the health of a system including a delivery device, the health assessment device comprising:

A recording system communicatively coupled to the transport device controller of the transport device, the recording system configured to:

Recording predetermined operational data representing at least one dynamic performance variable output by the transport device, the predetermined operational data implementing a predetermined set of motion basics for predetermined basic motion, and

Recording predetermined operational data representing at least one dynamic performance variable output by the transport device, the predetermined operational data implementing another predetermined motion group;

A motion resolver communicatively coupled to the carrier controller, the motion resolver configured to:

Decomposing an in-situ process motion command of the device controller from the transport device, wherein the in-situ process motion achieved by the transport device maps to the predetermined base motion of the predetermined motion base group, and

Defining another predetermined set of motions of the transport device using the mapped in-situ process motion;

A processor communicatively coupled to the recording system, the processor configured to:

Determining a base value (C _pkBase ) characterized by a probability density function of each dynamic performance variable output by the transport device for each motion of the predetermined motion base set, and

Another value (C _pkOther ) is determined, which is characterized by a probability density function for each dynamic performance variable output by the transport device, the other value implementing a mapped in-situ process of another predetermined motion group Exercise; and,

Comparing another value to a base value (C _pkBase ) for each dynamic performance variable output by the transport device respectively corresponding to the predetermined motion base group and another predetermined motion group, and

Evaluating the health of the delivery tool based on the comparison;

Wherein the transport device is a common transport device of both the predetermined motion base group and the other predetermined motion group.

In accordance with one or more aspects of the disclosed embodiments, each predetermined base motion defines a template motion, and each in-situ process motion is substantially mapped onto a corresponding one of the template motions.

In accordance with one or more aspects of the disclosed embodiments, each template motion is characterized by at least one of a torque command and a position command from a device controller.

In accordance with one or more aspects of the disclosed embodiments, the at least one torque command and position command characterizes template motion among at least one degree of freedom of movement of the transport device.

In accordance with one or more aspects of the disclosed embodiments, the transport device controller includes a log table configured to record a motion histogram commanded by the device controller, the motion histogram including the transport device The in-situ process motion is implemented, and the processor is further configured to decompose the mapped motion from the motion histogram that is periodically accessed in the record table.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a statistical feature number defining at least one common base motion of the base motion type.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a plurality of different basic motion types, wherein each of the basic motion types is co-moving by the transport device for each of the basic motion types Implemented in the number of statistical features.

In accordance with one or more aspects of the disclosed embodiments, each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining and each The basic movement types correspond to different common movements.

In accordance with one or more aspects of the disclosed embodiments, the recording system is further configured to record trend data for each of the dynamic performance variables, wherein the trend data characterizes a trend of deterioration of the corresponding dynamic performance variable.

In accordance with one or more aspects of the disclosed embodiments, the processor is further configured to aggregate the dynamic performance variable having the highest degradation trend of at least one dynamic performance variable output by the transport device, and predicting having a lower than predetermined The performance state of the performance of the transport device occurs.

In accordance with one or more aspects of the disclosed embodiments, the processor is further configured to provide an operator of the transport device with an event regarding the transport device having a performance below a predetermined performance state based on the aggregation of the dynamic performance variables. An indication of the prediction that occurred.

In accordance with one or more aspects of the disclosed embodiments, a health assessment device for assessing the health of a system including a delivery device, the health assessment device comprising:

A recording system communicatively coupled to the transport device controller of the transport device, the recording system configured to:

Recording predetermined operational data embossing at least one dynamic performance variable output by the transport device, the predetermined operational data transport device implementing a predetermined set of motion components set to define statistical characteristics of the predetermined basic motion, and

Recording predetermined operational data representing at least one dynamic performance variable output by the transport device, the predetermined operational data implementing another predetermined motion group;

A motion resolver communicatively coupled to the carrier controller, the motion resolver configured to:

Decomposing an in-situ process motion command of the device controller from the transport device, wherein the in-situ process motion achieved by the transport device maps to the predetermined base motion of the predetermined motion base group, and

Defining another predetermined motion group of the transport device using the mapped in-situ process motion;

A processor communicatively coupled to the recording system, the processor configured to:

Determining a normalized value that statistically characterizes the nominal performance of each dynamic performance variable output by the transport device for each motion of the predetermined set of motion basics,

Determining another normalized value that statistically characterizes the in-situ process performance of each dynamic performance variable output by the transport device, the another normalized value achieving another predetermined motion The in-situ process motion mapped by the group,

Comparing another normalized value to a normalized value for each dynamic performance variable of the transport device corresponding to the predetermined basic motion group and another predetermined motion group, respectively, and

Based on the comparison, the performance degradation rate of the transport device is determined from the nominal performance;

Wherein the transport device is a common transport device for both the predetermined basic motion group and the other predetermined motion group.

In accordance with one or more aspects of the disclosed embodiments, each predetermined base motion defines a template motion, and each in-situ process motion is substantially mapped onto a corresponding one of the template motions.

In accordance with one or more aspects of the disclosed embodiments, each template motion is characterized by at least one of a torque command and a position command from a device controller.

In accordance with one or more aspects of the disclosed embodiments, the at least one torque command and position command characterizes template motion among at least one degree of freedom of movement of the transport device.

In accordance with one or more aspects of the disclosed embodiments, the transport device controller includes a log table configured to record a motion histogram commanded by the device controller, the motion histogram including the transport device The in-situ process motion is implemented, and the processor is further configured to decompose the mapped motion from the motion histogram that is periodically accessed in the record table.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a statistical feature number defining at least one common base motion of the base motion type.

In accordance with one or more aspects of the disclosed embodiments, the predetermined basic motion of the predetermined set of motion includes a plurality of different basic motion types, wherein each of the basic motion types is co-moving by the transport device for each of the basic motion types Implemented in the number of statistical features.

In accordance with one or more aspects of the disclosed embodiments, each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining and each The basic movement types correspond to different common movements.

In accordance with one or more aspects of the disclosed embodiments, the recording system is further configured to record trend data for each of the dynamic performance variables, wherein the trend data characterizes a trend of deterioration of the corresponding dynamic performance variable.

In accordance with one or more aspects of the disclosed embodiments, the processor is further configured to aggregate the dynamic performance variable having the highest degradation trend of at least one dynamic performance variable output by the transport device, and predicting having a lower than predetermined The performance state of the performance of the transport device occurs.

In accordance with one or more aspects of the disclosed embodiments, the processor is further configured to provide an operator of the transport device with an event regarding the transport device having a performance below a predetermined performance state based on the aggregation of the dynamic performance variables. An indication of the prediction that occurred.

It should be understood that the foregoing description is only illustrative of various aspects of the disclosed embodiments. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the disclosed embodiments. Accordingly, the scope of the disclosed embodiments is intended to cover all such alternatives, modifications and In addition, the mere fact that certain features are recited in mutually different sub-claims or separate claims does not mean that a combination of these features may not be used. Such combinations are still within the scope of the invention.

100‧‧‧ Controller

105‧‧‧Processor

110‧‧‧Read-only memory

115‧‧‧ Random access memory

120‧‧‧Program memory

125‧‧‧User interface

130‧‧‧Network interface

135‧‧‧ built-in cache memory

140‧‧‧ display

145‧‧‧ Mouse

150‧‧‧User Interface Controller

155‧‧‧ keyboard

190‧‧‧Communication network

300‧‧‧Automated material processing platform

301‧‧‧Atmospheric part

302‧‧‧vacuum part

303‧‧‧Processing module

304‧‧‧Shell

305‧‧‧Load port

306‧‧‧Atmospheric robotic manipulator

307‧‧‧Base aligner

308‧‧‧Fan filter unit

309‧‧‧vacuum room

310‧‧‧Load lock

311‧‧‧Vacuum robot manipulator

312‧‧‧vacuum pump

313‧‧‧Slit valve

314‧‧‧Tool Controller

315‧‧‧Atmospheric part controller

316‧‧‧Vacuum Part Controller

317‧‧‧Processing controller

318‧‧‧Load port controller

319‧‧‧Atmospheric robot controller

320‧‧‧Aligner Controller

321‧‧‧Fan filter unit controller

322‧‧‧Motor controller

323‧‧‧Vacuum robot controller

400‧‧‧Robot Manipulator

401‧‧‧Robot block

402‧‧‧Flange

403‧‧‧Vertical rail

404‧‧‧Linear bearings

405‧‧‧carrier

406‧‧‧Vertical drive motor

407‧‧‧Ball screw

408‧‧‧Upper motor

409‧‧‧ lower motor

410‧‧‧Encoder 1

411‧‧‧Encoder 2

412‧‧‧External axis

413‧‧‧ inner shaft

414‧‧‧first link

415‧‧‧Belt active lever 2

416‧‧‧second connecting rod

417A‧‧‧Motor A

417B‧‧‧Motor B

418A‧‧‧The first stage of belt drive A

418B‧‧‧Belt Drive Phase 1B

419A‧‧‧The second stage of belt drive A

419B‧‧‧The second stage of belt drive B

420A‧‧‧Upper terminator

420B‧‧‧ lower terminator

421A, 421B‧‧‧ payloads of terminators A and B

422‧‧‧Master controller

423A, 423B, 423C‧‧‧ motor controller

424A, 424B‧‧‧ Electronic units for terminators A and B

425‧‧‧Communication network

426‧‧‧Slip ring

428A, 428B‧‧‧ mapping sensor

429‧‧‧Power supply

430‧‧‧vacuum pump

431A, 431B‧‧‧ valve

432A, 432B‧‧‧ Pressure Sensor

 433, 434A, 434B‧‧‧ lip seal

435‧‧‧ brake

501‧‧‧Basic Mobile 1

502‧‧‧Basic Mobile 2

503‧‧‧Basic Mobile 3

501', 502', 503'‧‧‧ in-situ process movement

STN1-STN6‧‧‧Base Station

700‧‧‧In-situ movement command histogram

700R‧‧‧record form

800‧‧‧Sports resolver

801‧‧‧ data buffer

801R‧‧‧recording system

810‧‧‧Robot controller

810P‧‧‧ processor

820, 820A, 820B, 820C‧‧‧ sports basic group

830, 830A, 830B, 830C‧‧‧ scheduled sports group

840‧‧‧record form

B10, B20, B50, 1300, 1310, 1320, 1330, 1340, 1350, 1360, 1400, 1401, 1402, 1403, 1500, 1501, 1502, 1503, 1504, 1505‧‧‧

870‧‧‧Trend/Evaluation Unit

890‧‧‧Basic Sports Group

890AG‧‧‧single gathering movement

TDR‧‧‧ register

A1-An‧‧‧Evaluation

LTM, LTM1-LTMn‧‧‧ linear trend model

C _pkBasei ‧‧‧ normalized value

App1-Appn‧‧‧ unique device

C _{pkOther(1-n) ‧‧‧Other} values

WS‧‧‧ first scheduled evaluation value

TD‧‧ trend data

ES‧‧‧ second scheduled evaluation value

t _warn , t _warnLTM1 , t _warnLTM2 , t _errorLTM1 , t _error ‧‧‧ time

The foregoing aspects and other features of the disclosed embodiments are explained in the following description in conjunction with the drawings in which:

1 is a schematic diagram of a controller for an automated device, such as an automated material processing platform in accordance with an embodiment of the disclosed embodiment;

2 is a schematic illustration of an automated material processing platform in accordance with an embodiment of the disclosed embodiment;

2A is a schematic illustration of a system including a plurality of different unique devices in accordance with aspects of the disclosed embodiments;

3 is a schematic illustration of an apparatus (eg, a transport robot) of an automated material processing apparatus in accordance with an embodiment of the disclosed embodiment;

4A-4E are schematic illustrations of different arm configurations of the device of FIG. 3 in accordance with an aspect of the disclosed embodiment;

5A is a schematic illustration of a portion of an automated material processing platform showing basic movement and in situ process movement in accordance with aspects of the disclosed embodiment;

5B and 5C are schematic illustrations of simple and complex movements of aspects in accordance with the disclosed embodiments;

6 is an exemplary diagram illustrating statistical convergence of moving samples performed by the apparatus of FIG. 3 in accordance with aspects of the disclosed embodiments;

7 is an exemplary moving histogram of an aspect in accordance with an embodiment of the disclosed embodiment;

8A is a schematic diagram of an exemplary process flow in accordance with aspects of the disclosed embodiments;

8B is a schematic diagram of a portion of the exemplary process flow of FIG. 8A in accordance with an aspect of the disclosed embodiment;

9 is an exemplary Gaussian distribution of moving samples indicating upper and lower limits of an aspect in accordance with an embodiment of the disclosed embodiment;

10 is a legend of a comparison between a baseline value of a pattern according to an embodiment of the disclosed embodiment and another value resulting from movement from an in situ process;

11 is an exemplary illustration of an application of a health assessment of the device of FIG. 3 with respect to predictive diagnosis, in accordance with an aspect of the disclosed embodiment;

12 is an exemplary illustration of a health assessment indication in accordance with an aspect of the disclosed embodiment;

13 is an exemplary flow diagram of aspects in accordance with disclosed embodiments;

14 is a flow chart of aspects in accordance with an embodiment of the disclosure;

15 is a flow chart of aspects in accordance with disclosed embodiments.

Claims (42)

A health assessment method for a system including a transport device, the method comprising: recording, by a recording system communicatively coupled to the device controller, predetermined operational data embodying at least one dynamic performance variable output by the transport device, Determining operational data to implement a predetermined set of basic motions for a predetermined basic motion; determining, by a processor communicatively coupled to the recording system, a base value (C _pkBase ) for each of the predetermined motion basic groups by the transport device Characterizing the probability density function of each of the dynamic performance variables output by the motion; utilizing a motion resolver communicatively coupled to the device controller to decompose the in-situ process motion command of the device controller from the transport device, wherein The in-situ process motion implemented by the transport device maps to the predetermined base motion of the predetermined motion base group, and defines another predetermined motion group of the transport device using the mapped in-situ process motion; recording the embodiment using the recording system The at least one dynamic performance variable output by the transport device Predetermined operation data, the operation data predetermined to achieve a further predetermined set of motion, and determining another value _(C pkOther) using the processor, each of the dynamic performance of the other variable by the value of the conveying means the output probability a density function to characterize, the other value (C _pkOther ) implementing the mapped in-situ process motion of the other predetermined motion group; and utilizing the processor to respectively correspond to the predetermined motion basic group and the Each of the dynamic performance variables output by the transport device of another predetermined motion group compares the other value with the base value (C _pkBase ), wherein the transport device is for the predetermined motion basic group and the other predetermined motion The group is a unique transport device and is common, and the health of the transport device is assessed based on the comparison. The method of claim 1, wherein each of the predetermined basic motions defines a template motion, and each in situ process motion is substantially mapped to a corresponding one of the template motions. The method of claim 2, wherein each of the template motions is characterized by at least one of a torque command and a position command from the device controller. The method of claim 3, wherein the at least one of the torque command and the position command characterize the template motion among at least one degree of freedom of movement of the transport device. The method of claim 1, wherein the method further comprises recording, in a record table of the device controller, a motion histogram commanded by the device controller, the motion histogram comprising an in-situ process motion implemented by the transport device And wherein the processor decomposes the motion of the mapping from the motion histogram that is periodically accessed in the record table. The method of claim 1, wherein the predetermined basic motion of the predetermined motion basic group comprises a statistical feature number defining at least one common basic motion of the basic motion type. The method of claim 1, wherein the predetermined basic motion of the predetermined motion basic group comprises a plurality of different basic motion types, wherein each of the basic motion types is co-moved by the transport device for each of the basic motion types Implemented in the number of statistical features. The method of claim 7, wherein each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining each The basic movement types correspond to different common movements. The method of claim 1, wherein the method further comprises recording, by the recording system, trend data of each of the dynamic performance variables, wherein the trend data characterizes a deterioration trend of the corresponding dynamic performance variable. The method of claim 9, the method further comprising: utilizing the processor to aggregate the dynamic performance variable of the at least one dynamic performance variable output by the transport device having the highest deterioration trend, and predicting that the performance is lower than a predetermined performance The state of performance of the event of the transport device occurs. The method of claim 10, the method further comprising utilizing the processor to provide the operator of the transport device with the event regarding the transport device having a performance lower than a predetermined performance state based on the aggregation of the dynamic performance variable. An indication of the occurrence of the prediction. A health assessment method for a system including a transport device, the method comprising: recording, by a recording system communicatively coupled to a device controller, predetermined operational data embodying at least one dynamic performance variable output by the transport device, The predetermined operational data implements a predetermined set of motions set to define statistical features of the predetermined base motion; determining a normalized value using a processor communicatively coupled to the recording system, the normalized value being statistically characterized a nominal performance of each of the dynamic performance variables output by the transport device for each motion of the predetermined set of motion; decomposing the device from the transport device using a motion resolver communicatively coupled to the device controller An in-situ process motion command of the controller, wherein the in-situ process motion implemented by the transport device maps to the predetermined base motion of the predetermined motion base group, and the in-situ process motion of the map is utilized to define another of the transport devices Predetermined motion group; recording using the recording system embodies the output by the transport device a predetermined operational profile of the at least one dynamic performance variable, the predetermined operational profile implementing another predetermined set of motions, and utilizing the processor to determine another normalized value, the another normalized value being statistically characterized An in-situ process performance of each of the dynamic performance variables output by the transport device, the another normalized value effecting the mapped in-situ process motion of the another predetermined set of motions; and utilizing the processor to respectively correspond to Comparing the other normalized value to the normalized value for each of the dynamic performance variables of the predetermined basic motion group and the other predetermined motion group, and determining from the nominal performance based on the comparison a performance degradation rate of the transport device, wherein the device is unique, and each of the predetermined normalized values of the predetermined basic motion group (C _pkBase ) and each of the other predetermined motion groups Each of the other values of the in-situ process motion (C _pkOther ) is uniquely related only to the unique device, and the determined performance degradation rate is uniquely related only to the unique device. The method of claim 12, the method further comprising providing the system with a plurality of different unique devices and the transport device connected to each other, wherein the different unique ones from the plurality of different unique devices (i) The device has a different corresponding normalized value (C _pkBasei ) for each basic motion of the predetermined basic motion group and other normalization of the in-situ process motion for each mapping of the other predetermined motion group a value (C _pkOtheri ), the normalized value (C _pkBasei ) and the other normalized value (C _pkOtheri ) are uniquely uniquely different from the corresponding unique device (i) from the plurality of different unique devices Associated. The method of claim 13, wherein the method further comprises recording, for each of the different unique devices (i), the corresponding normalized value to the controller separately coupled to the different corresponding unique device (C) _pkBasei ) and the other normalized value (C _pkOtheri ), the corresponding normalized value (C _pkBasei ) and the other normalized value (C _pkOtheri ) are uniquely related to the different corresponding unique device (i) And for each of the different unique devices (i), based on the device-by-device (i = 1...n), from the uniquely correlated normalized value (C _pkBasei ) and the different unique device (i) A comparison between the other normalized values (C _pkOtheri ) determines the corresponding performance degradation rate for the different unique device (i). The method of claim 13, wherein each of the different unique devices from the plurality of different unique devices has a common configuration with the transport device. The method of claim 13, wherein each of the different unique devices from the plurality of different unique devices has a different configuration than the transport device. The method of claim 13, wherein the method further comprises recording trend data in a record table of the controller, the trend data characterizing performance degradation of the plurality of different unique devices of the transport device and the system trend. The method of claim 13, the method further comprising utilizing the processor to combine the performance degradation trend of each of the plurality of different unique devices corresponding to the transport device and the system to determine characteristics of the system. Deteriorating system performance deterioration trend. The method of claim 13, the method further comprising comparing, by the processor, the performance deterioration trend of the transport device with the performance deterioration trend of each of the plurality of different unique devices, and using the processor It is determined whether the performance deterioration trend of the transport device or the performance deterioration tendency of the other of the plurality of different unique devices is a trend of the control performance deterioration tendency and whether the control performance deterioration tendency is deteriorating the performance deterioration trend of the system. The method of claim 12, wherein each of the predetermined basic motions defines a template motion, and each of the in situ process motions is substantially mapped to a corresponding one of the template motions. The method of claim 20, wherein each of the template motions is characterized by at least one of a torque command and a position command from the device controller. The method of claim 21, wherein the at least one of the torque command and the position command characterize the template motion among at least one degree of freedom of movement of the transport device. The method of claim 12, the method further comprising recording, in a record table of the device controller, a motion histogram commanded by the device controller, the motion histogram comprising an in-situ process motion implemented by the transport device And wherein the processor decomposes the motion of the mapping from the motion histogram that is periodically accessed in the record table. The method of claim 12, wherein the predetermined basic motion of the predetermined motion basic group comprises a statistical feature number defining at least one common basic motion of the basic motion type. The method of claim 12, wherein the predetermined basic motion of the predetermined motion basic group comprises a plurality of different basic motion types, wherein each of the basic motion types is co-moved by the transport device for each of the basic motion types Implemented in the number of statistical features. The method of claim 25, wherein each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining each of the basic motion types Corresponding different common movements. The method of claim 12, the method further comprising recording, by the recording system, trend data of each of the dynamic performance variables, wherein the trend data characterizes a deterioration trend of the corresponding dynamic performance variable. The method of claim 27, the method further comprising: utilizing the processor to aggregate the dynamic performance variable of the at least one dynamic performance variable output by the transport device having the highest deterioration trend, and predicting that the performance is lower than a predetermined performance The state of performance of the transport device occurs for this event. The method of claim 28, the method further comprising utilizing the processor to provide the operator of the transport device with the event regarding the transport device having a performance below a predetermined performance state based on the aggregation of the dynamic performance variable. An indication of the occurrence of the prediction. A health assessment device for assessing the health of a system including a transport device, the health assessment device comprising: a recording system communicatively coupled to a transport device controller of the transport device, the recording system configured to: record embodies At least one predetermined operational data of the dynamic performance variable output by the transport device, the predetermined operational data realizing a predetermined set of motion basics of the predetermined basic motion, and recording predetermined operational data embodying at least one dynamic performance variable output by the transport device The predetermined operational data implements another predetermined motion group; and a motion resolver communicatively coupled to the transport device controller, the motion resolver configured to: decompose the device controller from the transport device in situ a process motion command, wherein an in-situ process motion implemented by the transport device maps to the predetermined base motion of the predetermined motion base group, and another scheduled motion group of the transport device is defined using the mapped in-situ process motion; a processor communicatively coupled to the recording system The processor is configured to: determine a base value (C _pkBase ) characterized by a probability density function of each of the dynamic performance variables output by the transport device for each motion of the predetermined motion base group And determining another value (C _pkOther ), the another value being characterized by the probability density function of each of the dynamic performance variables output by the transport device, the another value achieving the other predetermined motion group a mapped in-situ process motion for the other value and the base value for each of the dynamic performance variables output by the transport device respectively corresponding to the predetermined motion base group and the other predetermined motion group (C _pkBase Comparing, wherein the transport device is a unique transport device for both the predetermined motion basic group and the other predetermined motion group and is common, and the health condition of the transport device is evaluated based on the comparison; The transport device is a common transport device for both the predetermined motion base group and the other predetermined motion group. The apparatus of claim 30, wherein each of the predetermined basic motions defines a template motion, and each of the in situ process motions is substantially mapped to a corresponding one of the template motions. The device of claim 31, wherein each of the template motions is characterized by at least one of a torque command and a position command from the device controller. The apparatus of claim 32, wherein the at least one of the torque command and the position command characterize the template motion among at least one degree of freedom of movement of the transport device. The apparatus of claim 30, wherein the transport device controller includes a record table configured to record a motion histogram commanded by the device controller, the device controller including an original implemented by the transport device The bit process moves and the processor is further configured to decompose the motion of the mapping from the motion histogram that is periodically accessed in the record table. The apparatus of claim 30, wherein the predetermined basic motion of the predetermined motion basic group comprises a statistical feature quantity defining at least one common basic motion of the basic motion type. The apparatus of claim 30, wherein the predetermined basic motion of the predetermined motion basic group comprises a plurality of different basic motion types, wherein each of the basic motion types is co-moved by the transport device for each of the basic motion types Implemented in the number of statistical features. The apparatus of claim 36, wherein each of the different basic motion types has a different respective at least one torque command characteristic and a position command characteristic, the torque command characteristic and the position command characteristic defining each of the basic motion types Corresponding different common movements. The apparatus of claim 30, wherein the recording system is further configured to record trend data for each of the dynamic performance variables, wherein the trend data characterizes a deterioration trend of the corresponding dynamic performance variable. The apparatus of claim 38, wherein the processor is further configured to aggregate the dynamic performance variable of the at least one dynamic performance variable output by the transport device having the highest trend of deterioration, and predicting having a lower than The occurrence of an event of the transport device that predicts the performance of the performance state. The apparatus of claim 39, wherein the processor is further configured to provide the operator of the transport device with respect to the transport device having performance below a predetermined performance state based on the aggregate of the dynamic performance variables. An indication of the prediction of the occurrence of the event. A health assessment device for assessing the health of a system including a transport device, the health assessment device comprising: a recording system communicatively coupled to a transport device controller of the transport device, the recording system configured to: record embodies At least one predetermined operational data of the dynamic performance variable output by the transport device, the predetermined operational data implementing a predetermined set of motions set to define statistical characteristics of the predetermined basic motion, and recording at least one of the outputs embodied by the transport device a predetermined operational data of the dynamic performance variable, the predetermined operational data implementing another predetermined motion group; a motion resolver communicatively coupled to the transport device controller, the motion resolver configured to: decompose the transport device from the transport device An in-situ process motion command of the device controller, wherein the in-situ process motion implemented by the transport device maps to the predetermined base motion of the predetermined motion base group, and the in-situ process motion using the map defines the transport device Another predetermined motion group; and a processor communicatively coupled to the recording system, the processor configured to: determine a normalized value that is statistically characterized by the transport device The nominal performance of each of the dynamic performance variables output by each motion of the predetermined motion basic set determines another normalized value that statistically characterizes each output by the transport device An in-situ process performance of the dynamic performance variable, the another normalized value implementing the mapped in-situ process motion of the another predetermined set of motions for respectively corresponding to the predetermined base motion group and the further predetermined motion group Each of the dynamic performance variables of the transport device compares the other normalized value with the normalized value and determines a performance degradation rate of the transport device based on the comparison, wherein the transport device is a common transport device for both the predetermined basic motion group and the other predetermined motion group. The apparatus of claim 41, wherein the apparatus is unique, and each of the predetermined normal motions of the predetermined motion basic group and the normalized value of each of the other predetermined motion groups Each other value of the process motion is uniquely related to only that unique device, and the determined rate of performance degradation is uniquely related to only that unique device.