TWI838606B - Information processing device, detection method, program, substrate processing system and manufacturing method of article - Google Patents

Abstract

An information processing device for detecting abnormalities in a control system having a plurality of sensors and a plurality of control units, comprising: a calculation unit for calculating an abnormality degree representing the degree of abnormality of the output value of the sensor or the control data of the control unit using a model representing the relationship between the output values of two sensors among the plurality of sensors, the relationship between the control data of two control units among the plurality of control units, or the relationship between the output value of one sensor among the plurality of sensors and the control data of one control unit among the plurality of control units, according to the plurality of sensors or the plurality of control units being divided into at least two groups; and a determination unit for determining the abnormalities of the plurality of sensors or the plurality of control units according to the group based on the abnormality degree calculated by the calculation unit.

Description

Information processing device, detection method, program, substrate processing system and manufacturing method of article

The present invention relates to an information processing device, a detection method, a program, a substrate processing system and a method for manufacturing an article.

There is a growing demand for improved productivity in substrate processing equipment that processes substrates for manufacturing semiconductor devices, MEMS, flat panel displays, and the like. To this end, it is necessary to suppress production interruptions caused by sudden abnormalities in the substrate processing equipment. Therefore, it is now necessary to detect abnormalities in the substrate processing equipment in advance and eliminate the causes of the abnormalities.

Japanese Patent Publication No. 2017-21702 discloses a method for monitoring the failure signs of a plurality of machines arranged in a factory. In Japanese Patent Publication No. 2017-21702, in order to monitor the failure signs of a plurality of machines, a model representing the relationship between the output values of the sensors measuring the behavior of each machine is constructed based on the output values of the sensors. Furthermore, based on the difference between the output values of the sensors and the predicted data calculated using the model, the change of the invariant relationship (invariant) between the output values of the sensors is detected, and the failure signs of each machine are detected.

When detecting an abnormality in a control system having a plurality of sensors and a plurality of control units, it is possible that an abnormality is detected in the output value of the sensor related to the control unit where no abnormality occurs or the control data of the control unit. For example, in the case of a temperature control system in which the temperature is adjusted by circulating a refrigerant in a pipe, it is possible that an abnormality is detected in the output value of the sensor related to the control unit where no abnormality occurs or the control data of the control unit. For example, in the case of a temperature control system in which the temperature is adjusted by circulating a refrigerant in a pipe, it is possible that an abnormality is detected in the output value of the sensor related to the control unit where no abnormality occurs or the control data of the control unit, which is possible because the temperature of the refrigerant circulating in the pipe changes.

The purpose of the present invention is to provide a technology that is advantageous for detecting abnormalities in a control system having a plurality of sensors and a plurality of control units. [Technical means for solving the problem]

As an aspect of the present invention, an information processing device is a device for detecting abnormalities in a control system having a plurality of sensors and a plurality of control units, and comprises: a calculation unit, which uses a model representing the relationship between output values of two sensors among a plurality of sensors, the relationship between control data of two control units among a plurality of control units, or the relationship between output values of one sensor among a plurality of sensors and control data of one control unit among a plurality of control units, to calculate an abnormality degree representing the degree of abnormality of the output value of the sensor or the control data of the control unit, for each group in which the plurality of sensors or the plurality of control units are divided into at least two; and a determination unit, which determines the abnormality of the plurality of sensors or the plurality of control units for each group based on the abnormality degree calculated by the calculation unit. Other features of the present invention will become apparent from the following embodiments (with reference to the drawings).

In the following, the preferred embodiments of the present invention are described in detail with reference to the drawings. In each drawing, the same components are marked with the same reference symbols, and repeated descriptions are omitted.

<First embodiment> Figure 1 is a diagram illustrating the structure of a substrate processing system. The substrate processing system 1 (product manufacturing system) may include a plurality of substrate processing devices 10 that process substrates respectively, and a host computer 11 that controls the operation of the plurality of substrate processing devices 10. The plurality of substrate processing devices 10 may include, for example, a photolithography device (exposure device, imprinting device, charged particle beam drawing device, etc.). In addition, the plurality of substrate processing devices 10 may include any one of a coating device, a developing device, a film forming device (CVD device, etc.), a processing device (laser processing device, etc.), and an inspection device (overlay inspection device, etc.). Here, the exposure device exposes the photoresist supplied to the substrate via the original plate (reduction mask, mask) to form a latent image corresponding to the pattern of the original plate on the photoresist. In addition, the imprinting device hardens the imprinting material in a state where the mold (original plate) is in contact with the imprinting material supplied to the substrate, thereby forming a pattern on the substrate. In addition, the charged particle beam drawing device draws a pattern on the photoresist supplied to the substrate through the charged particle beam, thereby forming a latent image on the photoresist. In addition, the coating device performs a coating process of an anti-etching material (adhesive material) on the substrate as a pre-processing of the photolithography process. In addition, the developing device performs a developing process as a post-processing of the photolithography process. In addition, the film-forming device is a device that forms a film such as an insulating film on the substrate. In addition, the processing device processes the pattern formed on the substrate, cuts the substrate, opens holes, etc. In addition, the inspection device inspects the position accuracy, line width, etc. of the pattern formed on the substrate.

FIG. 2 is a diagram illustrating the structure of the management device 12. The management device 12 can be implemented by a computer (information processing device) that is communicatively connected to each substrate processing device 10. In FIG. 2(a), CPU 201 (processing unit) is a central processing unit (CPU) that executes an OS (Operating System) and various application programs. In addition, CPU 201 is not limited to a central processing unit (CPU), but may also be a processor or circuit such as a microprocessing unit (MPU), a graphics processing unit (GPU), or an application-specific integrated circuit (ASIC). In addition, CPU 201 may also be a combination of any of these processors or circuits. ROM 202 is a memory that stores fixed data such as programs executed by CPU 201 and parameters used for calculations. RAM203 is a memory that provides a working area for CPU201 and a temporary storage area for data. ROM202 and RAM203 are connected to CPU201 via bus 208. 205 is an input device (input unit) including a mouse, keyboard, etc., and 206 is a display device (display unit) such as a CRT, liquid crystal display, etc. In addition, the input device 205 and the display device 206 can also be an integrated device such as a touch panel. In addition, the input device 205 and the display device 206 can also be configured as a device with a different form from the computer. 204 is a storage device such as a hardware device, CD, DVD, memory card, etc., which stores various programs, various data, etc. The input device 205, the display device 206, and the memory device 204 are connected to the bus 208 via interfaces not shown. In addition, the communication device 207 connected to the network for communication is also connected to the bus 208. The communication device 207 is connected to, for example, a LAN and performs data communication according to a communication protocol such as TCP/IP, and is used when communicating with other communication devices. The communication device 207 functions as a data sending unit and a receiving unit. For example, it receives data such as action information from a sending unit (not shown) in the substrate processing device 10 and stores it in the memory device 204. In addition, FIG. 2(b) is a diagram showing the structure of the CPU 201. The CPU 201 includes an acquisition unit 211 , a generation unit 212 , a calculation unit 213 , and a determination unit 214 .

The schematic structure of the management device 12 is described above with reference to FIG. 2 , and the host computer 11 and the substrate processing device 10 may also include the same computer.

Each of the plurality of substrate processing devices 10 in the substrate processing system 1 is connected to a management device 12 for managing maintenance. In addition, as shown in FIG1 , the article manufacturing system may include a plurality of substrate processing systems 1. Therefore, the management device 12 can manage each substrate processing device 10 in the plurality of substrate processing systems 1. The management device 12 collects and analyzes individual action information of the plurality of substrate processing devices 10, detects abnormalities or their precursors for each substrate processing device 10, and can function as a maintenance determination device for determining whether maintenance processing (maintenance processing) is necessary. In addition, in FIG1 , the connection between the plurality of substrate processing devices 10 and the host computer 11, and the connection between the plurality of substrate processing devices 10 and the management device 12 can be either a wired connection or a wireless connection.

In the following, in order to provide a specific example, an example in which the substrate processing device 10 is configured as an exposure device 10 is described. FIG3 is a diagram showing the configuration of the exposure device and the host computer. The exposure device 10, as shown in FIG3, may include a light source unit 101, an illumination system 102, a mask stage 104, a projection optical system 105, a wafer stage 106, a wafer clamp 107, a pre-alignment unit 109, and a control unit 111.

The light emitted from the light source unit 101 is used to illuminate the mask 103 held on the mask stage 104 via the illumination system 102. The light source of the light source unit 101 includes, for example, a high-pressure mercury lamp, an excimer laser, etc. In addition, when the light source is an excimer laser, the light source unit 101 is not limited to being inside the chamber of the exposure device 10, but may also be an external structure. The pattern to be transferred is drawn on the mask 103. The light illuminating the mask 103 reaches the wafer 108 via the projection optical system 105. The wafer 108 is, for example, a silicon wafer, a glass plate, a film substrate, etc.

The pattern on the mask 103 is transferred to the photosensitive medium (for example, an anti-etching layer) coated on the wafer 108 via the projection optical system 105. The wafer 108 is held by the wafer clamp 107 in a flat state corrected by means of vacuum adsorption or the like. In addition, the wafer clamp 107 is held by the wafer stage 106. The wafer stage 106 is configured to be movable. Furthermore, while the wafer stage 106 is stepped in two dimensions along a plane perpendicular to the optical axis of the projection optical system 105, the wafer 108 is repeatedly exposed to a plurality of irradiation areas. This is an exposure method called a step-and-repeat method. In addition, there is also an exposure method called a step-and-scan method in which exposure is performed by scanning while synchronizing the mask stage 104 and the wafer stage 106 . The present embodiment can also be applied to an exposure device using such a method.

In the exposure device 10, the wafer 108 before exposure processing is set in the exposure device in a state of being placed in the wafer box 110. At least one wafer 108 is stored in the wafer box 110, and generally a plurality of wafers 108 are stored. In addition, one wafer 108 is taken out from the wafer box 110 by a robot arm (not shown) and placed in the pre-alignment unit 109. After the pre-alignment unit 109 performs orientation alignment and position alignment of the wafer 108, the wafer 108 is set in the wafer clamp 107 by the robot arm and is exposed. The wafer 108 that has completed the exposure process is removed from the wafer clamp 107 by the robot arm and recovered to the wafer box 110, and at the same time, the next wafer 108 waiting in the pre-alignment unit 109 is set in the wafer clamp 107. In this way, the wafer 108 is successively exposed. In addition, the exposure device 10 may be connected in series with other devices such as a coating device (not shown) and a developing device (not shown), and the wafer 108 before exposure is carried in from the other devices, and the wafer 108 after exposure is carried out from the other devices.

The control unit 111 is an information processing device such as a computer, and performs various operations on the various units and machines of the exposure device 10. In addition, although the control unit 111 is a single structure in the example of FIG. 3 , the control unit 111 may be not limited to one, and the units and machines of the exposure device 10 may have a plurality of control units 111.

The host computer 11 is an information processing device connected to the exposure device 10 via a network or the like, and monitors and controls the exposure device 10. In addition, the host computer 11 is also connected to devices other than the exposure device 10, and similarly monitors and controls other manufacturing devices, etc. For example, the host computer 11 performs a task of instructing the exposure device 10 to act.

FIG4 is a diagram illustrating the configuration of the temperature control system incorporated in the exposure device 10. In FIG4 , the thick arrow 42 indicates the direction of the refrigerant circulation, and the thin arrow 43 indicates the direction of the control information transmission. The temperature control system (control system) 301 may include, for example, a first block 40 and a second block 41. The first block 40 and the second block 41 may be, for example, regarded as chambers in the exposure device 10. In addition, the number of blocks is not limited to 2, and the blocks may be divided into 1 or more units. In this case, if it is difficult to set up a chamber according to 1 or more units, a container for storing 1 or more units may be used.

In the first block 40, the refrigerant can be temperature-controlled and the temperature-controlled refrigerant can be supplied to the second block 41. In addition, a plurality of target units 416-419 can be arranged in the second block 41. The plurality of target units 416-419 can include, for example, the light source unit 101, the illumination system 102, the mask stage 104, the projection optical system 105, and the wafer stage 106. The refrigerant whose temperature is controlled in the first block 40 can be returned to the first block 40 after the temperature of the one or more target units is controlled while the one or more target units are heat removed in the second block 41.

The first block 40 may include, for example, a temperature control unit (control target unit) 401, a temperature control unit 402, a sensor 401T, a sensor 402T, a control unit 401C, and a control unit 402C. The temperature control unit 401 may lower the temperature of the refrigerant to a target temperature and supply the refrigerant to the temperature control unit 402. The control unit 401C determines a command value in accordance with the temperature measured by the sensor 401T so that the temperature of the refrigerant is consistent with the target temperature, and inputs the command value to the temperature control unit 401 for control. Furthermore, the temperature control unit 401 operates with an action amount corresponding to the command value.

In addition, the temperature control unit 402 can adjust the temperature of the refrigerant to the temperature range allowed by the second block 41 and supply the refrigerant to the second block 41. The control unit 402C can determine the command value in a manner such that the temperature of the refrigerant falls within the temperature range allowed by the second block 41 according to the temperature measured by the sensor 402T, and operate the temperature control unit 402 according to the operation amount of the command value.

In the second block 41, the temperature of the refrigerant can be adjusted through the temperature control units 412 to 415 in such a manner that each of the target units 416 to 419 falls within the target temperature range. The control unit 412C can determine the command value in such a manner that the target unit 416 falls within the target temperature range according to the temperature measured by the sensors 412T1 and 412T2, and operate the temperature control unit 412 in accordance with the amount of action of the command value. The control unit 413C can determine the command value in such a manner that the target unit 417 falls within the target temperature range according to the temperature measured by the sensors 413T1 and 413T2, and operate the temperature control unit 413 in accordance with the amount of action of the command value.

The control unit 411C can determine the command value in such a way that the temperature of the refrigerant falls within the target temperature range according to the temperature measured by the sensor 411T1 and the information from the control units 414C and 415C, and operate the temperature control unit 411 in accordance with the amount of action of the command value. That is, the control unit 414C can determine the command value in such a way that the target unit 418 falls within the target temperature range according to the temperature measured by the sensors 414T1 and 414T2, and operate the temperature control unit 414 in accordance with the command value. The control unit 415C can determine the command value in such a way that the target unit 419 falls within the target temperature range according to the temperature measured by the sensors 415T1 and 415T2, and operate the temperature control unit 415 in accordance with the amount of action of the command value.

The temperature control units 401, 402, 412-415 can be heating units or cooling units through heat exchange. In addition, for example, the temperature control unit 401 can be a cooling unit, and the temperature control units 402, 412-415 can be heating units. In addition, the temperature control units 401, 402, 412-415 can not only heat or cool the refrigerant, but also control the flow rate and pressure of the refrigerant circulating in the piping to adjust the temperature of the refrigerant.

In addition, the refrigerant circulating in the pipes may be liquid or gas.

In the temperature control system 301 shown in FIG. 4 , the temperature of the object unit is controlled, and sensors 401T, 402T, 411T to 415T2 are provided as sensors. However, the temperature control system 301 may also include sensors for measuring information other than temperature (e.g., a flow sensor of a refrigerant, a pressure sensor, etc.). In addition, the temperature control system 301 may also include a control unit for controlling the control object with respect to parameters other than temperature (e.g., a flow rate and pressure of a refrigerant, etc.).

Here, a model representing the relationship between the output values of each sensor is described. For simplicity, in the temperature control system 301 shown in FIG4, the output values of two sensors (for example, sensors 401T and 402T) at time t are a _t and b _t . The relationship between the output values a _t and b _t can be defined by the model (function) given by equation (1).

b _t =f(a _t )・・・(1) Model f may be, for example, a regression equation determined by the least squares method based on time series data of output values a _t and b _t outputted through two sensors. In addition, model f may be, for example, a learning model generated using machine learning. For example, model f may be a model including a neural network. A neural network is a model having a multi-layer network structure such as an input layer, an intermediate layer, and an output layer. Based on time series data of output values a _t and b _t outputted through two sensors, learning data showing the relationship between a _t as input data and b _t as teacher data is obtained. Furthermore, the learning model can be obtained by optimizing the connection weighting coefficients and the like inside the neural network using the obtained learning data according to an algorithm such as the error back propagation method. The error back propagation method is a method of adjusting the connection weighting coefficients and the like between the nodes of each neural network in such a way that the difference between the output data and the teacher data becomes smaller. In addition, the model f may be a model other than a neural network, for example, a learning model including an SVM (support vector machine).

The model _fij ( _xj ) for the output value (hereinafter, predicted output value) _xij of the sensor _Si can be given as a function of the output value (hereinafter, measured output value) _xj of the sensor _Sj by equation (2). Here, i is an integer from 1 to N, and N is the number of sensors. j is an integer other than i from 1 to N.

x _ij =f _ij (x _j )・・・(2) Here, formula (2) can be expressed as the following group of numbers.

x ₁₂ =f ₁₂ (x ₂ ) x ₁₃ =f ₁₃ (x ₃ ) x ₁₄ =f ₁₄ (x ₄ ) ・ ・ ・ x _1N =f _1N (x _N ) x ₂₁ =f ₂₁ (x ₁ ) x ₂₃ =f ₂₃ (x ₃ ) x ₂₄ =f ₂₄ (x ₄ ) ・ ・ ・ Furthermore, an evaluation value is calculated based on the predicted output value x _ij of the sensor S _i and the measured output value x _i of the sensor S _i , and an abnormality related to the sensor S _i is detected based on the evaluation value. The evaluation value is, for example, a value obtained by processing the difference between each of a plurality of predicted output values x _ij and the corresponding measured output value x _i , and for example, a value obtained by normalizing the sum of the differences by the number of the plurality of models. In addition, the evaluation value may be a value calculated based on a difference or ratio between a statistical value such as an average value or a median value of a plurality of predicted output values _xij and the measured output value _xij . Furthermore, when the evaluation value does not fall within a preset allowable range, an abnormality is detected in the output value of the sensor _Sj .

Here, although the example of using a model representing the relationship between the output values of each sensor is described, a model representing the relationship between the command values of each control unit may also be used. For example, in the temperature control system 301 shown in FIG. 4 , a model representing the relationship between the command value of the control unit 401C and the command value of the control unit 402C at time t may also be used. That is, the model g _ij (y _j ) of the command _value (hereinafter, predicted command value) y _ij given to the control unit C _i can be given as a function of the command value (hereinafter, measured command value) y _j of the control unit C j by equation (3).

y _ij =g _ij (y _j )・・・(3) Furthermore, an evaluation value is calculated based on the predicted command value y _ij of the control unit _Ci and the measured command value y _j of the control unit _Ci , and an abnormality related to the control unit _Ci is detected based on the evaluation value. In addition, the movement amount of the temperature control unit controlled by the control unit may be used instead of the command value of the control unit (hereinafter, the command value or the movement amount is referred to as the control data).

In addition, for example, a model that expresses the relationship between the output value of each sensor and the command value of each control unit may be used. For example, in the temperature control system 301 shown in FIG. 4 , a model that expresses the relationship between the output value of the sensor 401T at time t and the command value of the control unit _401C may be used. That is, the model _hij ( _xj ) of the command value (hereinafter, predicted command value) _yij given to the control unit Ci can be given as a function of the output value (hereinafter, measured output value) _xj of the sensor _Sj by equation (4).

y _ij =h _ij (x _j )・・・(4) Furthermore, an evaluation value is calculated based on the predicted command value y _ij of the control unit _Ci and the measured output value x _j of the sensor S _j , and an abnormality of the relevant control unit _Ci is detected based on the evaluation value. Furthermore, an evaluation value is calculated based on the predicted output value x _ij of the sensor S _i and the measured command value y _j of the control unit _Ci , and a model h is generated in such a manner that an abnormality of the relevant sensor S _i is detected based on the evaluation value.

In addition, a model represented by at least one of equations (2), (3), and (4) may be used. That is, a model representing the relationship between the output values of each sensor, a model representing the relationship between the command values of each control unit, and a model representing the relationship between the output value of each sensor and the command value of the control unit may be used in any combination. In addition, the amount of movement of the temperature control unit controlled by the control unit may be used instead of the command value of the control unit.

In this way, the management device 12 can obtain the output value of the sensor in the temperature control system 301 and the information of the control data of the control unit, and generate a model based on the obtained output value and the information of the control data. In addition, the management device 12 can store the information of the generated model in the memory device 204.

Here, the problem of detecting an abnormality of the temperature control system 301 is described. For example, when an abnormality occurs in the temperature control unit 402, the abnormality is detected based on the evaluation value calculated from the model associated with the output value of the sensor 402T. In addition, in the piping where the refrigerant circulates, the temperature control units 411, 412, and 413 are located downstream of the temperature control unit 402. In addition, due to the change in the temperature of the refrigerant caused by the abnormality of the temperature control unit 402, the evaluation value calculated from the model associated with the output value of the sensors 411T, 412T1, and 413T2 is also detected as abnormal. In addition, similarly, due to the change in the temperature of the refrigerant caused by the abnormality of the temperature control unit 402, the evaluation value calculated based on the model of the control data related to the control units 411C, 412C, and 413C is also detected as abnormal. That is, due to the abnormality of the temperature control unit 402, the sensors and control units related to the temperature control units 411, 412, and 413 are also detected as abnormal, which makes it difficult to identify the temperature control unit where the abnormality occurs.

Furthermore, for example, when the target unit 417 includes the projection optical system 105, the temperature of the target unit 417 rises due to the heat of the exposure light irradiated during the exposure process of the exposure device 10. Furthermore, for example, when the target unit 419 includes the substrate stage 6, the temperature of the target unit 419 rises due to the heat generated by the driving of the driving unit of the substrate stage 6 during the exposure process of the exposure device 10. Furthermore, when the exposure process of the exposure device 10 is completed, the exposure light is no longer irradiated, and the driving unit of the target unit 419 stops, so the temperature of the target units 417 and 419 decreases. In this way, even for the target unit whose temperature is controlled by different temperature control units, the actions of the target units are linked in the exposure process in the exposure device 10, so that the output values of each sensor are correlated. And, for example, when an abnormality occurs in the temperature control unit 413 associated with the target unit 417, the abnormality is detected based on the evaluation value calculated from the model associated with the sensors 413T1, 413T2 and the control unit 413C. In addition, since the temperatures of the target units 417 and 419 are correlated, the abnormality is also detected based on the evaluation value calculated from the model associated with the sensors 415T1, 415T2 and the control unit 415C. That is, although the temperature control unit where the abnormality occurs is the temperature control unit 413, the sensor and the control unit associated with the temperature control unit 415 also detect the abnormality.

In this way, the abnormality is detected based on the evaluation values calculated from the model corresponding to a plurality of temperature control units, making it difficult to identify the temperature control unit where the abnormality occurs.

Therefore, the management device 12 in this embodiment groups the evaluation values calculated from the models of the relevant sensors and control units, and obtains the abnormality of each group based on the evaluation values belonging to the individual groups, and detects the abnormality of the temperature control system 301 based on the obtained abnormality.

FIG5 is a flowchart illustrating a method for detecting an abnormality of a temperature control system in the present embodiment. In S501, the acquisition unit 211 acquires information on the output value of a sensor in the temperature control system 301 and the control data of a control unit, and the calculation unit 213 generates a model representing the relationship between the output value of the sensor and the like based on the acquired output value and the information on the control data. Here, the generated model can be defined as a model generated using the output value of the sensor in the temperature control system 301 and the control data of the control unit. In addition, the acquired model can be defined as at least one of a model representing the relationship between the output values of the sensors, a model representing the relationship between the control data of the control unit, and a model representing the relationship between the output value of the sensor and the control data of the control unit.

In S502, the acquisition unit 211 acquires the output value of the sensor and the information on the control data of the control unit in the temperature control system 301. And the calculation unit 213 calculates the output value of the sensor and the evaluation value of the control data of each control unit using the output value, the information on the control data, and the calculated model.

In S503, the calculation unit 213 calculates the abnormality of each group based on the information about the grouping of the sensors and control units. The abnormality of each group is a value indicating the degree of abnormality of the output value of the sensor or the control data of the control unit belonging to the group. In addition, the abnormality of each group can be determined as a value obtained by dividing the evaluation value obtained from the generated model by group and summing up the evaluation values belonging to each group, or an average value, etc.

Here, the grouping information is pre-stored in the memory device 204, and the management device 12 can obtain the grouping information from the memory device 204. In addition, the management device 12 can also obtain the grouping information from an external information processing device via the communication device 207. In addition, the method of grouping sensors and related control units will be described later.

In S504, the determination unit 214 determines abnormality for each group based on the acquired abnormality degree for each group. That is, when the abnormality degree of the group is not within a preset allowable range, the management device 12 determines that an abnormality has occurred in the sensor or control unit belonging to the group.

Next, the information of the group obtained by the management device 12 in S503 is described in detail with various embodiments.

(Example 1) In Example 1, an example of grouping is performed according to the blocks where sensors and control units exist. FIG. 6 is a diagram showing an example of grouping in this embodiment. In FIG. 6(a), the sensor 401T, sensor 402T, control unit 401C, and control unit 402C included in the first block 40 in FIG. 4 belong to group 1-1. In addition, the sensors 411T, 412T1~ 415T1, 412T2~415T2, and control units 411C~415C included in the second block 41 in FIG. 4 belong to group 1-2.

In addition, the sensors can be grouped only by sensors as shown in FIG6(b). In FIG6(b), the sensors 401T and 402T in the first block 40 in FIG4 belong to the group 1-3. In addition, the sensors 411T, 412T1-415T1, 412T2-415T2 in the second block 41 in FIG4 belong to the group 1-4.

In addition, the control units can also be grouped only as shown in FIG6(c). In FIG6(c), the control units 401C and 402C included in the first block 40 in FIG4 belong to the group 1-5. In addition, the control units 411C to 415C included in the second block 41 in FIG4 belong to the group 1-6.

In addition, the groups in Figures 6(a) to 6(c) can be combined arbitrarily. For example, the group 1-1 in Figure 6(a) and the group 1-4 in Figure 6(b) can be combined.

By grouping in this way, the management device 12 can determine whether an abnormality has occurred in either the temperature control unit in the first block 40 or the temperature control unit in the second block 41 .

(Example 2) In Example 2, an example of grouping is performed according to the sensors and control units of the temperature control unit. FIG. 7 is a diagram showing an example of grouping in this embodiment. For example, the sensor 401T and the control unit 401C of the temperature control unit 401 in FIG. 4 belong to group 2-1. In addition, for example, the sensor 412T1 and the control unit 412C of the temperature control unit 412 belong to group 2-4. In addition, the sensor 412T2 of the object unit 416 may also belong to group 2-4. Similarly, the sensors 413T2~415T2 of the object units 417~419 may also belong to groups 2-5~2-7 respectively.

In addition, as in Embodiment 1, grouping may be performed by only sensors, only control units, or any combination of sensors and control units.

By grouping in this way, the management device 12 can determine whether any one of the plurality of temperature control units has an abnormality.

(Example 3) In Example 3, an example of grouping is performed according to a group (hereinafter referred to as a control group) representing a range of information transmission related to control. FIG8 is a diagram showing an example of grouping in this embodiment. For example, the sensor 401T and the control unit 401C of the temperature control unit 401 in FIG4 belong to the group 3-1. That is, the information of the output value of the sensor 401T is transmitted to the control unit 401C and the control data is determined, so the sensor 401T and the control unit 401C belong to the same control group. In addition, for example, the sensors 411T, 414T1, 414T2, the control units 411C, and 414C belong to the group 3-3. That is, the information of the output values of the sensors 414T1 and 414T2 is transmitted to the control unit 414C and the control data is determined. In addition, the information of the output value of the sensor 411T and the information of the control data of the control unit 414C are transmitted to the control unit 411C and the control data is determined. In addition, in FIG8, although the groups 3-3 and 3-4 are different control groups, the information of the control data of the control units 414C and 415C is transmitted to the control unit 411C, so the groups 3-3 and 3-4 can also be the same control group.

In addition, as in Embodiment 1, grouping may be performed by only sensors, only control units, or any combination of sensors and control units.

Through such grouping, the management device 12 can determine if an abnormality occurs in any temperature control unit belonging to the control group.

(Example 4) In Example 4, an example of grouping according to the piping of the refrigerant circulation is shown. FIG. 9 is a diagram showing an example of grouping in this embodiment. In the example of FIG. 9(a), the sensors and control units of the temperature control units of the piping branched downstream of the temperature control unit 402 in the direction of the refrigerant circulation belong to groups 4-1 and 4-2. In addition, the sensors and control units of the temperature control units of the piping branched downstream of the temperature control unit 411 in the direction of the refrigerant circulation belong to groups 4-3 and 4-4. The temperature control units 402 and 412 in FIG. 4 and the sensors and control units of the piping of the configuration object unit 416 belong to group 4-1. Specifically, the sensors 402T, 412T1, 412T2, the control units 402C, and 412C belong to the group 4-1. In addition, the temperature control units 402, 411, 414 in FIG. 4 and the sensors and control units of the pipes in the configuration object unit 418 belong to the group 4-3. Specifically, the sensors 402T, 411T, 414T1, 414T2, the control units 402C, 411C, and 414C belong to the group 4-3. In addition, the temperature control units 411, 415 in FIG. 4 and the sensors and control units of the pipes in the configuration object unit 419 belong to the group 4-4. Specifically, sensors 411T, 415T1, 415T2, control units 411C, and 415C belong to group 4-4.

In the example of FIG. 9( b), the sensor 402T and the control unit 402C of the temperature control unit 402 belong to group 4-5. In addition, the sensor and the control unit of the temperature control unit arranged on the piping downstream of the temperature control unit 402 in the direction of the refrigerant circulation belong to group 4-5. In addition, the sensor 411T and the control unit 411C of the temperature control unit 411 belong to group 4-6. In addition, the sensor and the control unit of the temperature control unit arranged downstream of the piping in which the refrigerant circulates relative to the temperature control unit 411 belong to group 4-6.

In addition, in this embodiment, although all sensors and control units of the temperature control unit arranged on the downstream piping in the direction of refrigerant circulation are grouped, only some sensors and control units may be targeted. For example, since the sensor 412T1 and the sensor 412T2 are adjacent to the same piping, either the sensor 412T1 or the sensor 412T2 may be deleted.

In addition, as in Embodiment 1, grouping may be performed by only sensors, only control units, or any combination of sensors and control units.

By grouping in this way, the management device 12 can determine whether an abnormality has occurred in the temperature control unit of any one of the branched pipes disposed in the pipe through which the refrigerant circulates.

Based on the above, the management device according to the present embodiment can calculate the degree of abnormality for each group and identify the sensor or control unit of the group where the abnormality occurs, which is advantageous in detecting abnormalities in the temperature control system.

<Second Implementation> Next, the management device 12 related to the second implementation will be described. In addition, matters not mentioned here can be followed in accordance with the first implementation.

The management device 12 in this embodiment generates a model for each group based on the output values of the grouped sensors and the control data of the control unit, and calculates the abnormality for each group based on the evaluation value calculated by the model belonging to the individual group.

FIG. 10 is a flowchart showing a method for detecting an abnormality in the temperature control system in the present embodiment. In S1001, the acquisition unit 211 acquires information on the output values of the sensors and the control data of the control unit by group, and the generation unit 212 generates a model representing the relationship between the output values of the sensors, etc., by group based on the information on the acquired output values and control data. Here, the acquired model can be defined as a model generated using the output values of the sensors and the control data of the control unit grouped by the sensors and the control unit in the temperature control system 301. In addition, the grouping example can be the same as the examples 1 to 4 in the first embodiment. In addition, the grouping information is pre-stored in the storage device 204, and the acquisition unit 211 can acquire the grouping information from the storage device 204. In addition, the acquisition unit 211 can also obtain the grouped information from an external information processing device via the communication device 207.

In S1002, the acquisition unit 211 acquires the output value of the sensor and the information on the control data of the control unit in the temperature control system 301. And the calculation unit 213 calculates the output value of the sensor for each group and the evaluation value of the control data of the control unit using the output value, the information on the control data, and the model for each calculated group.

In S1003, the calculation unit 213 calculates the degree of abnormality for each group based on the evaluation value calculated using the model for each group. The degree of abnormality for each group can be determined as a value obtained by statistical processing such as a total value or an average value of the evaluation values obtained from the model belonging to the group.

In S1004, the determination unit 214 determines abnormality for each group based on the acquired abnormality level for each group. That is, when the abnormality level of the group is not within a preset allowable range, the management device 12 determines that an abnormality has occurred in the sensor or control unit belonging to the group.

Based on the above, in the management device related to the present embodiment, the abnormality degree of each group is calculated, and the sensor or control unit of the group where the abnormality occurs can be determined, so it is advantageous in detecting abnormalities in the temperature control system.

(Manufacturing method of article) In terms of articles, for example, the manufacturing method of a device (semiconductor device, magnetic storage medium, liquid crystal display element, etc.), a color filter, or a hard disk is described. Such a manufacturing method includes a process of forming a pattern on a substrate (wafer, glass plate, film substrate, etc.) using a photolithography device (for example, an exposure device, an imprinting device, a drawing device, etc.). Such a manufacturing method further includes a process of processing the substrate on which the pattern is formed. The processing step may include a step of removing the residual film of the pattern. In addition, other well-known steps such as a step of etching the substrate using the pattern as a mask may be included. The manufacturing method of the article under this embodiment is more advantageous than ever in terms of at least one of the performance, quality, productivity, and production cost of the article.

Although the preferred embodiments of the present invention are described above, the present invention is certainly not limited to these embodiments, and various modifications and changes can be made within the scope of the gist.

In addition, embodiments 1 to 4 can be implemented not only individually but also in any combination of embodiments 1 to 4. According to the present invention, a technology that is advantageous in detecting an abnormality of a control system having a plurality of sensors and a plurality of control units is provided. [Other embodiments]

The embodiments of the present invention may also be implemented through a computer of a system or device and a method, wherein the computer reads and executes computer-executable instructions (e.g., one or more programs) recorded in a storage medium (which may also be more fully referred to as a "non-transitory computer-readable storage medium") to implement the functions of one or more of the aforementioned embodiments, and/or includes one or more circuits (e.g., application specific integrated circuits (ASICs)) for implementing the functions of one or more of the aforementioned embodiments. The method is implemented by a computer of the system or device, for example, by reading and executing computer-executable instructions from a storage medium to implement the functions of one or more of the aforementioned embodiments and/or controlling one or more circuits to implement the functions of one or more of the aforementioned embodiments. The computer may include one or more processors (e.g., a central processing unit (CPU), a microprocessing unit (MPU)) and may include an independent computer or a network of independent processors to read and execute computer executable instructions. The computer executable instructions may be provided to the computer from, for example, a network or a storage medium. The storage medium may include, for example, one or more hard disks, random access memory (RAM), read-only memory (ROM), storage of a distributed computing system, optical disks (e.g., optical disks CD, digital versatile disks DVD or Blu-ray disks BD ^(TM) ), flash memory devices, memory cards, etc. Although the present invention is described with reference to embodiments, it should be understood that the present invention is not limited to the disclosed embodiments. The scope of the patent application should be interpreted broadly to include various modifications, equivalent structures and functions. This case claims priority to Japanese Patent Application No. 2020-071692 filed on April 13, 2020, the entire contents of which are hereby incorporated by reference.

1: Substrate processing system 10: Substrate processing device 11: Main computer 12: Management device 41: Block 2 42: Direction of refrigerant circulation 43: Direction of information transmission related to control 101: Light source unit 102: Illumination system 103: Mask 104: Mask stage 105: Projection optical system 106: Wafer stage 107: Wafer fixture 108: Wafer 109: Pre-alignment unit 110: Wafer box 111: Control unit 201: CPU 202: ROM 203: RAM 204: Memory device 205: Input device 206: Display device 207: Communication device 208: Bus 211: Acquisition unit 212: Generation unit 213: Calculation unit 214: Judgment unit 301: Adjustment unit Temperature system 401: Temperature control unit 402: Temperature control unit 411: Temperature control unit 412: Temperature control unit 414: Temperature control unit 415: Temperature control unit 416: Object unit 417: Object unit 418: Object unit 419: Object unit 401C: Control unit 401T: Sensor 402C: Control unit 402T: Sensor 411 C: Control unit 411T: Sensor 412C: Control unit 412T1: Sensor 412T2: Sensor 413C: Control unit 413T1: Sensor 413T2: Sensor 414C: Control unit 414T1: Sensor 414T2: Sensor 415C: Control unit 415T1: Sensor 415T2: Sensor

[FIG. 1] is a diagram showing the configuration of a substrate processing system. [FIG. 2] is a diagram showing the configuration of a management device. [FIG. 3] is a diagram showing the configuration of an exposure device and a host computer. [FIG. 4] is a diagram showing the configuration of a temperature control system incorporated in an exposure device. [FIG. 5] is a flowchart showing a method for detecting an abnormality of a temperature control system in the first embodiment. [FIG. 6] is a diagram showing an example of grouping in Embodiment 1. [FIG. 7] is a diagram showing an example of grouping in Embodiment 2. [FIG. 8] is a diagram showing an example of grouping in Embodiment 3. [FIG. 9] is a diagram showing an example of grouping in Embodiment 4. [Figure 10] is a flow chart showing a method for detecting an abnormality in the temperature control system in the second embodiment.

40: Block 1

41: Block 2

42: Direction of refrigerant circulation

43: Direction of communication of information related to control

301: Temperature Control System

401: Temperature control unit

401C: Control unit

401T:Sensor

402: Temperature control unit

402C: Control unit

402T:Sensor

411: Temperature control unit

411C: Control unit

411T:Sensor

412: Temperature control unit

412C: Control unit

412T1:Sensor

412T2:Sensor

413: Temperature control unit

413C: Control unit

413T1:Sensor

413T2:Sensor

414: Temperature Control Unit

414C: Control unit

414T1:Sensor

414T2:Sensor

415: Temperature control unit

415C: Control unit

415T1:Sensor

415T2:Sensor

416: Object unit

417: Object unit

418: Object unit

419: Object unit

Claims (14)

An information processing device for detecting an abnormality of a control system having a plurality of sensors and a plurality of control units, comprising: a calculation unit for calculating an abnormality degree representing the degree of abnormality of the output value of the sensor or the control data of the control unit using a model representing the relationship between the output values of two sensors among the plurality of sensors, the relationship between the control data of two control units among the plurality of control units, or the relationship between the output value of one sensor among the plurality of sensors and the control data of one control unit among the plurality of control units, according to the plurality of sensors or the plurality of control units being divided into at least two groups; and a determination unit for determining the abnormality of the plurality of sensors or the plurality of control units according to the group based on the abnormality degree calculated by the calculation unit. An information processing device as claimed in claim 1, comprising a generating unit for generating the aforementioned model based on output values of the aforementioned plurality of sensors or control data of the aforementioned plurality of control units. As in claim 1, the information processing device, wherein the plurality of sensors or the plurality of control units are divided into the groups according to the chamber of the substrate processing device having the control system. An information processing device as claimed in claim 1, wherein the aforementioned multiple sensors or the aforementioned multiple control units are divided into the aforementioned groups according to the output values of the aforementioned sensors or the scope of information transmission of the relevant control data of the aforementioned control units. An information processing device as claimed in claim 1, wherein the control system is a temperature control system for adjusting the temperature of an object unit, and the plurality of sensors or the plurality of control units are divided into the groups according to the temperature control units or the object units controlled by the control units. An information processing device as claimed in claim 1, wherein the control system is a temperature control system for adjusting the temperature of a target unit by circulating a refrigerant in a pipe, and the plurality of sensors or the plurality of control units are divided into the groups based on information on branches of pipes related to refrigerant circulation in the temperature control system. An information processing device as claimed in claim 1, wherein the calculation unit divides the plurality of evaluation values calculated using the model into groups according to the information of the group, and calculates the abnormality according to the group. An information processing device as claimed in claim 1, wherein the calculation unit calculates the abnormality according to the group based on the plurality of evaluation values calculated using the model grouped according to the information of the group. An information processing device as claimed in claim 1, wherein the aforementioned sensor includes a sensor for measuring temperature, flow or pressure. An information processing device as claimed in claim 1, wherein the control data includes a command value input to a control target unit controlled by the control unit, or an action amount of the control target unit according to the command value. A detection method for detecting an abnormality of a control system having a plurality of sensors and a plurality of control units, comprising: a calculation program for calculating an abnormality degree representing the degree of abnormality of the output value of the sensor or the control data of the control unit using a model representing the relationship between the output values of two sensors among the plurality of sensors, the relationship between the control data of two control units among the plurality of control units, or the relationship between the output value of one sensor among the plurality of sensors and the control data of one control unit among the plurality of control units, according to the plurality of sensors or the plurality of control units being divided into at least two groups; and a determination program for determining the abnormality of the plurality of sensors or the plurality of control units according to the group based on the abnormality degree calculated in the calculation program. A non-temporary computer-readable storage medium storing a program for causing a computer to execute a detection method for detecting an abnormality of a control system having a plurality of sensors and a plurality of control units, wherein the detection method comprises: a calculation program for using a relationship between output values of two sensors among the plurality of sensors, a relationship between control data of two control units among the plurality of control units, or a relationship between output values of one sensor among the plurality of sensors. A model of the relationship between an output value and control data of one of the plurality of control units, wherein an abnormality indicating the degree of abnormality of the output value of the sensor or the control data of the control unit is calculated for each group in which the plurality of sensors or the plurality of control units are divided into at least two groups; and a determination program for determining abnormalities of the plurality of sensors or the plurality of control units for each group based on the abnormality calculated in the calculation program. A substrate processing system, comprising: a substrate processing device having a control system with a plurality of sensors and a plurality of control units and processing a substrate, and a management device for detecting an abnormality of the control system, the management device comprising: a calculation unit, which uses a relationship between output values of two sensors among the plurality of sensors, a relationship between control data of two control units among the plurality of control units, or a relationship between output values of one of the plurality of sensors. A model of the relationship between the output value of the sensor and the control data of one of the plurality of control units, wherein the degree of abnormality of the output value of the sensor or the control data of the control unit is calculated for each group in which the plurality of sensors or the plurality of control units are divided into at least two groups; and a determination unit that determines the abnormality of the plurality of sensors or the plurality of control units for each group based on the degree of abnormality calculated by the calculation unit. A method for manufacturing an article, comprising: a procedure for processing a substrate using a substrate processing system, and a procedure for manufacturing an article from the substrate processed in the procedure, wherein the substrate processing system comprises: a substrate processing device for processing a substrate having a control system having a plurality of sensors and a plurality of control units, and a management device for detecting an abnormality of the control system, wherein the management device comprises: a calculation unit which uses a relationship between output values of two of the plurality of sensors and two of the plurality of control units. a model of the relationship between the output value of a sensor among the plurality of sensors and the control data of a control unit among the plurality of control units, or the relationship between the output value of a sensor among the plurality of sensors and the control data of a control unit among the plurality of control units, and calculating an abnormality indicating the degree of abnormality of the output value of the sensor or the control data of the control unit for each group in which the plurality of sensors or the plurality of control units are divided into at least two groups; and a determination unit for determining the abnormality of the plurality of sensors or the plurality of control units for each group based on the abnormality calculated by the calculation unit.

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