TW202324011A - 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 transport devices

CROSS REFERENCE TO RELATED APPLICATIONS: This patent application claims priority and benefit to U.S. Provisional Patent Application No. 62/502,292, filed May 5, 2017, the disclosure of which is hereby incorporated by reference in its entirety. Exemplary embodiments relate generally to automated handling systems.

1. Field: The exemplary embodiments relate more particularly to health assessment and predictive diagnostics 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 used to produce semiconductor components, are common problems that often Represents a substantial cost burden for the end user of the manufacturing tool.

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 that something is wrong somewhere in the monitored system, fault isolation to determine the exact location of the fault (ie, the failed component), and fault identification to determine the magnitude of the fault.

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

While these fault diagnosis schemes facilitate fault detection, isolation and adaptive recovery among them, still make components, tools, fabs (such as fabs/factories) or other automated devices operate with limited or virtually nonexistent predictive range Essentially responsive way to run. Prediction methods are recognized as attempts to increase the scope of prediction to fault diagnosis systems, such as mathematical modeling of automation devices, where sensory measurements of automation device variables are compared with individual variables (such as from Newtonian dynamics models or class The analytically calculated values generated by the neural network dynamics model) are compared, and the mathematical model represents the nominal conditions. This approach is subject to non-conservative factors such as signal noise and model errors, which unpredictably and adversely affect the resulting difference between the analytical (nominal) value and the value derived from the sensory measurement, and requires Fault diagnosis systems make further investments in processing power and/or duplicate/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 aspects of the disclosed embodiments will be described with reference to the drawings, it is to be understood that aspects of the disclosed embodiments may be embodied in many forms. In addition, any suitable size, shape or type of elements or materials could be used.

Aspects of the disclosed embodiments described herein provide a method and apparatus for quantifying the health status and predictive diagnostics of automated systems, such as those described herein with respect to FIGS. 1-4E , using available variables that are available Variables are monitored by any suitable controller of the automated system (where the controller includes non-transitory computer software code for implementing aspects of the disclosed embodiments). Metrics of state of health are achieved by aspects of the disclosed embodiments through unique statistical processing for variables collected that are uniquely associated with a given device and/or system of multiple devices and A given device and/or system of the plurality of devices is uniquely characterized as a state of health quantity providing a predictive diagnosis of the given device and/or system. Aspects of the disclosed embodiments may allow a controller of an automated system to use the concept of a "baseline" (which includes base values and/or base motions as described herein) to determine statistics for monitored variables (of unique elements) characteristics (unique characteristics of that unique element), and facilitate further comparison of future performance with these baselines. As a result, the methods and apparatus of the disclosed embodiments may allow controllers of automated systems to perform predictions based on trend analysis, allowing controllers of automated systems to make recommendations for preventive maintenance based on data distinct from the automated systems being monitored . Aspects of the disclosed embodiments may also allow identification of expected limits to operating with variables that make it difficult to determine acceptable and feasible specifications.

Although aspects of the disclosed embodiments of a semiconductor robot (also referred to herein as a robotic manipulator) with three degrees of freedom (theta rotation, R extension, and Z lift motion) will be described herein, in other aspects, semiconductor A robot may have more or less than three degrees of freedom. In still other aspects, the disclosed embodiments may be applied to other components of a semiconductor processing system having a single degree of freedom of motion (eg, robotic handling, load ports, aligners, pumps, fans, valves, etc.). It should also be understood that aspects of the disclosed embodiments may be used with any automation and/or power receiving component or system (including, for example, combinations of devices and/or components described above), which any automation and/or power receiving component or The system is capable of sampling similar or related performance monitoring data that is uniquely associated with and uniquely characterizes each unique device, component, and/or system.

Aspects of the disclosed embodiments provide a type of metric based on normalization of statistical parameters that allow for direct comparison of variables of different physical meaning (eg, temperature and maximum torque). Such a comparison allows calculation of the impact of these extraneous variables on the overall health of the automated system being monitored.

FIG. 1 shows an exemplary controller 100 for an automated device including automated device health assessment and predictive diagnostics according to aspects of the disclosed embodiments. Aspects of the disclosed embodiments can be implemented in hardware or software. For example, aspects of the disclosed embodiments may reside within a component controller, a controller directing the operation of multiple components, a controller controlling a subsystem, or a system controller. Aspects of the disclosed embodiments can also be implemented in dedicated hardware or software.

Controller 100 may be any suitable controller for an automated device such as automated material handling platform 300 shown in FIG. , program memory 120, user interface 125 and network interface 130. Processor 105 may include built-in cache memory 135 and is generally operable to access data from a computer program product, for example, a computer-usable medium such as built-in cache memory 135, read-only memory 110, random access memory 115 and program memory 120) to read information and programs.

At power-on, processor 105 can begin operating programs found in read-only memory 110, and after initialization, can load instructions from program storage 120 into random-access memory 115 and control them under the control of those programs. down operation. Frequently used instructions can be temporarily stored in the built-in cache memory 135 . Both ROM 110 and RAM 115 may utilize semiconductor technology or any other suitable materials and technologies. The program storage 120 may include a magnetic disk, a computer hard disk, a compact disk (CD), a digital versatile disk (DVD), an optical disk (optical disk), a chip, a semiconductor, or any device capable of storing programs in the form of computer-readable code. other devices.

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

Specifically, built-in cache memory 135 , read-only memory 110 , random access memory 115 , and program storage 120 , alone or in any combination, may include functions for causing processor 105 to execute Procedures for fault diagnosis and fault prediction of aspects of the disclosed embodiments are described. Network interface 130 may generally be adapted to provide an interface between controller 100 and other controllers or other systems. Network interface 130 may be operable to receive data from one or more additional function controllers and to communicate data to the same or other function controllers. The network interface 130 may also provide an interface to a global diagnostic system that may provide remote monitoring and diagnostic services.

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

The controller 100 may include a user interface 125 having a display 140 and an input device such as a keyboard 155 or a mouse 145 , for example. The user interface may be operated by the user interface controller 150 under the control of the processor 105, and may provide a graphical user interface to the user to visualize the results of health monitoring and fault diagnosis. The user interface can also be used to guide maintenance personnel through troubleshooting routines or maintenance procedures. In addition, the user interface controller may also provide a connection or interface 180 for communicating with other function controllers, an external network, another control system, or a host computer.

An exemplary material processing platform for fabricating a semiconductor device in which aspects of the disclosed embodiments may be used is schematically depicted in FIG. 2, along with an illustrative description of the major components listed in Table 1. accomplish. The one or more controllers of the material handling platform of FIG. 2 may include a controller as described herein with respect to FIG. 1 .

Table 1 : Explanatory description of the automated material handling platform 300 (also referred to as a processing tool) of FIG. 2 . serial number illustrate 301 atmospheric part 302 Vacuum part 303 processing module 304 shell 305 load port 306 Atmospheric Robotic Manipulator 307 Substrate aligner 308 fan filter unit 309 vacuum chamber 310 load lock 311 Vacuum robot manipulator 312 vacuum pump 313 Slit valve 314 tool controller 315 Atmospheric part controller 316 Vacuum section 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

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

Atmospheric portion 301 may include housing 304 , one or more load ports 305 , one or more robotic manipulators 306 , one or more substrate aligners 307 , and fan filter unit 308 . It may also include one or more ionization units (not shown). The vacuum section 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 generally located between the atmospheric section 301 and the load locks 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 .

Operation of the platform is coordinated by a tool controller 314 , a vacuum section controller 316 , and one or more process controllers 317 , which monitor an atmospheric section controller 315 . Atmospheric section controller 315 is responsible for one or more loadport controllers 318 , one or more atmospheric robot controllers 319 , one or more aligner controllers 320 , and fan filter unit controllers 321 . Each of loadport controller 318 , atmospheric robot controller 319 , and aligner controller 320 is in turn responsible for one or more motor controllers 322 . Vacuum section controller 316 is responsible for one or more vacuum robot controllers 323 , controls vacuum pump 312 and operates 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 practical to combine two or more control layers into a single controller. For example, the atmospheric robot controller 319 and corresponding motor controller 322 could be combined in a single centralized robotic controller, or the atmospheric section controller 315 could be combined with the atmospheric robot controller 319 to eliminate the need for two separate controller units. need.

Five-axis direct drive robotic manipulator 400 may be employed in automated material handling platform 300 of FIG. FIG. 3 provides a simplified schematic diagram of one such robotic manipulator 400 . Explanatory notes for the main components are listed in Table 2. In one aspect, aspects of the disclosed embodiments can be implemented within robotic manipulator 400; however, it should be understood that while aspects of the disclosed embodiments are described with respect to a robotic manipulator, the described Aspects of the disclosed embodiments may be implemented in any suitable automated portion of the automated material handling platform 300, including but not limited to delivery robots, load ports, aligners, pumps, fans, valves, etc. , note that the controller 800 in FIG. 8A is a general representation of a controller for any of the automation devices described above. Note that robotic manipulator 400 is depicted as a five-axis direct drive robotic manipulator for exemplary purposes only, and that otherwise, a robotic manipulator (or other automated portion of a processing tool including aspects of the disclosed embodiments ) can have any suitable number of drive shafts, with any suitable degrees of freedom, and with a direct or indirect drive system.

Table 2: Explanatory description of the robotic manipulator 400 of FIG. 3 . serial number illustrate 401 Robot Cube Rack 402 mounting flange 403 vertical rail 404 linear bearing 405 carrier 406 vertical drive motor 407 ball screw 408 Motor 1 (active lever 1) 409 Motor 2 (active rod 2) 410 Encoder 1 (connected to motor 1) 411 Encoder 2 (connected to motor 2) 412 Outer shaft 413 inner shaft 414 Link 1 (upper arm) 415 Belt drive lever 2 416 Link 2 (forearm) 417A Motor A (drive terminator A) 417B Motor B (drive terminator B) 418A Belt-driven first stage A 418B Belt-driven first stage B 419A Belt-driven second stage A 419B Belt-driven second stage B 420A Terminator A (upper terminator) 420B Terminator B (lower terminator) 421A, 421B Payload of terminators A and B 422 main controller 423A, 423B, 423C motor controller 424A, 424B Electronic unit for terminators A and B 425 communication network 426 slip ring 428A, 428B map sensor 429 Power Supplier 430 vacuum pump 431A, 431B valve 432A, 432B pressure sensor 433, 434A, 434B lip seal 435 brake

Referring to FIG. 3 , a robotic manipulator 400 is constructed around an open cylindrical cube frame 401 suspended from a circular mounting flange 402 . Cube frame 401 includes vertical rails 403 with linear bearings 404 to provide guidance to carriers 405 driven by brushless DC motors 406 via ball screw mechanisms 407 . The 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 connected to a first link 414 of the robotic arm. The lower motor 409 is connected to a coaxial inner shaft 413 which is coupled to a second linkage 416 via a belt drive 415 . The first linkage 414 houses a brushless DC motor 417A which drives an upper terminator 420A via a two-stage belt arrangement 418A, 419A. The lower terminator 420B is actuated using another DC brushless motor 417B and two-stage belt drives 418B, 419B. Each stage 418A, 418B, 419A, and 419B is designed to have a 1:2 ratio between input and output pulleys. Substrates 421A and 421B are held attached to terminators 420A and 420B, respectively, by means of vacuum actuated edge contact grippers, surface contact suction grippers, or passive grippers.

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. Points A, B, and C represent the rotational coupling of the shoulder, elbow, and wrist joints, respectively. Point D represents a reference point that indicates the desired location of the center of the substrate on the corresponding terminator.

The control system of the example robotic manipulator may be distributed. It includes a power supply 429, a main controller 422 and motor controllers 423A, 423B and 423C. The master controller 422 is responsible for overseeing mission and trajectory planning. Each motor controller 423A, 423B, and 423C implements a position and current feedback loop for one or both motors. In FIG. 3 , a controller 423A controls motors 408 and 409 , a controller 423B controls motors 417A and 417B, and a controller 423C controls motor 406 . In addition to implementing the feedback loop, the motor controller collects data such as motor current, motor position, and motor speed, and transmits the data file-by-file to the main controller. Motor controllers 423A, 423B, and 423C are connected to the main controller through a high-speed communication network 425 . Since joint A is an infinite rotary joint, communication network 425 is routed through slip ring 426 . Additional electronics units 424A and 424B may be used to support the edge contact holders of terminators 420A and 420B, respectively.

Referring now to FIGS. 4A-4E , the robotic manipulator 400 of FIG. 3 may include any suitable arm linkage. Suitable examples of arm linkages can be found, for example, in U.S. Patent Nos. 7,578,649 issued August 25, 2009; 5,794,487 issued August 18, 1998; 7,946,800 issued May 24, 2011; 6,485,250 published, 7,891,935 published February 22, 2011, 8,419,341 published April 16, 2013, and U.S. Patent Application Nos. 13/293,717 and 13/ 861,693, filed September 5, 2013, titled "Linear Vacuum Robot with Z Motion and Articulated Arm", the disclosure of which is incorporated herein by reference in its entirety. In aspects 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 derived from a conventional SCARA arm 315 (Selectively Compliant Articulating Robotic Arm) (FIG. 4C) type of design that includes an upper arm 315U, a driven forearm 315F, and a constrained terminator 315E, or may be derived from a telescoping arm or any other suitable arm design such as a Cartesian linear sliding arm 314 (FIG. 4B). Suitable examples of transport arms can be found, for example, in U.S. Patent Application No. 12/117,415, filed May 8, 2008, entitled "Substrate Transport Apparatus with Multiple Movable Arms Utilizing a Mechanical Switch Mechanism," and U.S. Patent Application No. 12/117,415, published January 19, 2010. Patent No. 7,648,327, the disclosure of which is incorporated herein by reference in its entirety. The transport arms may operate independently of each other (e.g., each arm extends/retracts independently of the other arms), may be operated by an idle switch, or may be operatively connected in any suitable manner such that the arms share at least one common drive shaft. In still other aspects, the transport arms may have any other desired arrangement, such as a frog leg arm 316 (FIG. 4A) configuration, a jumping frog arm 317 (FIG. 4E) configuration, a double symmetrical arm 318 (FIG. 4D) configuration, etc. Examples of suitable delivery arms can be found in U.S. Patents 6,231,297 issued May 15, 2001, 5,180,276 issued January 19, 1993, 6,464,448 issued October 15, 2002, 6,224,319 issued May 1, 2001, US Patent 5,447,409 issued September 5, 1995, US Patent 7,578,649 issued August 25, 2009, 5,794,487 issued August 18, 1998, 7,946,800 issued May 24, 2011, November 26, 2002 6,485,250 published, 7,891,935 published February 22, 2011 and U.S. Patent Application No. 13/293,717, filed November 10, 2011, entitled "Dual Arm Robot" and filed October 11, 2011, entitled Found in 13/270,844 of "Coaxial Drive Vacuum Robot". Its entire content is incorporated herein by reference.

Still referring to FIGS. 2-4E , robotic manipulators 306 , 311 , 400 described herein transport substrates S (see FIGS. 4A and 4B ) between points in space, such as substrate holding stations STN1 - STN6 shown in FIG. 5A . To accomplish the transport of the substrate S, motion control algorithms are run in any suitable controller of the automated material handling platform 300, such as robotic controllers (also referred to as robotic manipulator controllers) 319, 323, 422, 423A-423C, 810 (see FIGS. 2 , 3 and 8A ), which is connected to the robotic manipulator 306 , 311 , 400 . The motion control algorithm defines the desired substrate path in space, and the position control loop calculates the desired control torque (or force) to apply to each robot actuator responsible for moving the individual robot degrees of freedom in space.

The robotic manipulators 306, 311, 400 (which may be referred to as automated systems) are expected to perform repetitive tasks of continuously transporting substrates S, and are subject to environmental conditions associated with handling such substrates. Methods and apparatus provided with aspects like the disclosed embodiments monitor the performance of robotic manipulators (or any other automated equipment of automated material handling platform 300 ) over time and determine (predictive diagnosis) It is beneficial if 400 is able to operate within desired parameters in order to handle its primary tasks, such as carrying and transporting substrates between substrate holding stations STN1-STN6.

According to aspects of the disclosed embodiments, health assessments such as those made for robotic manipulators 306, 311, 400 are performed by generating basic statistical characteristics (e.g., a baseline of behavior for a given variable operating under typical environmental conditions) or statistical representation) for a set of basic movements/motions (the terms movement and motion are used interchangeably herein) 820, 820A, 820B, 820C of the robotic manipulators 306, 311, 400 (see Fig. 8A) Characterize each dynamic performance variable output by the robotic manipulator 306 , 311 , 400 . The basic statistical characteristics are obtained by utilizing the recording system 801R (which may be provided by any Suitable memory is formed or resides in any suitable memory, such as memory 801) to generate, for example, recording predetermined operating data reflecting at least A dynamic performance variable in which the predetermined operating profile implements a predetermined set of motion primitives of predetermined primitive motions.

Each dynamic performance variable is specific to an automation system (e.g. robotic manipulator 306, 311, 400), which may be in a group of different automation systems (e.g. the group of automation systems forming automated material handling platform 300), from which Dynamic performance variables. In this way, 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, robotic manipulator 306 located in atmospheric portion 301 of automated material handling platform 300 has relative base statistical characteristics, and robotic manipulator 311 located in vacuum portion 302 has relative base statistical characteristics. If the robotic manipulator 311 is placed in the atmospheric portion 301 , the basic statistical characteristics of the robotic manipulator 311 can still be applied to the robotic manipulator 311 when placed in the atmospheric portion 301 . In one aspect, the base statistical characteristics are associated with the relative automation system residing in memory and/or in a controller of the automation system. Additionally, each robotic manipulator may have unique operating characteristics that affect the underlying statistical characteristics of the opposing robotic manipulator. For example, robotic manipulator 311 and another robotic manipulator may be manufactured as the same make and model of robotic manipulator. However, the basic statistical characteristics of the robotic manipulator 311 may not apply to other similar robotic manipulators, and vice versa, due to, for example, manufacturing tolerances present in the robot drive system and arm structure. Thus, the base statistics of each robotic manipulator are paired with the corresponding robotic manipulator (e.g., the base statistics _Cpkbase of the robotic manipulator 311 moves with and is unique to the robotic manipulator 311, and the robotic manipulator The base statistical feature _Cpkbase of 306 moves with the robotic manipulator 306 and is unique to it). Thus, each device, such as a robotic manipulator 311, is unique and each normalized value or basic statistical feature/value _Cpkbase and each other value _Cpkother for each mapped in situ process movement 501', 502', 503' for the other predetermined set of movements 830, 830A-830C is uniquely associated only with a unique device, and The determined performance degradation rate (eg indicated by the linear trend model LTM - see Figure 11) is only uniquely associated with unique devices.

In one aspect, a system (such as the automated material handling platform 300 shown in FIG. 3 ) includes or is otherwise provided with a plurality of different interconnected unique device unit 308 etc. listed in Table 1 and shown in FIG. 2 ), and, for example, the transport device 311, from a plurality of different unique devices App(i) (represented graphically in FIG. 2A as Each different unique device of App1-Appn) has a different corresponding normalized value _CpkBasei (which includes _{CpkBase( 1-n)} ), and other normalized values _CpkOtheri of each mapped in-situ process movement 501', 502', 503' for other predetermined motion groups 830, 830A-830C, which other predetermined motion groups 830 , 830A-830C are uniquely associated with no more than the different corresponding unique devices App1-Appn from the plurality of different unique devices App(i). In one aspect, each (or at least one) of the different unique devices App1-Appn from the plurality of different unique devices App(i) has a common configuration with another of the different unique devices App1-Appn. For example, robotic manipulator 306 may have a common configuration with robotic manipulator 311 . In other aspects, each (or at least one) of the different unique devices App1-Appn from the plurality of different unique devices App(i) has a different configuration than the other from the different unique devices App(i). For example, aligner 307 has a different configuration than robotic manipulator 306 .

The dynamic performance variables of each automated device and/or system can be measured directly (ie, continuously monitored variables) or derived from available measurements (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 a motor is the percentage of the input voltage supplied to each motor phase at any given time. Health monitoring and fault diagnosis system can be used in 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 as an absolute value or as a percentage of the maximum current. If obtained in absolute value, its unit is ampere. The motor current value can be used in turn to calculate the motor torque by using the motor torque-current relationship;

Actual Position, Velocity and Acceleration: These are the position, velocity and acceleration of each motor shaft. For rotary axes, position, velocity, and acceleration values are in degrees, degrees/second, and degrees/ ^second2 , respectively. For the translation axis, the position, velocity and acceleration values are in mm, mm/ ^s2 and mm/ ^s2 respectively;

Desired Position, Velocity and Acceleration: These are the position, velocity and acceleration values that the controller commanding the motor has. These properties have similar units to the actual position, velocity and acceleration above;

Position and Velocity Tracking Errors: These are the differences between the respective expected and actual values. These properties have similar units to the actual position, velocity and acceleration above;

Settling Time: This is the time it takes for the automation and/or system to determine that the position and velocity tracking error is within a specified window at the end of the motion;

Encoder Analog and Absolute Position Output: The motor position is determined by the encoder, which outputs two types of signals - analog and absolute position signals. Analog signals are sine and cosine signals in mVolts. The absolute position signal is a non-volatile integer value that indicates the number of analog sine cycles or an integer multiple of analog sine cycles that have elapsed. Typically, the digital output is read at power-on, after which the axis position is only determined by an analog signal;

Gripper State: This is the state of the gripper - open or closed. It is the blocked/unblocked state of one or more sensors in the case of a vacuum-actuated edge contacting the gripper;

Vacuum System Pressure: This is the vacuum level measured by the vacuum sensor. This is an analog sensor whose output is digitized by an analog-to-digital converter. In the case of a suction gripper, the vacuum level indicates whether the wafer is gripped;

Substrate Presence Sensor Status: In passive clamp terminators, the wafer presence sensor output is a binary output. In vacuum-actuated edge-contact clamp terminators, wafer presence is determined from the output states of two or more sensors, each sensor being binary;

Mapper sensor status: this is the status of the mapper sensor - blocked or not blocked in any given case;

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

Base Mapper Sensor Position Capture Data: This is an array of robot axis position values where the mapper sensor changes state;

Vacuum Valve Status: This is the command status of the vacuum valve. It specifies whether the solenoid operating the vacuum valve should be energized;

Voltage at Fuse Output Terminals: Monitors the voltage at each fuse output terminal in the motor control circuit. Low output terminal voltage due to blown fuse;

Substrate Alignment Data: This is the substrate eccentricity vector and angular location of the substrate's alignment datum as reported by the aligner;

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

Substrate Cycle Time: This is the time it takes for an automated device and/or system to process a single substrate by a tool, usually measured under steady flow conditions;

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

Specific examples of continuously monitored variables include:

Table 3: Continuous Monitoring Variables name unit abbreviation Full name of unit T1 position_actual deg Degrees (degrees) T2 position_actual deg Degrees (degrees) Z position_actual m Meters (meters) T1 speed_actual deg/sec Degrees per second (degrees per second) T2 speed_actual deg/sec Degrees per second (degrees per second) Z speed_actual m/sec Meters per second(meter/second) T1 acceleration_actual deg/sec2 Degrees per second squared (degrees per second squared) T2 acceleration_actual deg/sec2 Degrees per second squared (degrees per second squared) Z Acceleration_Actual m/sec2 Meters per second squared (meters per second squared) T1 acceleration command deg/sec2 Degrees per second squared (degrees per second squared) T2 acceleration command deg/sec2 Degrees per second squared (degrees per second squared) Z acceleration command m/sec2 Millimeters per second squared (millimeters per second squared) T1 position error deg Degrees (degrees) T2 position error deg Degrees (degrees) Z position error deg Degrees (degrees) T1 torque_actual N Newton-meters (Newton meters) T2 torque_actual N Newton-meters (Newton meters) Z torque_actual N Newton-meters (Newton meters) T1 torque_modeling N Newton-meters (Newton meters) T2 Torque_Modeling N Newton-meters (Newton meters) Z Torque_Modeling N Newton-meters (Newton meters) T1 Current_Actual A Amps (ampere) T2 Current_Actual A Amps (ampere) Z current_actual A Amps (ampere) busbar motor voltage V Volts (volts) Busbar 24V rail V Volts (volts) Busbar 12V rail V Volts (volts) Busbar 5V Rail V Volts (volts) Busbar 3.3V rail V Volts (volts) core temperature C Celsius FPGA core supply voltage V Volts (volts) FPGA io supply voltage V Volts (volts) Processor Core Supply Voltage V Volts (volts) Processor io supply voltage V Volts (volts) DDR supply voltage V Volts (volts) T1 temperature C Celsius T2 temperature C Celsius Z temperature C Celsius T1 servo state bitwise Bitwise T2 servo status bitwise Bitwise Z servo state bitwise Bitwise T1 encoder CRC error counter count counter T2 Encoder CRC Error Counter count counter Z encoder CRC error counter count counter T1 encoder warning flag counter count counter T2 Encoder Warning Flag Counter count counter Z encoder warning flag counter count counter T1 Encoder Error Flag Counter count counter T2 Encoder Error Flag Counter count counter Z Encoder Error Flag Counter count counter T1 command position deg Degrees (degrees) T2 command position deg Degrees (degrees) Z command position m Meters (meters) Radial command position m Meters (meters) Radial command position arm B m Meters (meters) Theta instruction position deg Degrees (degrees) Theta command position arm B deg Degrees (degrees) radial position error m Meters (meters) Radial position error arm B m Meters (meters) Tangential position error m Meters (meters) Tangential Position Error Arm B m Meters (meters) command compound acceleration g g Command compound acceleration arm B g g Actual Compound Acceleration g g Actual compound acceleration arm B g g CPU core 0 utilization % percentage CPU core 1 utilization percentage % percentage Total System RAM B Bytes available system RAM B Bytes System RAM used B Bytes Percentage of system RAM used % percentage Total disk 0 space B Bytes free disk 0 space B Bytes disk 0 space used B Bytes % disk 0 space used % percentage Processor PCB temperature C Celsius RTC battery low flag Bool Boolean Controller startup cycle times count count Controller hardware working time sky sky total cycle count count count Temperature inside the fan controller C Celsius Fan 0 speed RPM RPM fan 0 error flag Bool Boolean Fan 1 speed RPM RPM Fan 1 Error Flag Bool Boolean

Wherein T1 and T2 are robot manipulator driving rotation axes (there may be more or less than two rotation driving axes); Z is robot driving Z axis; CPU is robot controller (such as controller 319, 323, 422, 423A～ 423C, 800). Fan 0, Fan 1 are the various fans of the robotic manipulator; theta is the rotation of the robotic manipulator arm; and R is the extension of the robotic manipulator arm.

Specific examples of derived variables include:

Table 4: Derived variables name unit abbreviation Full name of unit T1 servo state duration sky sky T2 servo state duration sky sky Z servo state duration sky sky T1 servo state bool Boolean T2 servo state bool Boolean Z servo status bool Boolean T1 mechanical power W Watts T2 mechanical power W Watts Z mechanical power W Watts T1 electric power W Watts T2 electric power W Watts Z electric power W Watts T1 motor efficiency % percentage (percentage) T2 motor efficiency % percentage (percentage) Z motor efficiency % percentage (percentage) T1 incremental energy J Joules T2 incremental energy J Joules Z delta energy J Joules T1 incremental position deg Degrees (degrees) T2 incremental position deg Degrees (degrees) Z incremental position m Meters (meters) Position loop average execution time us Microseconds (microseconds) Position cycle maximum execution time us Microseconds (microseconds) Position cycle average signal delay time us Microseconds (microseconds) Position cycle maximum signal delay time us Microseconds (microseconds) Current Loop Average Execution Time us Microseconds (microseconds) Current loop maximum execution time us Microseconds (microseconds) IO cycle average interval time ms Milliseconds (milliseconds) IO cycle maximum interval time ms Milliseconds (milliseconds) IO loop average execution time ms Milliseconds (milliseconds) IO loop maximum execution time ms Milliseconds (milliseconds) T1 encoder CRC error flag count counter T2 encoder CRC error flag count counter Z encoder CRC error flag count counter T1 encoder warning flag count counter T2 encoder warning flag count counter Z encoder warning flag count counter T1 encoder error flag count counter T2 encoder error flag count counter Z encoder error flag count counter

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 movement basic groups 820, 820A-820C include at least one common basic movement defining the basic movement type (e.g., a movement that forms a baseline and is created from enough sample movements that enough sample movements were collected to define a statistically significant batch) of statistically representative quantities. For example, motion (each) for a respective basic move 501, 502, 503 (e.g., basic move 501 has a basic set of motions 820A, basic move 502 has a basic set of motions 820B, and basic move 503 has a basic set of motions 820C) The base set 820, 820A-820C (see FIG. 8A ) is essentially the minimum number of moves N _min (see FIG. 6 ) (e.g., sample size) sufficient to provide a statistically meaningful standard deviation based on a given convergence criterion, The basic set of motions (or sets of moves) 820 , 820A- 820C are then characterized for a particular robotic manipulator 306 , 311 , 400 . Thus, each dynamic performance variable is specific to and output by a corresponding robotic manipulator 306, 311, 400.

The predetermined basic movements 501, 502, 503 of the corresponding predetermined movement basic groups 820, 820A-820C include a plurality of different basic movement types, wherein each basic movement type is performed by the transport device 306, 311, 400 for each basic movement Type produces the effect of the number of statistical features of common movement. Each different basic motion type has a different corresponding at least one torque command feature and position command feature defining a different common motion associated with each basic motion type. In one aspect, the predetermined set of basic movements 820, 820A-820C may be one or more movement/movement types. For example, each motion 501 , 502 , 503 in elementary motion groups 820 , 820A-820C may be a simple motion or a complex (eg, hybrid) motion characterized by torque and position commands that define the corresponding motion. change.

A simple movement is a straight line 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) along One of the theta, extension, or Z axes of the robotic manipulators 306, 311, 400 (eg, a single degree of freedom of movement).

As shown in Figure 5B, a compound or blended move is one in which more than two simple moves are blended together, and Figure 5B illustrates a move extending from point 0 to point 2 with a blend path adjacent to point 1, which The path blends the two straight line movements from point 0 to point 1 and point 1 to point 2 along at least two (e.g., two or more degrees of freedom of movement).

Each of the basic sets of motion 820, 820A-820C can also be defined by the location of the move within the set (e.g., the start and end points of the move), the load parameters of the move within the set (e.g., the robot manipulator 306, 311, 400 Dynamic conditions (e.g. motion/stop, stop/stop, stop/motion, motion/motion, etc.) . For example, referring to the complex movement in Figure 5B, the dynamic condition at point 0 is stopped and the dynamic condition at point 2 is stopped. Referring to the two simple moves in Figure 5C, the dynamic condition at point 0 is stopped and the dynamic condition at point 1 is moving; and the dynamic condition at point 2 is stopped. As noted above, although movement types are described in terms of one, two, or three degrees of freedom with respect to the motion of the robotic manipulator arm, it should be understood that movement types may include movements produced with any suitable number of degrees of freedom or a single degree of freedom. Movement (e.g. with vacuum pumps, substrate aligners, etc.).

Each move type affects the minimum number of moves _Nmin that statistically characterize each move type. For example, each dynamic performance variable or movement type can be represented historically as:

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

Table 5: Derived signals on elementary movement with scalar output (per motor) Metric system illustrate unit the peak tracking error Spend RMS tracking error Spend the peak torque error Newton-meter RMS torque error Newton-meter the peak electric current ampere RMS electric current ampere the peak Duty cycle as a percentage of available torque percentage the peak mechanical power watt average motor efficiency percentage sum Mechanical energy joule average Winding temperature Celsius sum Vibration power, over configurable Hz range dB the peak vibration power dB/Hz the peak Vibration power crest factor dB/Hz

Table 6: Derived signals on fundamental movement with scalar output (per arm or terminator) Metric system illustrate unit condition the peak Settling time Second Precise and stable the peak Stable radial error rice Precise and stable the peak stable tangential error rice Precise and stable the peak Toggle Composite Error rice Position: R EX Change: Z state the peak tangential tracking error rice Change: R status RMS tangential tracking error rice Change: R status the peak synthetic acceleration g

Table 7: Derived system signals with respect to elementary movement with scalar output Metric system illustrate unit the peak Motor busbar voltage drop volt the peak Accumulated motor mechanical power watt sum Accumulated motor mechanical energy joule

Table 8: Derived signals on basic movement with vector output illustrate unit Position error (per motor) Spend Actual Torque (per motor) Newton-meter Tangential Error (per arm or terminator) rice Radial Error (per arm or terminator) rice

These vector output signals have a signal at each time sample along the trajectory, so the amount of these signals differs between moves, and being one move versus another in a time sample is irrelevant to the evaluation in any physical sense. The basic movement (type) index is denoted by i, and the history for a given index is denoted by j.

The last basic move evaluated in this example is

and in this example the third last basic move evaluated is

Referring to FIGS. 5A-5C , basic movements, such as basic movements 501 , 502 , 503 in a corresponding set of basic movements 820 , 820A- 820C may also be referred to as template movements. The basic moves 501, 502, 503 are repeated moves along a unique path. Basic moves 501, 502, 503 may consist of simple moves or complex moves as described above.

To assess system performance degradation and performance trends, characterization data is analyzed along distinct paths of fundamental movement about the baseline. The basic moves 501, 502, 503 may be defined theoretically and/or empirically. For example, theoretical base movement is based on the desired design configuration and handling of the process tool to account for expected movement in operation, and then is performed at any time before or after in-situ process tool installation.

Empirical base moves can be generated from in-situ process move commands as moves expected to occur in common to yield sufficient statistical properties to have meaningful statistical values settled between predetermined rates of change of convergence bounds as shown in FIG. 6 (where N _min in FIG. 6 is the minimum number of moves (eg, sample size) sufficient to provide a statistically meaningful standard deviation based on a given convergence criterion). The generation of empirical base moves can be a two-part process (similarly applied to the empirical generation of base statistical features). For example, generating an empirical base move may include accessing a home move command histogram 700 (see FIG. status, etc.) to identify the home move, the command mapped to the base move 501, 502, 503 (e.g., the home move matches the base move within a configurable tolerance); The recording table 840 accesses each dynamic performance variable output by the corresponding robotic manipulator 306, 311, 400. The recording system 801R records predetermined operating data reflecting at least one dynamic performance variable output by the robotic manipulator. The performance variables are used to enable the determination of another predetermined set of motions 830 (described in detail below).

The generation of empirical base moves can be performed in near real-time, running in the background and accessing the record table 840 without accessing the controllers 319, 323, 422, 423A, 423B, 423C, 800 and associated two-way communication/data channel. Home move command histogram 700 includes motions commanded by robotic manipulator controllers (e.g., controllers 319, 323, 422, 423A, 423B, 423C, 800) including motions commanded by corresponding robotic manipulators 306. , 311, 400 achieved in-situ process movement. Home move command histogram 700 may be stored in any suitable location such as a robotic manipulator controller (e.g., controller 319, 323, 422, 423A, 423B, 423C, 810) or any other suitable controller of automated material handling platform 300. Recorded in record form 700R (see FIG. 8A ). As described herein, the robotic manipulator controller resolves the mapped motion from the periodically accessed motion histogram 700 in the log table 700R.

For example, still referring to FIG. 8A , motion resolver 800 resolves in-situ process motion commands ( where the in-situ process motions 501', 502', 503' (see Fig. 5A) effected by the transport device are mapped to predetermined elementary motions 501, 502, 503 of a predetermined basic set of motions (described in detail below), each of which defines a corresponding Template motions such that the in situ process motions are mapped onto the respective template motions)) and the robot controllers 319, 323, 422, 423A-423C, 810 are defined with the mapped in situ process motions 501', 502', 503' Another predetermined exercise group (detailed below). For example, in situ process motion 501' maps to base motion 501, in situ process motion 502' maps to base motion 502, and in situ process motion 503' maps to base motion 503. Note that, in a manner similar to that described above, each in-situ process motion 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 One characterizes the in situ process motion in at least one degree of freedom of motion of the robotic manipulator 306 , 311 , 400 .

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

The motion resolver 800 iterates over all in situ process moves 501', 502', 503' to identify those in situ processes that have a required minimum number of moves N _min as determined by, for example, the standard deviation convergence shown in FIG. 6 Move 501', 502', 503'. For example, as described above, in order to create a baseline (eg, establish base moves 501, 502, 503), enough samples must be collected to define statistically significant batches. The number of samples required to create a baseline depends on the physical properties of the variables being analyzed. For example, defining typical (mean and standard deviation) statistics of mechanical work for a given axis of motion of a robotic manipulator 306, 311, 400 takes longer than peak control torque for the same axis performing the same motion. To correct this situation, the size of the baseline is defined based on the statistical analysis of the collected data. For example, the standard deviation can be calculated during the collection of baseline data up to a point at which their values settle within certain limits (as shown in Figure 6). In Figure 6, the standard deviation for a given variable is plotted against sample size. As the sample size increases, the standard deviation tends to converge within certain bounds. Depending on the actual data set, these bounds may be defined a priori or calculated, for example when the rate of change of the graph is below about +/- 10% change; however, any suitable convergence method and/or percentage change may be used .

Still referring to FIGS. 8A , 5 , and 8B , the moves constituting at least the required minimum number of moves N _min (eg, moves used to define a baseline) may be referred to as a predetermined base set of motions 820 . Each elementary movement 501 , 502 , 503 has a corresponding predetermined set of movement elements 820A, 820B, 820C that is unique to that elementary movement 501 , 502 , 503 . Exemplary process flows for determining and updating respective base sets of predetermined movements 820A, 820B, 820C are illustrated in FIGS. 8A and 8B .

Still referring to FIGS. 5 , 8A, and 8B, in one aspect, once the motion resolver 800 identifies and resolves a predetermined set of motion bases 820A, 820B, 820C for the respective base moves 501, 502, 503, the mapping (as described above) The home process move 501', 502', 503' to a respective one of the base moves 501, 502, 503 is included in the relative base set of scheduled motions 820A, 820B, 820C to update the relative base set of scheduled motions 820A , 820B, 820C. In other aspects, the in situ process motion 501 ′, 502 ′, 503 ′ mapped to a predetermined basic set of motion 820A, 820B, 820C of an opposing one of the basic movements 501 , 502 , 503 may form a predetermined basic set of motion 820A. , 820B, 820C is a different group of different exercise type groups. The updated base set of scheduled motions and/or the different set of motion types may be referred to as another set of scheduled motions 830 . As will be described herein, the other predetermined set of motion bases 830A, 830B, 830C for the respective in situ process moves 501 ′, 502 ′, 503 ′ are substantially the same as the motion bases for the respective base moves 501 , 502 , 503 . Groups 820A, 820B, 820C conduct a comparison (as described herein) regarding health assessment and predictive diagnosis of the automated system being monitored, such as robotic manipulator 300 .

As described above, health assessment of, for example, robotic manipulators 306, 311, 400 (or other suitable automated equipment of automated material handling platform 300) is performed by generating basic statistical characteristics (e.g., operating values of a given variable under typical environmental conditions) baseline or statistical representation of behavior) characterized by the robotic manipulator 306, 311 for a set of basic movements 820, 820A, 820B, 820C (see FIG. 8A) of the robotic manipulator 306, 311, 400 , Each dynamic performance variable of 400 output.

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

Baseline metrics are extracted/determined by, for example, computing the probability density function (PDF) of the underlying statistical characteristics, where 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. Also defined in Figure 9 are upper and lower specification limits (USL and LSL, respectively).

The basic statistical features of each dynamic performance variable of the corresponding robotic manipulator 306, 311, 400 (see Figures 2 and 3) are normalized for each different basic movement type (movement type group to basic value), which The base move types characterize for each different base move type/set of move types a nominal/baseline for each dynamic performance variable specific to the corresponding robotic manipulator 306 , 311 , 400 . For example, for each motion of a predetermined base set of motions, a base value (e.g., a throughput index C _pkBase ) characterized by a corresponding probability density function PDF for each dynamic performance variable is determined, wherein the dynamic performance variable is determined by the corresponding The robotic manipulator 306, 311, 400 output of .

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

where σ is the standard deviation and μ is the mean of the samples collected for the corresponding variable. The throughput index _Cpk can be used as a metric to represent the baseline of the corresponding dynamic performance variable because the throughput index _Cpk captures the mean and standard deviation of a population sample 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 standard deviation of measurement for the respective robotic manipulator 306, 311, 400 being measured The function. For example:

Where N can be an integer greater than 3, so that C _pk can be a number greater than 1. As an example, if N=6, then the baseline processing capability 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 data set mean μ of a baseline of +/-6σ to identify upper and lower specification limits USL, LSL such that 99.9% of moving samples is captured (as shown in Figures 9 and 10). In other aspects, the upper and lower specification limits USL, LSL can be configured on a per signal basis when limits are well established, eg peak torque limit, maximum settling time, etc.

In one aspect, refer also to FIG. 2A , for each of the corresponding normalized values C _pkBase(1-n) and other values C _pkOther(1-n) for each relatively distinct unique device App1-Appn is recorded in any suitable controller, such as the controller of a corresponding one of the devices App1-Appn. The normalized value C _pkBase(1-n) uniquely associated with a corresponding one of the different unique devices App1-Appn is compared with the other value C _pkOther(1-n) to obtain a specific value for each different unique device App1-Appn determine the corresponding performance degradation rate (indicated by, for example, the corresponding linear trend model LTM, see FIG. 11 ) on a device basis on a device basis, which will be further described herein describe in detail. For example, each corresponding device App1-Appn has a corresponding linear trend model LTM1-LTMn as shown in FIG. 11 .

Once a baseline metric is established for each measured variable (raw and derived), a batch of in-situ process movements 501'-503' are sampled during operation of the corresponding robotic manipulator 306, 311, 400. For example, in-situ process movements 501', 502', 503' are generated by a controller, e.g., controllers 319, 323, 422, 423A, 423B, 423C, 810, to identify responses to the monitored robotic manipulator 306, 311, 400 is another specific statistical feature. As described above, each dynamic performance variable for a group of in-situ process movements is mapped to a corresponding elementary movement (eg elementary movement type/group of types - see equations 1, 2 and 3). As described above, the mapped home process motions 501', 502', 503' are used to define other predetermined sets of motions 830, 830A-830C of the respective robotic manipulators 306, 311, 400.

As with baseline movements 501-503, for each different home (another) movement type/group of types (e.g., other predetermined movement groups 830, 830A-830C), each of the corresponding robotic manipulators 306, 311, 400 The in situ process movement 501'-503' process (another) statistical characteristics of the dynamic performance variables are mapped to corresponding predetermined motion basis sets 820, 830A-830C and normalized to the in situ (another) value _CpkOther , the home (other) value C _pkOther characterizes each dynamic performance variable of the corresponding robotic manipulator 306, 311, 400 for each of the different home movement types (which may be simple or complex movements) in-situ performance. The in situ (another) value C _pkOther is the processing power index characterized by the probability density function PDF for each dynamic performance variable output by the robotic manipulator 306, 311, 400, the 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) value C _pkOther is referenced to the upper and lower limits USL, LSL of the baseline to position the other predetermined set of motions relative to the base set of predetermined motions, as shown in Figure 10 (wherein the other predetermined set of motions is identified as "New Batch" and the base set of predetermined motions is identified as "Baseline"). C _pkOther is a processing capability index that can be defined as:

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

The comparison between the in-situ (another) value _CpkOther and the base value _CpkBase may be performed by the processor 810P or any other suitable controller of the automated material handling platform 300, wherein the corresponding robotic manipulators 306, 311, 400 is a common carrier for both the base set of scheduled motions 820, 820A-820C and the other set of scheduled motions 830, 830A-830C (and the corresponding home (other) values _CpkOther and base values _CpkBase ). The comparison between the in situ (another) value C _pkOther and the base value C _pkBase is targeted to a particular device (such as a corresponding robotic manipulation) by providing a way to track how much each dynamic performance variable deviates or drifts from its baseline (see FIG. 10 ). 306, 311, 400) to implement 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 100% of the evaluations represent a perfect statistical match between the in situ (another) value C _pkOther and the base value C _pkBase . Equation (10) above represents an example of evaluation for a given dynamic performance variable. In other aspects, other measures of assessment may be used, such as measuring the number of occurrences of upper and lower limits USL and LSL above the baseline. FIG. 10 illustrates an example of health assessment calculation for a given dynamic performance variable based on its statistics. In the example shown in FIG. 10, where 20% of the batch data samples lie outside the baseline range, the in-situ (other) value of _CpkOther puts subjects at a disadvantage.

Still referring to FIG. 10 , and referring also to FIGS. 11 and 12 , the value of each dynamic performance variable of the corresponding robotic manipulator 306, 311, 400 can be defined according to the degree to which the home (another) value _CpkOther deviates from the base value _CpkBase health assessment. The degree of variation may be defined according to prescribed thresholds, such as "warning" and "error", where "warning" may mean "requires attention" and "error" may mean "immediate action required", which will be described below. Another aspect of tracking the in situ (other) value C _pkOther (and the base value C _pkBase ) is that such a track provides trend analysis that can be evaluated when the corresponding dynamic performance variable is expected to reach different levels of variation. or extrapolated.

Determining the amount by which each dynamic performance variable deviates or drifts from its baseline provides trend data TD for each dynamic performance variable, wherein the trend data TD characterizes a deterioration trend of the corresponding dynamic performance variable. Trend data TD may be recorded in any suitable register TDR of the automated material handling platform 300 . Figure 11 depicts an exemplary trend profile graph of an exemplary dynamic performance variable; where the evaluations A1-An of the comparison of the in situ (another) value C _pkOther and the base value C _pkBase at predetermined time points from different batches of samples are drawn plotted on the graph.

The oblique lines in FIG. 11 represent linear trend models LTM, LTM1˜LTMn, which can be obtained in any suitable way, such as by using the least squares method; and in other aspects, any suitable trend model can be used. Characterize, for example, trend 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 at, for example, any suitable controller/processor of the automated material handling platform 300 (such as, for example, the corresponding device controller or tool controller 314 or processing device 810P) record table. In one aspect, the processor 810P combines performance degradation trends corresponding to a transport device (e.g., transport device 306) and each of a plurality of different unique devices App1-Appn of the automated material handling platform 300 to determine a characterized automated A system performance degradation trend for performance degradation of the material processing platform 300 .

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

As seen in FIGS. 11 and 12 , trend data TD may also be provided by, for example, any suitable display 140 to the health Evaluate warnings. For example, any suitable controller of automated materials handling platform 300 (e.g., processor 810P), which may be separate from or included in controllers 319, 323, 422, 423A, 423B, 423C, 810, may include trending/evaluation unit 870 (FIG. 8A), the trending/assessment unit 870 is configured to send a predetermined signal to indicate the health assessment of the robotic manipulator 306, 311, 400 to the operator. In other aspects, trending/evaluation unit 870 may be part of controller 319 , 323 , 422 , 423A, 423B, 423C, 810 . For example, when the trend data TD reaches a first predetermined evaluation value WS, the processor 810P may send or cause a "warning" indication to be displayed visually, for example in yellow, and when the trend data TD reaches a second predetermined evaluation value ES (e.g., below An "error" indication may be rendered in red at the first predetermined evaluation value WS), and a "normal" indication may be rendered in green when the trend profile is above the first predetermined evaluation value WS (e.g., all dynamic performance variables are at predetermined operating within limits). In other aspects, the operational status (eg, normal, warning, and error) of the automated system may be presented audibly, visually, or in any other suitable manner.

In one aspect, the processor 810P aggregates at least one dynamic performance variable output by the transport means with the dynamic performance variable having the highest tendency to deteriorate (e.g., the lowest percentage estimate) and predicts the occurrence of transport means having performance below a predetermined performance state . For example, the overall health of the robotic manipulator 306, 311, 400 can be measured as a worst case estimate of all dynamic performance variables monitored in a given batch of data samples. For example, imagine measuring five dynamic performance variables Var1-Var5 (like, for example, T1 Position_Actual, Z Acceleration_Actual, busbar motor voltage, T2 temperature, and theta commanded position to account for the different variables being compared) and Compare them to their respective baselines, where the resulting evaluation values are:

Table 9: Evaluation Values variable Evaluate Var1 95% Var2 92% Var3 89% Var4 96% Var5 70%

In the above example, the evaluation of the dynamic performance variable Var5 is the lowest evaluation of the five dynamic performance variables Var1-Var5, and can be used to represent the overall current health evaluation of the robotic manipulator 306, 311, 400, the manipulator 306, The health of 311, 400 is 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-Var5, since evaluations can be performed directly on all of these entities based on the fact that these evaluations are relative measurements against their respective baselines Compare.

As an example of the performance variable comparison described above, the processor 810P compares the performance degradation trend of the transportation device 306 to the performance degradation trends of each of a plurality of different unique devices App1-Appn, and determines the performance degradation trend of the transportation device 306 Or whether the performance degradation trend of another one of the plurality of different unique devices App1-Appn is the determining factor of the control performance degradation trend and whether the control performance degradation trend is the performance degradation trend of the system. For example, at time t _s , the linear trend model LTM2 for the robotic manipulator 306 has the lowest assessment that is considered to be with respect to the overall health of the automated material handling platform 300 described in Table 9 . Other linear trend models (such as the linear trend model LTM1) may show a faster rate of performance degradation over time. In this case, for example, the overall health of the automated material handling platform can be judged 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 .

Although the overall health of an automated material handling system can be determined by the linear trend model having the lowest estimated value at any given time, the linear trend model also provides information about which devices App1-Appn are the cause or cause of system errors or warnings. Fingerprint or indication of primary source. For example, the power supply PS may affect the other devices App1-Appn, eg, by not providing sufficient voltage to, eg, 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 degraded performance of the power supply PS. A warning may be generated at time t _warnLTM2 for degraded performance of the robotic manipulator 306; however, the robotic manipulator 306 may function normally without the insufficient voltage supplied to the robotic manipulator 306 by the power supply PS . These two warnings indicate that the power supply PS and the robot manipulator 306 should be checked for repairs, and suggest that there may be some correlation between the performance degradation of the power supply PS and the performance degradation of the robot manipulator 306 .

)，其中S _0,μ是純量值，S _μ+1是向量值，以便產生系統性能歸一化值

並且對於映射的運動產生與μ個裝置的系統獨特地相關的另一個值

。 In another aspect, as shown in FIGS. 5A and 8A , aspects of the disclosed embodiments may provide the health of the system as a combination of aggregated characterization and health predictions. Note that the combination of aggregate characterization and health prediction of a system is different from combining/aggregating the different degradation trends of system components to determine the overall system degradation trend. For example, the combination of aggregated characterization and health prediction can be thought of as analogous to determining the deterioration trend of a system with μ devices, where the system and its multiple devices are considered as a single unique device, while also separately determining the The deterioration tendency of each unique unit of the system. In this aspect, the fundamental movements 501-503 and the in-situ process movements 501'-503' are uniquely associated with respective unique devices, as described above. The basic moves 501-503 and in-situ process motions 501'-503' may be different for each different device of a general type (e.g. the basic moves 501-503 and in-situ process motions 501'-503' of the robotic manipulator 306 The basic movements 501-503 and the in-situ process movements 501'-503' of the robotic manipulator 311 may be different. The basic set of motions 820, 820A-820C and other predetermined sets of motions 830, 830A-830C for a unique system (e.g., automated material handling platform 300) can be determined by a set of basic motions 890 (see FIG. 8A), where the basic motions 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 basic motions is associated with a unique device (such as Table 1 and Table 1 above). 2) that are communicatively linked (eg, power supplies, robotic manipulators, wafer sensors, etc.) to form a single aggregation motion 890AG. A single gathering motion is uniquely associated with a unique system (e.g., automated material handling platform 300) and (of each device operating in a single gathering motion) μ associated combined dynamic performance variables (e.g.,

), where S _{0, μ} is a scalar value, and S _μ+1 is a vector value in order to generate a system performance normalized value

and for the mapped motion yields another value uniquely related to the system of μ devices

.

In one aspect, referring to FIG. 14 , where a component (such as those listed in Tables 1 and 2 ) is replaced in a system (such as the automated material handling platform 300 ), it may be determined by repeating the system health determination. to generate a health measure of the system (Figure 14 block 1400), wherein the repeated system health measure includes (1) the deterioration trend of each element of the repeated system (or at least the device of the replaced element) (such as by the linear trend model LTM, LTM1～ Determination of LTMn) and in conjunction with the degradation trends of the elements to determine the overall system health from one of the controls described in relation to, for example, Table 9 (FIG. 14, block 1401); (2) Determining the aggregated characterization of the aggregated deterioration trend of the new system as described above (FIG. 14, block 1402); (3) identifying whether the replaced element improved or reduced the overall deterioration tendency of the system, and if the new element Once degradation tendency is reduced, components are replaced again, and/or components are mixed and matched to improve overall system degradation tendency (FIG. 14, block 1403).

In one aspect, the importance of the trade-off trend of deterioration (FIG. 15, block 1500) can be applied by any suitable processor of the system (e.g., tool controller 314) to the linear trend models LTM, LTM1˜ LTMn. For example, when applying trade-off importance, the tool controller 314 can determine whether the deterioration trend of any one or more elements is controlling (e.g., maximum deterioration) or otherwise indicated at the expected failure time (FIG. 15, block 1501) The predicted time to failure outside of the predetermined time frame; or any of the more elements can be otherwise identified as the first element predicted to fail, and can be predicted to fail after the first element A range (eg, time range) is defined between the component predicted to be the last to fail (FIG. 15, block 1502). A history of past failures, if any, can also be determined and stored in the system's memory and reviewed by the tool controller 314 to determine which components, if any, tend to be the first to fail ( Figure 15, block 1503). Based on the determinations made above, it may be determined via the tool controller 314 whether the failure frequency of the component is inconsistent with the system (eg, the failure frequency of other components) (FIG. 15, block 1504). The tool controller 314 may also identify component characteristics related to system performance (eg, whether the system is usable or inoperable with a faulty component) (FIG. 15, block 1505). In one aspect, component characteristics related to system performance may be classified as critical (eg, when the system cannot operate without the component) or routine (the system can operate without the component). Component characteristics may include, but are not limited to, the primacy of the component, the difficulty of finding a replacement for the component, the accessibility of the component within the system (whether the component is easily accessible to replace/difficult to access and difficult to replace), the packaging of the component (e.g., Failure of the motors in the robotic manipulator requires replacement of the robotic manipulator, whereas failure of the power supply only requires replacement of the power supply) or other factors that may affect system downtime and/or availability of component replacements.

The trade-off importance given to each component's deterioration tendency may be determined by, for example, the tool controller 314 based on the component's failure frequency and component characteristics related to system performance. The trade-off importance of component degradation trends increases or decreases the impact of component degradation trends on the overall system degradation trend, where the overall system health is assessed based on the weighted importance degradation trends 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 an element that has been in service for a period of time, such that the element that was just replaced/repaired contributes to the judgment of the health of the overall system Less impact than components that have been in service for a longer period of time. In another aspect, the importance of the linear trend models LTM, LTM1~LTMn can be weighed, so that the linear trend models of components known to fail frequently will not affect the judgment of the health status of the entire system, or have only a limited impact . 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. Predetermined operational profiles are recorded using a recording system 801R communicatively coupled to the device controllers 319, 323, 422, 423A-423C, 810 (FIG. 13, block 1300). The predetermined operating profile embodies at least one dynamic performance variable output by the transport device, the predetermined operating profile implementing a predetermined set of motion elements 820, 820A, 820B, 820C of predetermined elementary motions. Base value _CpkBase is determined using, for example, processor 810P communicatively coupled to recording system 801R (FIG. 13, block 1310). The base value C _pkBase is characterized by the probability density function PDF of each dynamic performance variable output by the transport device 306 , 311 , 400 for each motion of the predetermined base set of motions 820 , 820A, 820B, 820C.

The commands for the in-situ process motions 501'-503' are decomposed by, for example, a 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 decomposed in-situ process motion commands and implemented by the transport devices 306, 311, 400 are mapped to the predetermined elementary motions 501-503' of the predetermined motion elementary groups 820, 820A, 820B, 820C. 503. Another predetermined set of motions 830, 830A, 830B, 830C of the transporter are defined together with the mapped in-situ process motions 501'-503' (FIG. 13, block 1330).

Predetermined operational data embodying at least one dynamic performance variable output by the transport device, which implements other predetermined sets of motions, is recorded (FIG. 13, block 1340) by, for example, the recording system 801R. Processor 810P determines another value C _pkOther (FIG. 13, block 1350) characterized by the probability density function PDF for each dynamic performance variable output by the transport device, which implements the other value C _pkOther Mapped in situ process motions 501'-503' of predetermined motion groups 830, 830A-830C.

The other value _CpkOther and the base value _CpkBase are compared by, for example, the processor 810P for each dynamic performance variable output by the conveyance means respectively corresponding to the base set of predetermined movements and the other set of predetermined movements (Fig. 13, block 1360), wherein the transport device is a common transport device for both the base set of predetermined movements and the other set of predetermined movements. The health of the transporter can be assessed based on the comparisons described above, and any appropriate health assessment notifications can be sent to the operators of the automated material handling platform 300 as described above.

According to one or more aspects of the disclosed embodiments, a method for health assessment of a system includes delivering a device:

recording, with a recording system communicatively coupled to the device controller, predetermined operational data embodying at least one dynamic performance variable output by the conveyance device, the predetermined operational data achieving a predetermined elementary set of predetermined elementary movements;

A processor communicatively coupled to the recording system is utilized to determine a base value (C _pkBase ) derived from the probability density function of each dynamic performance variable output by the conveyance device for each motion of the predetermined base set of motions to characterize

decomposing the in-situ process motion commands of the device controller from the carrier device using a motion resolver communicatively coupled to the device controller, wherein the in-situ process motion achieved by the carrier device is mapped to predetermined elementary motions of a predetermined set of motion primitives, and using the mapped in-situ process motion to define another predetermined set of motions for the transport device;

A recording system is used to record predetermined operating profiles embodying at least one dynamic performance variable output by the conveyance device, the predetermined operating profiles effectuate another predetermined set of motions, and a processor is used to determine another value ( _CpkOther ) which Values characterized by the probability density function of each dynamic performance variable output by the conveyor, the other value ( _CpkOther ) effectuating the mapped in-situ process motion of another predetermined set of motions; and

The processor is used to compare the other value with the base value ( _CpkBase ) for each dynamic performance variable output by the conveyance means respectively corresponding to a predetermined base set of movements and another predetermined set of movements for which the conveyance means Both the base set of predetermined movements and the other set of predetermined movements are unique to and common to a transport device, and the health of the transport device is assessed based on this comparison.

According to one or more aspects of the disclosed embodiments, each predetermined elementary motion defines a template motion, and each in-situ process motion maps substantially onto a corresponding one of the template motions.

According to one or more aspects of the disclosed embodiments, each template movement is characterized by at least one of a torque command and a position command from the device controller.

According to one or more aspects of the disclosed embodiments, at least one of the torque command and the position command characterizes template motion in at least one degree of freedom of motion of the transporter.

According to one or more aspects of the disclosed embodiments, the method further includes recording in a log table of the device controller a histogram of motion commanded by the device controller, the histogram of motion including the in-situ process performed by the transport device motion, and wherein the processor resolves the motion from a map of regularly accessed motion histograms located in a record table.

According to one or more aspects of the disclosed embodiments, the predetermined elementary motions of the predetermined elementary group of motions include a statistical characteristic quantity of at least one common elementary motion defining an elementary motion type.

According to one or more aspects of the disclosed embodiments, the predetermined basic motions of the predetermined basic group of motions include a plurality of different basic motion types, wherein each basic motion type is performed by the conveying device for each basic motion type in a common motion. implemented in the number of statistical features.

According to one or more aspects of the disclosed embodiments, each of the different basic motion types has a different corresponding at least one torque command characteristic and position command characteristic that define the Different common motions corresponding to the basic motion types.

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

According to one or more aspects of the disclosed embodiments, the method further includes aggregating, with the processor, the dynamic performance variable having the highest tendency to deteriorate of the at least one dynamic performance variable output by the conveyance device, and predicting a dynamic performance variable with a lower than predetermined performance state The occurrence of an event in the performance of the delivery device.

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

According to one or more aspects of the disclosed embodiments, a method for health assessment of a system including a transport device is provided. The method includes:

Using a recording system communicatively coupled to the device controller, recording predetermined operational profiles embodying at least one dynamic performance variable output by the conveyance device, the predetermined operational data achieving a predetermined motion configured to define a statistical characteristic of the predetermined base motion basic group;

Determining, with a processor communicatively coupled to the recording system, a normalization value that statistically characterizes the value of each dynamic performance variable output by the conveyance device for each motion of a predetermined basic set of motions nominal performance;

decomposing the in-situ process motion commands of the apparatus controller from the conveyance apparatus with a motion resolver communicatively coupled to the apparatus controller, wherein the in-situ process motions effected by the conveyance apparatus are mapped to predetermined elementary motions of a predetermined elementary set of motions, and using the mapped in-situ process motion to define another predetermined set of motions for the transport device;

A recording system is used to record predetermined operating profiles embodying at least one dynamic performance variable output by the conveying device, the predetermined operating profiles effectuate another predetermined set of motions, and a processor is used to determine another normalized value, the other normalized a normalization value statistically characterizing the in-situ process performance for each dynamic performance variable output by the transport device, the other normalization value achieving the mapped in-situ process motion of another predetermined motion set; and

Using the processor to compare another normalized value with the normalized value for each dynamic performance variable of the transport device respectively corresponding to a predetermined basic set of motions and another predetermined set of motions, and based on the comparison from the standard Determining the rate of performance degradation of a conveyance device, where the device is unique, and each normalized value (C _pkBase ) of each predetermined base motion of the predetermined base group of motions and each of the other predetermined motion base groups Every other value of the mapped in situ process motion ( _CpkOther ) is uniquely associated only with that unique device, and the determined performance degradation rate is uniquely related only to that unique device.

According to one or more aspects of the disclosed embodiments, the method further includes providing the system with a plurality of different unique devices and delivery devices connected to each other, wherein each different unique device from the plurality of different unique devices (i) The unique device has a different corresponding normalization value ( _CpkBasei ) for each base motion of a predetermined set of base motions and another normalization value for each mapped in situ process motion of another predetermined set of base motions (C _pkOtheri ), the normalized value (C _pkBasei ) and the other normalized value (C _pkOtheri ) are at most uniquely related to a different corresponding unique device (i) from the plurality of different unique devices couplet.

According to one or more aspects of the disclosed embodiments, the method further includes recording, for each different unique device (i) to a controller respectively coupled to the different corresponding unique device, the corresponding normalized value (C _pkBasei ) and other normalized value (C _pkOtheri ), the corresponding normalized value (C _pkBasei ) and the other normalized value (C _pkOtheri ) are unique to the different corresponding unique means (i) and for each different unique device (i), on a device-by-device (i = 1...n) basis, from the uniquely correlated normalized value (C _pkBasei ) and the different unique device (i ) _to determine the corresponding performance degradation rate for the different unique device (i).

According to one or more aspects of the disclosed embodiments, each different unique device and transport device from the plurality of different unique devices has a common configuration.

According to one or more aspects of the disclosed embodiments, each different 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 log in the controller the data that characterizes the performance degradation trends for each of the plurality of different unique devices of the transport device and system. trend data.

According to one or more aspects of the disclosed embodiments, the method further includes incorporating, with a processor, the performance degradation trends corresponding to each of the plurality of different unique devices of the transport device and the system to determine the System performance deterioration trend of system performance deterioration.

According to one or more aspects of the disclosed embodiments, the method further includes comparing, with the processor, the performance degradation trend of the transport device to the performance degradation trend of each of the plurality of different unique devices, and using the processing to determine whether the degradation trend of the transport device or another of the plurality of different unique devices is the control degradation trend and whether the control degradation trend is determinative of the system degradation trend.

According to one or more aspects of the disclosed embodiments, each predetermined elementary motion defines a template motion, and each in-situ process motion maps substantially onto a corresponding one of the template motions.

According to 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 the device controller.

According to one or more aspects of the disclosed embodiments, the at least one torque command and position command characterize template motion in at least one degree of freedom of motion of the transport device.

According to one or more aspects of the disclosed embodiments, the method further includes recording in a log table of the device controller a histogram of motion commanded by the device controller, the histogram of motion including the in-situ process performed by the transport device motion, and wherein the processor resolves the motion from a map of regularly accessed motion histograms located in a record table.

According to one or more aspects of the disclosed embodiments, the predetermined elementary motions of the predetermined elementary group of motions include a statistical characteristic quantity of at least one common elementary motion defining an elementary motion type.

According to one or more aspects of the disclosed embodiments, the predetermined basic motions of the predetermined basic group of motions include a plurality of different basic motion types, wherein each basic motion type is performed by the conveying device for each basic motion type in a common motion. implemented in the number of statistical features.

According to one or more aspects of the disclosed embodiments, each of the different basic motion types has a different corresponding at least one torque command characteristic and position command characteristic that define the Different common motions corresponding to the basic motion types.

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

According to one or more aspects of the disclosed embodiments, the method further includes aggregating, with the processor, the dynamic performance variable having the highest tendency to deteriorate of the at least one dynamic performance variable output by the conveyance device, and predicting a dynamic performance variable with a lower than predetermined performance state The occurrence of an event in the performance of the delivery device.

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

According to one or more aspects of the disclosed embodiments, a health assessment device for assessing the health of a system including a transport device includes:

a recording system communicatively coupled to the transporter controller of the transporter, the recording system being configured to:

recording predetermined operational profiles embodying at least one dynamic performance variable output by the conveyance device, the predetermined operational profiles implementing a predetermined set of motion primitives of predetermined primitive motions, and

recording predetermined operational profiles embodying at least one dynamic performance variable output by the transport device, the predetermined operational profiles effectuating another predetermined set of motions; and

a motion resolver communicatively coupled to the carriage controller, the motion resolver configured to:

decomposing in situ process motion commands of the plant controller from the transport device, wherein the in situ process motion achieved by the transport device is mapped to the predetermined elementary motions of the predetermined elementary set of motions, and

using the mapped in-situ process motion to define another predetermined set of motions for the transport device; and

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

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

Determining another value (C _pkOther ) characterized by the probability density function of each dynamic performance variable output by the conveyance device, which realizes the mapped in situ process of another predetermined set of motions exercise; and,

comparing the other value with the base value ( _CpkBase ) for each dynamic performance variable output by the conveyance means respectively corresponding to a predetermined base set of movements and another predetermined set of movements, and

assessing the health of the vehicle based on the comparison;

Wherein the transport means is a common transport means of both the basic set of predetermined movements and the other set of predetermined movements.

According to one or more aspects of the disclosed embodiments, each predetermined elementary motion defines a template motion, and each in-situ process motion maps substantially onto a corresponding one of the template motions.

According to one or more aspects of the disclosed embodiments, each template movement is characterized by at least one of a torque command and a position command from the device controller.

According to one or more aspects of the disclosed embodiments, the at least one torque command and position command characterize template motion in at least one degree of freedom of motion of the transport device.

In accordance with one or more aspects of the disclosed embodiments, the transporter controller includes a logging table configured to record a movement histogram commanded by the transporter controller, the movement histogram including In situ process motion is achieved, and the processor is further configured to resolve the motion from the regularly accessed motion histogram map located in the record table.

According to one or more aspects of the disclosed embodiments, the predetermined elementary motions of the predetermined elementary group of motions include a statistical characteristic quantity of at least one common elementary motion defining an elementary motion type.

According to one or more aspects of the disclosed embodiments, the predetermined basic motions of the predetermined basic group of motions include a plurality of different basic motion types, wherein each basic motion type is performed by the conveying device for each basic motion type in a common motion. implemented in the number of statistical features.

According to one or more aspects of the disclosed embodiments, each of the different basic motion types has a different corresponding at least one torque command characteristic and position command characteristic that define the Different common motions corresponding to the basic motion types.

According to 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 deterioration trend of the corresponding dynamic performance variable.

According to one or more aspects of the disclosed embodiments, the processor is further configured to aggregate the dynamic performance variable with the highest tendency to deteriorate of the at least one dynamic performance variable output by the conveyance device, and predict a dynamic performance variable with a lower than predetermined Performance State The occurrence of an event for the performance of the transport device.

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

According to one or more aspects of the disclosed embodiments, a health assessment device for assessing the health of a system including a transport device includes:

a recording system communicatively coupled to the transporter controller of the transporter, the recording system being configured to:

recording predetermined operating profiles embodying at least one dynamic performance variable output by a conveyance device that implements a predetermined set of motion bases configured to define statistical characteristics of the predetermined base motions, and

recording predetermined operational profiles embodying at least one dynamic performance variable output by the conveyance device, the predetermined operational profiles effectuating another predetermined set of motions;

a motion resolver communicatively coupled to the carriage controller, the motion resolver configured to:

decomposing in situ process motion commands of the plant controller from the transport device, wherein the in situ process motion achieved by the transport device is mapped to the predetermined elementary motions of the predetermined elementary set of motions, and

using the mapped in-situ process motion to define another predetermined set of motion for the transport device; and

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 conveyance device for each motion of the predetermined base set of motions,

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

comparing the further normalized value with the normalized value for each dynamic performance variable of the transport device respectively corresponding to the predetermined basic set of motions and the other predetermined set of motions, and

Based on this comparison, determine the rate of performance degradation of the delivery device from the nominal performance;

Wherein the transport means is a common transport means of both the predetermined basic set of motions and the further predetermined set of motions.

According to one or more aspects of the disclosed embodiments, each predetermined elementary motion defines a template motion, and each in-situ process motion maps substantially onto a corresponding one of the template motions.

According to one or more aspects of the disclosed embodiments, each template movement is characterized by at least one of a torque command and a position command from the device controller.

According to one or more aspects of the disclosed embodiments, the at least one torque command and position command characterize template motion in at least one degree of freedom of motion of the transport device.

In accordance with one or more aspects of the disclosed embodiments, the transporter controller includes a logging table configured to record a movement histogram commanded by the transporter controller, the movement histogram including In situ process motion is achieved, and the processor is further configured to resolve the motion from the regularly accessed motion histogram map located in the record table.

According to one or more aspects of the disclosed embodiments, the predetermined elementary motions of the predetermined elementary group of motions include a statistical characteristic quantity of at least one common elementary motion defining an elementary motion type.

According to one or more aspects of the disclosed embodiments, the predetermined basic motions of the predetermined basic group of motions include a plurality of different basic motion types, wherein each basic motion type is performed by the conveying device for each basic motion type in a common motion. implemented in the number of statistical features.

According to one or more aspects of the disclosed embodiments, each of the different basic motion types has a different corresponding at least one torque command characteristic and position command characteristic that define the Different common motions corresponding to the basic motion types.

According to 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 deterioration trend of the corresponding dynamic performance variable.

According to one or more aspects of the disclosed embodiments, the processor is further configured to aggregate the dynamic performance variable with the highest tendency to deteriorate of the at least one dynamic performance variable output by the conveyance device, and predict a dynamic performance variable with a lower than predetermined Performance State The occurrence of an event for the performance of the transport device.

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

It should be understood that the foregoing description is only an illustration of various aspects of the disclosed embodiments. Various alternatives and modifications can be devised by those skilled in the art without departing from the aspects of the disclosed embodiments. Accordingly, aspects of the disclosed embodiments are intended to embrace all such alternatives, modifications and variations that fall within the scope of the appended claims. Furthermore, the mere fact that different features are recited in mutually different dependent or independent claims does not indicate that a combination of these features cannot be used to advantage, and such combinations still lie within the scope of the present 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 Handling Platform 301: Atmospheric Section 302: Vacuum Section 303: Processing Module 304: Housing 305: Loading Port 306: Atmospheric Robotic Manipulator 307: Substrate Pair Calibrator 308: Fan filter unit 309: Vacuum chamber 310: Load lock 311: Vacuum robot manipulator 312: Vacuum pump 313: Slit valve 314: Tool controller 315: Atmospheric section controller 316: Vacuum section controller 317: Process Controller 318: Loadport 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 Box Holder 402: Mounting flange 403: Vertical guide rail 404: Linear bearing 405: Carrier 406: Vertical drive motor 407: Ball screw 408: Upper motor 409: Lower motor 410: Encoder 1 411: Encoder 2 412: Outer shaft 413: Inner Shaft 414: First Link 415: Belt Drive Rod 2 416: Second Link 417A: Motor A 417B: Motor B 418A: Belt Driven First Stage A 418B: Belt Driven First Stage B 419A: Belt Driven Second Stage A 419B: Belt Drive Second Stage B 420A: Upper Terminator 420B: Lower Terminator 421A, 421B: Payload for Terminator A and B 422: Master Controller 423A, 423B, 423C: Motor Control 424A, 424B: Electronic unit 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 sense Detector 433, 434A, 434B: lip seal 435: brake 501: base move 1 502: base move 2 503: base move 3 501', 502', 503': in-situ process move STN1-STN6: substrate holding station 700: Original position movement command histogram 700R: recording table 800: motion resolver 801: data buffer 801R: recording system 810: robot controller 810P: processor 820,820A, 820B, 820C: motion basic group 830,830A, 830B, 830C: Scheduled exercise group 840: record table B10, B20, B50, 1300, 1310, 1320, 1330, 1340, 1350, 1360, 1400, 1401, 1402, 1403, 1500, 1501, 1502, 1503, 1504, 1505: block 870: Trend/Evaluation Unit 890: Basic Motion Group 890AG: Individual Aggregated Motion TDR: Registers 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 predetermined evaluation value TD: trend data ES: second predetermined evaluation value t _warn , t _warnLTM1 , t _warnLTM2 , t _errorLTM1 , t _error : time

The aforementioned aspects and other features of the disclosed embodiments are explained in the following description with reference to the accompanying drawings, wherein:

[ FIG. 1 ] is a schematic diagram of a controller for an automated device, such as an automated material handling platform according to aspects of the disclosed embodiments;

[ FIG. 2 ] is a schematic diagram of an automated material processing platform according to aspects of the disclosed embodiments;

[ FIG. 2A ] is a schematic diagram of a system including a number of different unique devices according to aspects of the disclosed embodiments;

[ FIG. 3 ] is a schematic diagram of an automated material handling device (such as a delivery robot) according to aspects of the disclosed embodiments;

[FIGS. 4A-4E] are schematic diagrams of different arm configurations of the device of FIG. 3 according to aspects of the disclosed embodiments;

[ FIG. 5A ] is a schematic diagram of a portion of an automated material handling platform showing basic movement and in-situ process movement, according to aspects of the disclosed embodiments;

[FIGS. 5B and 5C] are schematic illustrations of simple and complex movements according to aspects of the disclosed embodiments;

[ FIG. 6 ] is an exemplary graph illustrating statistical convergence of moving samples performed by the apparatus of FIG. 3 according to aspects of the disclosed embodiments;

[ FIG. 7 ] is an exemplary movement histogram according to aspects of the disclosed embodiments;

[ FIG. 8A ] is a schematic diagram of an exemplary process flow according to aspects of the disclosed embodiments;

[ FIG. 8B ] is a schematic diagram of a portion of the exemplary process flow of FIG. 8A according to aspects of the disclosed embodiments;

[ FIG. 9 ] is an exemplary Gaussian distribution of moving samples indicating upper and lower bounds according to aspects of the disclosed embodiments;

[ FIG. 10 ] is an illustration of a comparison between a baseline value and another value resulting from an in-situ process move in accordance with aspects of the disclosed embodiments;

[ FIG. 11 ] is an exemplary illustration of the application of health assessment of the device of FIG. 3 with respect to predictive diagnosis according to aspects of the disclosed embodiments;

[ FIG. 12 ] is an exemplary illustration of a health assessment indication according to aspects of the disclosed embodiments;

[ FIG. 13 ] is an exemplary flowchart according to aspects of the disclosed embodiments;

[ FIG. 14 ] is a flowchart according to aspects of the disclosed embodiments; and

[ FIG. 15 ] is a flowchart according to aspects of the disclosed embodiments.

830, 830A, 830B, 830C: scheduled exercise groups

870:Trend/Evaluation Unit

890: Basic Exercise Group

890AG: Single Gathering Movement

TDR: scratchpad

700: Histogram of in-situ move command

700R: record sheet

800: motion resolver

801: data buffer

801R: Recording System

810:Robot Controller

810P: Processor

820, 820A, 820B, 820C: Basic group of sports

840: record sheet

Claims (22)

A method of health assessment for a system including a delivery device, the method comprising: recording predetermined operational data embodying at least one dynamic performance variable output by at least one device component of the conveyance device, the predetermined operational data achieving a predetermined base torque command for the conveyance device, utilizing a recording system communicatively coupled to the device controller The basic set of predetermined command torques; Determining, with a processor communicatively coupled to the recording system, a base value that is a probability density for each of the dynamic performance variables output by the at least one device component for each torque command of the base set of predetermined commanded torques function to characterize; Using a resolver communicatively coupled to the plant controller, decomposing the plant controller's in-situ process torque command from the at least one plant component, wherein the in-situ process torque achieved by the at least one plant component is mapped to the the predetermined base torque commands of a base set of predetermined commanded torques, and using the mapped in-situ process torque to define another set of predetermined commanded torques for the conveyance device corresponding to the conveyance device of the base set of predetermined commanded torques; and recording with the recording system predetermined operating data embodying the at least one dynamic performance variable output by the at least one plant component, the predetermined operating data achieving another predetermined set of commanded torques, and using the processor to determine another value, The other value is characterized by the probability density function of each of the dynamic performance variables output by the at least one plant component, the other value effectuating the mapped in situ process of the other set of predetermined commanded torques Torque to compare the other value with the base value and assess the health of the transport device based on the comparison. The method as claimed in claim 1, further comprising using the processor to target each of the dynamic performance variables output by the at least one device component respectively corresponding to the predetermined command torque basic set and the other predetermined command torque set Instead, the other value is compared with the basic value. The method of claim 1, wherein the transport device is the only transport device for both the base set of predetermined commanded torques and the other set of predetermined commanded torques. The method of claim 1, wherein each of the predetermined base command torques defines a template command torque, and each in-situ process torque is mapped to a corresponding one of the template command torques. The method of claim 1, further comprising recording in a log table of the plant controller a torque histogram commanded by the plant controller, the torque histogram comprising in-situ process torque achieved by the at least one plant component, And wherein, the processor resolves the mapped in-situ process torque from the regularly accessed torque histogram located in the record table. The method of claim 1, wherein the predetermined basic command torques of the predetermined basic set of command torques include a statistical characteristic quantity of at least one common basic command torque defining a basic command torque type. The method of claim 1, wherein the predetermined basic command torque of the predetermined basic set of command torques includes a plurality of different basic command torque types, each of the basic command torque types is determined by the at least one device component for each basic The commanded torque type is realized in the statistical characteristic quantity of the common commanded torque. The method of claim 7, wherein each of the different basic command torque types has a different corresponding at least one torque command characteristic and position command characteristic, the torque command characteristic and the position command characteristic define the The common command torque differs according to the torque type. The method according to claim 1, further comprising using the recording system to record respective trend data of the dynamic performance variables, wherein the trend data characterizes the deterioration trend of the corresponding dynamic performance variables. The method of claim 9, further comprising utilizing the processor to aggregate the at least one dynamic performance variable output by the at least one device component with the highest dynamic performance variable of the deterioration tendency, and predicting a dynamic performance variable with a lower than predetermined performance state The occurrence of an event of the performance of the transport device. The method of claim 10, further comprising providing, with the processor based on the aggregation of the dynamic performance variables, a prediction of the occurrence of the event of the transport unit having performance below a predetermined performance state to an operator of the transport unit instructions. 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 transporter controller of the transporter, the recording system being configured to: recording predetermined operational data embodying at least one dynamic performance variable output by at least one device component of the conveyance device, the predetermined operational data implementing a predetermined commanded torque base set of predetermined base torque commands for the conveyance device, and recording predetermined operating data embodying at least one dynamic performance variable output by the at least one plant component, the predetermined operating data achieving another predetermined set of commanded torques; and a resolver communicatively coupled to the transporter controller, the resolver configured to: decomposing the plant controller's in-situ process torque command from the at least one plant component, wherein the in-situ process torque achieved by the at least one plant component maps to the predetermined base torque commands of the predetermined base set of torque commands, and defining another predetermined set of commanded torques for the conveyance device using the mapped in-situ process torque, corresponding to the conveyance device of the base set of predetermined commanded torques; and a processor, communicatively coupled to the recording system, the processor configured to: determining a base value characterized by a respective probability density function of the dynamic performance variable output by the at least one plant component for each torque command of the base set of predetermined commanded torques, and determining another value characterized by the probability density function of each of the dynamic performance variables output by the at least one device component, the other value implementing the mapping of the other set of predetermined commanded torques An in-situ process torque is used to compare the other value to the base value and to assess the health of the transport device based on the comparison. The device according to claim 12, wherein the processor is further configured to respond to each of the dynamic performance variables output by the at least one device component respectively corresponding to the basic set of predetermined command torques and the other set of predetermined command torques One to compare the other value with the base value. The apparatus of claim 12, wherein the transport device is the only transport device for both the base set of predetermined commanded torques and the other set of predetermined commanded torques. The apparatus of claim 12, wherein each of the predetermined base command torques defines a template command torque, and each in-situ process torque is mapped to a corresponding one of the template command torques. The device of claim 12, wherein the conveyance device controller includes a logging table configured to record a torque histogram commanded by the device controller, the device controller including a torque histogram implemented by the at least one device component and the processor is further configured to decompose the mapped in-situ process torque from the regularly accessed torque histogram located in the record table. The apparatus of claim 12, wherein the predetermined base command torques of the base set of predetermined command torques include a statistical characteristic quantity of at least one common base command torque defining a base command torque type. The apparatus of claim 12, wherein the predetermined base command torques of the base set of predetermined command torques comprise a plurality of different base command torque types, wherein each of the base command torque types is controlled by the at least one device component This is implemented in the statistical characteristic quantities of common command torques for each basic command torque type. The apparatus of claim 18, wherein each of the different basic motion types has a different corresponding at least one torque command characteristic and position command characteristic, the torque command characteristic and the position command characteristic define the relationship between the respective basic command torque The common command torque differs according to the type. The apparatus of claim 12, 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 device of claim 20, wherein the processor is further configured to aggregate the dynamic performance variable with the highest deterioration tendency of the at least one dynamic performance variable output by the at least one device component, and predict a dynamic performance variable with a value lower than a predetermined Performance State The occurrence of an event for the transporter's performance. The apparatus of claim 21, wherein the processor is further configured to provide an operator of the transportation device with the occurrence of the event regarding the transportation device having performance below a predetermined performance state based on the aggregation of the dynamic performance variables indication of the forecast.

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