TW202208996A - Information processing apparatus, detection method, substrate processing system, and article manufacturing method - Google Patents

Abstract

The invention relates to an information processing apparatus, a detection method, a substrate processing system, and an article manufacturing method. The information processing apparatus detects an abnormality in a control system including a plurality of sensors and a plurality of control units, the information processing apparatus including: a calculation unit that uses a model representing the relationship between the output values of two of the plurality of sensors, the relationship between the control data of two of the plurality of control units, or the relationship between the output value of one of the plurality of sensors and the control data of one of the plurality of control units, wherein, for at least two groups into which the plurality of sensors or the plurality of control units are divided, an abnormality degree for each group is calculated, the abnormality degree indicating an abnormality degree of the output values of the sensors or the control data of the control units; and a determination unit that determines an abnormality of the plurality of sensors or the plurality of control units for each group on the basis of the abnormality degree calculated by the calculation unit.

Description

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

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

For substrate processing apparatuses that process substrates in order to manufacture articles such as semiconductor devices, MEMS, and flat panel displays, there is an increasing demand for improvement in productivity. For this reason, it is necessary to suppress production interruption due to a sudden occurrence of abnormality of the substrate processing apparatus. Therefore, nowadays, it is necessary to detect the abnormality of the substrate processing apparatus in advance and eliminate the cause of the abnormality.

Japanese Patent Application Laid-Open No. 2017-21702 discloses a failure sign monitoring method for monitoring failure signs of a plurality of equipment arranged in a factory. In Japanese Patent Laid-Open No. 2017-21702, in order to monitor failure signs of a plurality of devices, a model representing the relationship between the output values of the sensors is constructed based on the output values of the sensors that measure the behavior of each device. Then, based on the difference between the output value of the sensor and the prediction data calculated using the model, the change of the invariant between the output values of each sensor is detected, and the failure sign of each device is detected.

When detecting an abnormality in a control system including a plurality of sensors and a plurality of control units, it is possible to detect the output value of the sensor related to the control unit in which the abnormality does not occur, and the control data of the control unit as abnormal. For example, in the case of a temperature control system in which a refrigerant circulates in a pipe to adjust the temperature, a plurality of control units for controlling a plurality of temperature control units for adjusting the temperature of the refrigerant, such as a cooler, a heater, and a heat exchanger, are provided, A plurality of sensors that measure the temperature of the refrigerant, etc. In such a temperature control system, even if an abnormality occurs in a part of the temperature control units, there is a possibility that the output value of the sensor related to the control unit that does not have abnormality, the control unit due to the temperature fluctuation of the refrigerant circulating in the piping, An abnormality was detected in the control data of the unit.

An object of the present invention is to provide a technique that is advantageous for detecting an abnormality in a control system including a plurality of sensors and a plurality of control units. [Technical means to solve problems]

An information processing device that is an aspect of the present invention detects an abnormality in a control system including a plurality of sensors and a plurality of control units, and includes a calculation unit that uses an index representing the plurality of sensors. relationship between the output values of 2 sensors in The model of the relationship between the control data of one control unit in the unit will represent the abnormality degree of the output value of the sensor or the degree of abnormality of the control data of the control unit, according to at least a plurality of sensors or a plurality of control units. The calculation is performed by dividing into 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 degree of abnormality calculated by the calculation unit. Other features of the present invention will be clarified by the following embodiments (refer to the drawings).

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. In each drawing, the same members are given the same reference numerals, and the overlapping descriptions are omitted.

<First Embodiment> FIG. 1 is a diagram illustrating a configuration of a substrate processing system. The substrate processing system 1 (product manufacturing system) may include a plurality of substrate processing apparatuses 10 for processing substrates, respectively, and a host computer 11 for controlling the operations of the plurality of substrate processing apparatuses 10 . The plurality of substrate processing apparatuses 10 may include, for example, a lithography apparatus (exposure apparatus, imprint apparatus, charged particle beam drawing apparatus, etc.). In addition, the plurality of substrate processing apparatuses 10 may include any one of a coating apparatus, a developing apparatus, a film forming apparatus (CVD apparatus, etc.), a processing apparatus (laser processing apparatus, etc.), and an inspection apparatus (stack inspection apparatus, etc.) . Here, the exposure device exposes the photoresist supplied on the substrate through the original plate (reduction mask, mask) to form a latent image corresponding to the pattern of the original plate on the photoresist. Further, the imprint apparatus hardens the imprint material in a state where the mold (original plate) is brought into contact with the imprint material supplied over the substrate to form a pattern over the substrate. In addition, the charged particle beam drawing device draws a pattern on the photoresist supplied on the substrate through the charged particle beam to form a latent image on the photoresist. In addition, the coating apparatus performs the coating process of the resist material (adhesive material) on the substrate as a pretreatment of the photolithography process. In addition, the developing device performs development processing as a post-processing of the photolithography processing. In addition, the film-forming apparatus is an apparatus which forms films, such as an insulating film, on a board|substrate. Moreover, the processing apparatus performs processing of the pattern formed on the board|substrate, cutting of a board|substrate, opening of a hole, and the like. In addition, the inspection apparatus inspects the positional accuracy, line width, and the like of the pattern formed on the substrate.

FIG. 2 is a diagram illustrating the configuration of the management device 12 . The management device 12 can be realized by a computer (information processing device) communicably connected to each substrate processing device 10 . In FIG. 2( a ), a CPU 201 (processing unit) is a central processing unit (CPU) that executes an OS (Operating System) and various application programs. In addition, the CPU 201 is not limited to a central processing unit (CPU), but may also be a processor or circuit such as a micro processing unit (MPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC). In addition, the CPU 201 may also be a combination of any of these processors or circuits. The ROM 202 is a memory that stores fixed data among programs executed by the CPU 201 and parameters for calculation. The RAM 203 is a memory that provides a work area of the CPU 201 and a temporary storage area for data. ROM 202 and RAM 203 are connected to CPU 201 via bus 208. 205 is an input device (input unit) including a mouse, a keyboard, and the like, and 206 is a display device (display unit) such as a CRT and a liquid crystal display. In addition, the input device 205 and the display device 206 may be integrated devices such as a touch panel. In addition, the input device 205 and the display device 206 may also be configured as devices with different shapes from the computer. 204 is a memory device such as a hardware device, CD, DVD, memory card, etc., and stores various programs, various data, and the like. The input device 205, the display device 206, and the memory device 204 are respectively connected to the bus bar 208 through interfaces not shown. In addition, the communication device 207 connected to the network for communication is also connected to the bus bar 208 . The communication device 207 is connected to, for example, a LAN to perform 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 transmitting unit and a receiving unit, for example, receives data such as operation information from a transmitting unit (not shown) in the substrate processing apparatus 10 and stores it in the memory device 204 . In addition, FIG.2(b) is a figure which shows the structure of CPU201. The CPU 201 includes an acquisition unit 211 , a generation unit 212 , a calculation unit 213 , and a determination unit 214 .

As mentioned above, although the schematic structure of the management apparatus 12 was demonstrated with reference to FIG. 2, the host computer 11 and the board|substrate processing apparatus 10 may be equipped with the same computer.

Each of the plurality of substrate processing apparatuses 10 in the substrate processing system 1 is connected to a management apparatus 12 that manages maintenance. Additionally, as shown in FIG. 1 , the article manufacturing system may include a plurality of substrate processing systems 1 . Therefore, the management apparatus 12 can manage each of the substrate processing apparatuses 10 in the plurality of substrate processing systems 1 . The management apparatus 12 collects and analyzes individual operation information of the plurality of substrate processing apparatuses 10 , detects abnormality or signs of each substrate processing apparatus 10 , and functions as a maintenance determination apparatus for determining the necessity of maintenance processing (maintenance processing). In addition, in FIG. 1 , the connection between the plurality of substrate processing apparatuses 10 and the host computer 11 and the connection between the plurality of substrate processing apparatuses 10 and the management apparatus 12 may be wired or wireless.

In the following, in order to provide a specific example, an example in which the substrate processing apparatus 10 is configured as the exposure apparatus 10 will be described. FIG. 3 is a diagram illustrating the configuration of an exposure apparatus and a host computer. As shown in FIG. 3 , the exposure apparatus 10 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 holder 107 , a pre-alignment unit 109 , and a control unit 111 .

The light emitted from the light source unit 101 illuminates the mask 103 held by the mask stage 104 via the lighting system 102 . The light source of the light source unit 101 includes, for example, a high-pressure mercury lamp, an excimer laser, and the like. In addition, in the case of an excimer laser as the light source, the light source unit 101 is not limited to being inside the chamber of the exposure apparatus 10, and may be an external configuration. A pattern to be transferred is drawn on the mask 103 . The light for illuminating the mask 103 reaches the wafer 108 through the projection optical system 105 . The wafer 108 is, for example, a silicon wafer, a glass plate, a film substrate, or the like.

The pattern on the mask 103 is transferred to a photosensitive medium (eg, a resist layer) coated on the wafer 108 via the projection optical system 105 . The wafer 108 is held by the wafer holder 107 in a state of being flattened by means of vacuum suction or the like. Further, the wafer holder 107 is held by the wafer stage 106 . The wafer table 106 is configured to be movable. Then, while the wafer stage 106 is moved two-dimensionally stepwise along a plane perpendicular to the optical axis of the projection optical system 105 , a plurality of shot areas are repeatedly exposed to the wafer 108 . This is an exposure method called step-and-repeat. In addition, there is also an exposure method called step-and-scan exposure in which the mask table 104 and the wafer table 106 are scanned and exposed while synchronizing, and the present embodiment can also be applied to an exposure apparatus employing such a method.

In the exposure apparatus 10 , the wafers 108 before exposure processing are set in the exposure apparatus in a state of being placed in the pod 110 . At least one generally stores a plurality of wafers 108 in the wafer cassette 110 . Then, one wafer 108 is taken out from the pod 110 and placed in the pre-alignment unit 109 by a robot arm or the like (not shown). After the pre-alignment unit 109 performs azimuth alignment and position alignment of the wafers 108, the wafers 108 are set on the wafer holder 107 through the robot arm and subjected to exposure processing. The wafer 108 after the exposure process is removed from the wafer holder 107 by the robot arm is recovered to the wafer cassette 110 , while the next wafer 108 waiting in the pre-alignment unit 109 is set in the wafer holder 107 . In this way, the wafers 108 are successively exposed to light. Alternatively, the exposure apparatus 10 may be connected in series with other apparatuses such as a coating apparatus (not shown) and a developing apparatus (not shown), and the wafer 108 before exposure processing may be carried in from another apparatus, and the exposure processing may be performed. The subsequent wafer 108 is carried out to another apparatus.

The control unit 111 is an information processing device such as a computer, and performs control of each unit, equipment, etc. of the exposure apparatus 10 and various calculations. In addition, in the example of FIG. 3, although the control unit 111 is comprised by one, the control unit 111 may be comprised not limited to one, and may have a some control unit 111 according to the unit of the exposure apparatus 10, or apparatus.

The host computer 11 is an information processing apparatus connected to the exposure apparatus 10 via a network or the like, and monitors and controls the exposure apparatus 10 . Moreover, the host computer 11 is also connected to the apparatuses other than the exposure apparatus 10, and monitors and controls other manufacturing apparatuses etc. similarly. For example, the host computer 11 executes a job for instructing the exposure apparatus 10 to operate.

4 : is a figure which shows the structure of the temperature control system incorporated in the exposure apparatus 10. FIG. In FIG. 4, the arrow 42 of a thick line shows the direction of a refrigerant|coolant circulation, and the arrow 43 of a thin line shows the direction of the information transmission about control. The temperature regulation 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 can be used as chambers in the exposure apparatus 10 , for example. In addition, the number of blocks is not limited to two, and the blocks can also be divided into one or a plurality of units. In this case, when it is difficult to provide a chamber for one or a plurality of units, a container for storing one or a plurality of units may be used.

In the first block 40 , the temperature of the refrigerant can be regulated, and the temperature-regulated refrigerant can be supplied to the second block 41 . In addition, in the second block 41, a plurality of object units 416-419 can be arranged. The plurality of object units 416 to 419 may 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 temperature of the refrigerant adjusted in the first block 40 is adjusted in the second block 41 while taking heat from one or a plurality of target units, and then the temperature of the one or more target units can be adjusted, and then returned to the first block 40 .

The first block 40 may include, for example, a temperature adjustment unit (a control object unit) 401 , a temperature adjustment unit 402 , a sensor 401T, a sensor 402T, a control unit 401C, and a control unit 402C. The temperature adjustment unit 401 can lower the temperature of the refrigerant to the target temperature and supply it to the temperature adjustment unit 402 . The control unit 401C determines a command value so that the temperature of the refrigerant is equal to the target temperature according to the temperature measured by the sensor 401T, and inputs the command value to the temperature adjustment unit 401 for control. Then, the temperature adjustment unit 401 operates with an operation amount corresponding to the command value.

In addition, the temperature adjustment unit 402 can adjust the temperature of the refrigerant within the allowable temperature range of the second block 41 to supply the refrigerant to the second block 41 . The control unit 402C can determine the command value according to the temperature measured by the sensor 402T in such a way that the temperature of the refrigerant falls within the allowable temperature range of the second block 41 , so as to make the temperature adjustment unit 402 operate according to the movement amount of the command value. action.

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

The control unit 411C can determine the command value according to the temperature measured by the sensor 411T1 and the information from the control units 414C and 415C in such a way that the temperature of the refrigerant falls within the target temperature range, so that the action amount corresponding to the command value can be used. The temperature adjustment unit 411 operates. That is, the control unit 414C can determine the command value according to the temperature measured by the sensors 414T1 and 414T2 so that the target unit 418 falls within the target temperature range, and actuate the temperature adjusting unit 414 according to the command value. The control unit 415C can determine the command value according to the temperature measured by the sensors 415T1 and 415T2 so that the target unit 419 falls within the target temperature range, so as to make the temperature adjustment unit 415 operate according to the action amount of the command value.

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

In addition, the refrigerant circulating through the piping may be liquid or gas.

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

Here, a model representing the relationship between the output values of the respective sensors will be described. Here, for simplicity, in the temperature regulation system 301 shown in FIG. 4 , the output values of the two sensors (eg, the sensors 401T and 402T) at time _{t are at and b t .} The relationship between the _output values at and _bt can be defined by a model (function) given by the formula (1).

b _t = _f (at )・・・(1) The model f can be, for example, a regression model determined by the least squares method based on the time-series data of the output values at and b _t output by two sensors _. Mode. In addition, the model f can also be, for example, a learning model generated using machine learning. For example, model f may be a model comprising a neural network. A neural network is a model of a multi-layer network structure including an input layer, an intermediate layer, and an output layer. From the time-series data of the output values at and b _t output by the two sensors _, learning data showing the relationship between at as input data and b _t as teacher data _is obtained. Then, a learning model can be obtained by optimizing the connection weighting coefficient and the like inside the neural network according to an algorithm such as an error back-propagation method using the acquired learning data. The error back-propagation method is a method of adjusting the connection weighting coefficient and the like between the nodes of each neural network so that the difference between the output data and the teacher data becomes smaller. In addition, the model f may not be a model including a neural network, for example, a learning model including an SVM (Support Vector Machine).

The model f _ij (x _j ) given the output value (hereinafter, the predicted output value) x _ij of the sensor S _i as a function of the output value (hereinafter, the measured output value) x _j of the sensor S _j can be given by It is given by formula (2). Here, i is an integer ranging from 1 to N, and N is the number of sensors. j is an integer other than i among 1 to N.

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

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 ) _· · The evaluation value _detects abnormalities related to the sensor Si. The evaluation value is, for example, a value obtained by processing the difference between each of the plurality of prediction output values x _ij and the measurement output value x _i corresponding thereto, and may be, for example, a total value obtained by summing the differences with the number of models. Normalized value. Further, the evaluation value may be, for example, a value calculated from a difference or ratio of a statistical value such as an average value of a plurality of predicted output values x _ij , a median value, and the like, and a measured output value x _i . Then, when the evaluation value does not fall within the preset allowable range, the occurrence of abnormality is detected in the output value of the sensor S _i .

Here, an example of using a model representing the relationship between the output values of the respective sensors will be described, but a model representing the relationship between the command values of the respective control units may also be used. For example, in the temperature regulation 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 be used. That is, the model g _ij (y _j ) given to the command value (hereinafter, the predicted command value) y _ij of the control unit C _i as a function of the command value (hereinafter, the measured command value) y _j of the control unit C _j can be is given in formula (3).

y _ij =g _ij (y _j )・・・(3) Further, an evaluation value is calculated from the predicted command value y _ij of the control unit C _i and the measured command value y _j of the control unit C _i , and the control-critical value is detected based on the evaluation value. Abnormality of cell C _i . In addition, instead of the command value in the control unit, the operation amount of the temperature regulation unit controlled by the control unit may be used (hereinafter, the command value or the operation amount is referred to as control data).

In addition, for example, a model representing the relationship between the output value of each sensor and the command value in each control unit may be used. For example, in the temperature regulation system 301 shown in FIG. 4 , a model representing the relationship between the output value of the sensor 401T and the command value of the control unit 401C at time t may be used. That is, the model h _ij (x _j ) of the command value (hereinafter, the predicted command value) y _ij given to the control unit C _i is a function of the output value (hereinafter, the measured output value) x _j of the sensor S _j , can be given in formula (4).

y _ij =h _ij (x _j )・・・(4) In addition, an evaluation value is calculated from the predicted command value y _ij of the control unit C _i and the measured output value x _j of the sensor S _j , and the relationship is detected based on the evaluation value. Abnormality of control unit C _i . Furthermore, an evaluation value is calculated from the predicted output value x _ij of the sensor S _i and the measurement command value y _j of the control unit C _i , and the model h is generated so as to detect an abnormality related to the sensor S _i from the evaluation value.

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

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

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

In addition, for example, when the target unit 417 includes the projection optical system 105, the temperature of the target unit 417 increases due to the heat of the irradiated exposure light while the exposure apparatus 10 is performing the exposure process. For example, when the target unit 419 includes the substrate stage 6 , the temperature of the target unit 419 increases due to heat generated by the driving of the drive unit of the substrate stage 6 during the exposure process of the exposure apparatus 10 . In addition, when the exposure process of the exposure apparatus 10 is completed, the exposure light is not irradiated, and the drive unit of the target unit 419 is stopped, so the temperatures of the target units 417 and 419 are lowered. In this way, even if the temperature of the target unit is controlled by different temperature control units, in the exposure process of the exposure apparatus 10, the actions of the target unit are linked, so that the output value of each sensor is correlated. Then, for example, when the temperature adjustment unit 413 related to the target unit 417 is abnormal, the abnormality is detected based on the evaluation value calculated from the model related to the sensors 413T1 and 413T2 and the control unit 413C. In addition, since there is a correlation between the temperatures of the target units 417 and 419, an 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 adjustment unit in which the abnormality occurs is the temperature adjustment unit 413 , the abnormality is also detected from the sensor and the control unit related to the temperature adjustment unit 415 .

In this way, since abnormality is detected based on the evaluation value calculated from the model corresponding to a plurality of temperature regulation units, it becomes difficult to identify the temperature regulation unit in which the abnormality has occurred.

Therefore, the management device 12 in the present embodiment groups the evaluation values calculated from the models related to the sensor and the control unit. Then, the abnormality degree for each group is acquired based on the evaluation value belonging to the individual group, and the abnormality of the temperature regulation system 301 is detected based on the acquired abnormality degree.

FIG. 5 is a flowchart illustrating a method for detecting an abnormality of the temperature regulation system in this embodiment. In S501 , the obtaining unit 211 obtains the output value of the sensor in the temperature regulation system 301 and the information related to the control data of the control unit, and the calculating unit 213 generates a sensor representing the sensor according to the obtained output value and the information related to the control data. A model of the relationship between the output value of the controller, etc. Here, the generated model can be defined as a model generated using the output value of the sensor in the temperature regulation system 301 and the control data of the control unit. In addition, the obtained model can be determined as 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. at least one of the models.

In S502, the obtaining unit 211 obtains the output value of the sensor in the temperature regulation system 301 and the information related to the control data of the control unit. Then, the calculation unit 213 calculates the output value of the sensor and the evaluation value of the control data related to each control unit using the output value, the information related to the control data, and the calculated model.

In S503, the calculating part 213 calculates the abnormality degree by group based on the information related to the grouping of the sensor and the control unit. The degree of abnormality by group is a value indicating the degree of abnormality of the output value of the sensor belonging to the group or the control data of the control unit. In addition, the abnormality degree by group can be determined as a value of statistical processing such as dividing the evaluation values obtained from the generated model into groups and summing the evaluation values belonging to the individual groups, or an average value.

Here, the grouped information is pre-stored in the memory device 204 , and the management device 12 can obtain the grouped 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 the sensor and the control unit will be described later.

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

Next, the grouping information obtained by the management device 12 in S503 will be described in detail with each embodiment.

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

In addition, only sensors can be grouped as shown in FIG. 6(b). In FIG. 6( b ), the sensor 401T and the sensor 402T included in the first block 40 in FIG. 4 belong to groups 1-3. In addition, the sensors 411T, 412T1 ˜ 415T1 , 412T2 ˜ 415T2 included in the second block 41 in FIG. 4 belong to the groups 1-4.

In addition, as shown in FIG. 6( c ), only the control unit may be grouped. In FIG. 6( c ), the control unit 401C and the control unit 402C included in the first block 40 in FIG. 4 belong to groups 1-5. In addition, the control units 411C to 415C included in the second block 41 in FIG. 4 belong to the groups 1-6.

In addition, the groupings of Figs. 6(a) to (c) may be arbitrarily combined. For example, group 1-1 in FIG. 6(a) and group 1-4 in FIG. 6(b) may also be combined.

Through such grouping, the management device 12 can determine that an abnormality has occurred in either the temperature adjustment unit in the first block 40 and the temperature adjustment unit in the second block 41 .

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

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

Through such grouping, the management device 12 can determine that there is an abnormality in any one of the plurality of temperature control units.

(Example 3) In Embodiment 3, it is an example of grouping according to a group (hereinafter, referred to as a control group) indicating the range of information related to control. FIG. 8 is a diagram illustrating an example of grouping in this embodiment. For example, the sensor 401T and the control unit 401C of the temperature adjustment unit 401 in FIG. 4 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. Also, for example, sensors 411T, 414T1, 414T2, control units 411C, and 414C belong to 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 FIG. 8, although the groups 3-3 and 3-4 are other control groups, the information of the control data of the control units 414C and 415C is transmitted to the control unit 411C, so the group 3-3 can also be used It is the same control group as 3-4.

In addition, as in the first embodiment, only the sensor, only the control unit, or any combination of the sensor and the control unit can be used for grouping.

Through such grouping, the management device 12 can determine that an abnormality has occurred in any temperature adjustment unit belonging to the control group.

(Example 4) In Example 4, it is an example of grouping by the piping of a refrigerant circulation. FIG. 9 is a diagram illustrating an example of grouping in this embodiment. In the example of FIG.9(a), the sensor and the control unit of the temperature control unit arrange|positioned in the piping branched downstream of the temperature control unit 402 in the direction of a refrigerant|coolant circulation belong to group 4-1, 4-2. In addition, the sensor and the control unit of the temperature adjustment unit arranged in the piping branched downstream of the temperature adjustment unit 411 in the direction of the refrigerant circulation belong to the 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 in the arrangement target unit 416 belong to the group 4-1. Specifically, sensors 402T, 412T1, 412T2, control units 402C, and 412C belong to group 4-1. In addition, the temperature control units 402, 411, and 414 in FIG. 4 and the sensors and control units of the piping in the arrangement target unit 418 belong to the group 4-3. Specifically, sensors 402T, 411T, 414T1, 414T2, control units 402C, 411C, and 414C belong to group 4-3. In addition, the temperature regulation units 411 and 415 in FIG. 4, and the sensor and the control unit of the piping in the arrangement|positioning 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 addition, in the example of FIG. 9( b ), the sensor 402T and the control unit 402C of the temperature adjustment unit 402 belong to groups 4-5. In addition, the sensor and the control unit of the temperature adjustment unit at the position on the piping downstream of the temperature adjustment unit 402 in the refrigerant circulation direction belong to the group 4-5. In addition, the sensor 411T and the control unit 411C of the temperature adjustment unit 411 belong to the group 4-6. In addition, the sensor and the control unit of the temperature adjustment unit arranged downstream in the piping through which the refrigerant flows with respect to the temperature adjustment unit 411 belong to the group 4-6.

In addition, in the present embodiment, although all the sensors and control units of the temperature regulation unit arranged in the position on the downstream piping in the direction of refrigerant circulation are grouped into groups, it is also possible to group only Some sensors and control units are objects. 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 the first embodiment, only the sensor, only the control unit, or any combination of the sensor and the control unit can be used for grouping.

By such grouping, the management device 12 can determine that the piping for circulating the refrigerant has an abnormality in any of the temperature control units arranged in the branched piping.

From the above, in the management device according to the present embodiment, the abnormality degree for each group can be calculated, and the sensor or control unit of the abnormal group can be specified, which is advantageous for detecting the abnormality of the temperature regulation system.

<Second Embodiment> Next, the management device 12 according to the second embodiment will be described. In addition, the matters not mentioned here can conform to the first embodiment.

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 group-by-group based on the evaluation value calculated by the model belonging to the individual group. abnormality.

FIG. 10 is a flowchart illustrating a method for detecting an abnormality of the temperature regulation system in this embodiment. In S1001, the obtaining unit 211 obtains information related to the output value of the sensor and the control data of the control unit by group, and the generating unit 212 generates the information indicating the sensor by group according to the information related to the obtained output value and the control data. A model of the relationship between the output value of the controller, etc. Here, the obtained model can be determined as a model generated in the temperature regulation system 301 using the output values of the sensors and the control data of the control units grouped by the sensors and the control units. In addition, the example of grouping can be adopted in the same manner as in Examples 1 to 4 in the first embodiment. In addition, the information of the grouping is stored in the memory device 204 in advance, and the obtaining unit 211 can obtain the information of the grouping from the memory device 204 . In addition, the obtaining unit 211 can also obtain the information of the packet from an external information processing device via the communication device 207 .

In S1002, the obtaining unit 211 obtains the output value of the sensor in the temperature regulation system 301 and the information related to the control data of the control unit. Then, the calculation unit 213 calculates the output value of the sensor by the group and the evaluation value of the control data related to the control unit by using the output value, the information concerning the control data, and the model by the calculated group.

In S1003, the calculating part 213 calculates the abnormality degree by group based on the evaluation value calculated using the model by group. The degree of abnormality by group can be determined as a value of statistical processing such as summing the evaluation values obtained from the models belonging to the group or the average value.

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

From the above, in the management device according to the present embodiment, the abnormality degree by group is calculated, and the sensor or the control unit of the abnormal group can be specified, which is advantageous for detecting the abnormality of the temperature regulation system.

(Production method of the article) In terms of articles, for example, methods for manufacturing devices (semiconductor devices, magnetic memory media, liquid crystal display elements, etc.), color filters, or hard disks will be described. Such a manufacturing method includes a process of forming a pattern on a substrate (wafer, glass plate, film substrate, etc.) using a photolithography apparatus (eg, exposure apparatus, imprint apparatus, drawing apparatus, etc.). Such a manufacturing method further includes a process of processing the patterned substrate. The processing step may include a step of removing the residual film of the pattern. Moreover, other well-known processes, such as the process of etching a board|substrate using this pattern as a mask, may be included. The manufacturing method of the article according to the present embodiment is more advantageous to at least one of the performance, quality, productivity, and production cost of the article than conventionally.

Although preferred embodiments of the present invention have been described above, it goes without saying that the present invention is 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, an advantageous technique is provided for detecting an abnormality of a control system including a plurality of sensors and a plurality of control units. [Other Embodiments]

Embodiments of the present invention can also be implemented by a computer and a method of a system or device that reads and executes the recording on a storage medium (also more fully referred to as a "non-transitory computer-readable storage medium") computer-executable instructions (eg, one or more programs) to implement the functions of one or more of the foregoing embodiments, and/or include one or more circuits for implementing the functions of one or more of the foregoing embodiments (eg, application-specific Integrated circuit (ASIC)), the method is implemented by a computer of the system or device by, for example, reading and executing computer-executable instructions from a storage medium to implement the functions of one or more of the foregoing embodiments and/or to control one or more circuits The functionality of one or more of the preceding embodiments is thus implemented. The computer may include more than one processor (eg, a central processing unit (CPU), a microprocessor unit (MPU)) and may include a separate computer or a network of separate processors to read and execute computer-executable instructions. Computer-executable instructions may be provided to the computer from, for example, a network or storage medium. The storage medium may include, for example, one or more hard disks, random access memory (RAM), read only memory (ROM), storage for distributed computing systems, optical disks (such as compact discs, digital versatile discs (DVD), or Blu-ray discs (BD) ^(TM) ), flash memory devices, memory cards, etc. Although the present invention is described with reference to the embodiments, it should be understood that the present invention is not limited to the disclosed embodiments. The claimed scope should be accorded the broadest interpretation so as to encompass various modifications and equivalent structures and functions. This case claims the priority of Japanese Patent Application No. 2020-071692 filed on April 13, 2020, the entire contents of which are incorporated herein 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: The direction of communication of control-critical information 101: Light source unit 102: Lighting system 103: Mask 104: Masking Table 105: Projection Optical System 106: Wafer table 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: Busbar 211: Acquire Department 212: Generation Department 213: Calculation Department 214: Judgment Department 301: Thermostat system 401: Thermostat unit 402: Thermostat unit 411: Thermostat unit 412: Thermostat unit 414: Thermostat unit 415: Thermostat unit 416: Object Unit 417: Object Unit 418: Object Unit 419: Object Unit 401C: Control Unit 401T: Sensor 402C: Control Unit 402T: Sensor 411C: 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

1 is a diagram showing the configuration of a substrate processing system. [ Fig. 2] Fig. 2 is a diagram illustrating a configuration of a management device. [ Fig. 3] Fig. 3 is a diagram illustrating the configuration of an exposure apparatus and a host computer. It is a figure which shows the structure of the temperature control system incorporated in an exposure apparatus. [ Fig. 5] Fig. 5 is a flowchart showing a method of detecting an abnormality of the temperature regulation system in the first embodiment. [ Fig. 6] Fig. 6 is a diagram illustrating an example of grouping in Embodiment 1. [Fig. 7 is a diagram illustrating an example of grouping in the second embodiment. 8 is a diagram illustrating an example of grouping in the third embodiment. 9 is a diagram illustrating an example of grouping in the fourth embodiment. [ Fig. 10] Fig. 10 is a flowchart showing a method of detecting an abnormality of the temperature regulation system in the second embodiment.

40: Block 1

41: Block 2

42: Direction of refrigerant circulation

43: The direction of communication of control-critical information

301: Thermostat system

401: Thermostat unit

401C: Control Unit

401T: Sensor

402: Thermostat unit

402C: Control Unit

402T: Sensor

411: Thermostat unit

411C: Control Unit

411T: Sensor

412: Thermostat unit

412C: Control Unit

412T1: Sensor

412T2: Sensor

413: Thermostat unit

413C: Control Unit

413T1: Sensor

413T2: Sensor

414: Thermostat unit

414C: Control Unit

414T1: Sensor

414T2: Sensor

415: Thermostat 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 abnormality of a control system including a plurality of sensors and a plurality of control units, It has: A calculation unit that uses a relationship representing 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 the plurality of sensors The model of the relationship between the output value of one sensor in the control unit and the control data of one control unit among the plurality of control units will represent the difference between the output value of the sensor or the degree of abnormality of the control data of the control unit. The degree of abnormality is calculated by dividing the plurality of sensors or the plurality of control units into at least two groups; and A determination unit that determines the abnormality of the plurality of sensors or the plurality of control units by the group based on the abnormality degree calculated by the calculation unit. The information processing apparatus according to claim 1, comprising a generation unit that generates the model based on the output values of the plurality of sensors or the control data of the plurality of control units. The information processing apparatus of claim 1, wherein the plurality of sensors or the plurality of control units are divided into the aforementioned groups according to chambers of the substrate processing apparatus having the aforementioned control system. The information processing device of claim 1, wherein the plurality of sensors or the plurality of control units are divided into the above-mentioned according to the output value of the above-mentioned sensor or the range of the information transmission related to the control data of the above-mentioned control unit group. The information processing device of claim 1, wherein, The aforementioned control system is a temperature regulation system that adjusts the temperature of the target unit, The plurality of sensors or the plurality of control units are divided into the above-mentioned groups according to the temperature-adjusting unit or the above-mentioned object unit controlled by the above-mentioned control unit. The information processing device of claim 1, wherein, The above-mentioned control system is a temperature control system that adjusts the temperature of the target unit by circulating the refrigerant through the piping, The plurality of sensors or the plurality of control units are divided into the above-mentioned groups according to the information of the branches of the pipes related to the circulation of the refrigerant in the above-mentioned temperature regulation system. The information processing device of claim 1, wherein, The calculation unit divides the plurality of evaluation values calculated using the model into groups based on the information of the groups, and calculates the abnormality degree for each of the groups. The information processing apparatus according to claim 1, wherein the calculation unit calculates the degree of abnormality for each group based on a plurality of evaluation values calculated using the model grouped according to the information of the group. The information processing device of claim 1, wherein the sensor includes a sensor for measuring temperature, flow or pressure. The information processing device of 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 operating according to the command value. A detection method for detecting abnormality of a control system having a plurality of sensors and a plurality of control units, It has: A calculation procedure is performed using the relationship representing the output values of two sensors among the plurality of sensors, the relationship between the control data of the two control units among the plurality of control units, or the relationship between the plurality of sensors The model of the relationship between the output value of one sensor among the sensors and the control data of one control unit among the plurality of control units will represent the degree of abnormality of the output value of the sensor or the control data of the control unit The degree of abnormality is calculated according to the above-mentioned plurality of sensors or the above-mentioned plurality of control units 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 by the group according to the abnormality degree calculated in the calculation program. A non-transitory computer-readable storage medium, which memorizes 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, The aforementioned detection method has: A calculation procedure is performed using the relationship representing the output values of two sensors among the plurality of sensors, the relationship between the control data of the two control units among the plurality of control units, or the relationship between the plurality of sensors The model of the relationship between the output value of one sensor among the sensors and the control data of one control unit among the plurality of control units will represent the degree of abnormality of the output value of the sensor or the control data of the control unit The degree of abnormality is calculated according to the above-mentioned plurality of sensors or the above-mentioned plurality of control units 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 by the group according to the abnormality degree calculated in the calculation program. A substrate processing system having: a substrate processing apparatus having a control system including a plurality of sensors and a plurality of control units and processing a substrate, and A management device for detecting abnormality of the aforementioned control system, The aforementioned management device has: A calculation unit that uses a relationship representing 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 the plurality of sensors The model of the relationship between the output value of one sensor in the control unit and the control data of one control unit among the plurality of control units will represent the difference between the output value of the sensor or the degree of abnormality of the control data of the control unit. The degree of abnormality is calculated by dividing the plurality of sensors or the plurality of control units into at least two groups; and A determination unit that determines the abnormality of the plurality of sensors or the plurality of control units by the group based on the abnormality degree calculated by the calculation unit. A method of manufacturing an article, comprising: a program for processing a substrate using a substrate processing system, and A process of making an article from a substrate processed in the preceding process, The aforementioned substrate processing system has: a substrate processing apparatus having a control system including a plurality of sensors and a plurality of control units and processing a substrate, and A management device for detecting abnormality of the aforementioned control system, The aforementioned management device has: A calculation unit that uses a relationship representing 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 the plurality of sensors The model of the relationship between the output value of one sensor in the control unit and the control data of one control unit among the plurality of control units will represent the difference between the output value of the sensor or the degree of abnormality of the control data of the control unit. The degree of abnormality is calculated by dividing the plurality of sensors or the plurality of control units into at least two groups; and A determination unit that determines the abnormality of the plurality of sensors or the plurality of control units by the group based on the abnormality degree calculated by the calculation unit.

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