Patent Publication Number: US-2023138127-A1

Title: Information processing method and information processing apparatus including acquiring a time series data group measured duirng a processing cycle for a substrate

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a bypass continuation application of international application No. PCT/JP2021/023137, having an international filing date of Jun. 18, 2021, and designating the United States, the international application being based upon and claiming the benefit of priority from Japanese Patent Application No. 2020-114579, filed on Jul. 2, 2020, the entire contents of each are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to an information processing method and an information processing apparatus. 
     BACKGROUND 
     In a manufacturing process for a semiconductor device, there is an atomic layer deposition (ALD) method in which a thin unit layer that is a substantially monomolecular layer is repeatedly stacked on a substrate by switching between a plurality of processing gases. Further, there is atomic layer etching (ALE) that repeats etching of a thin unit layer, which is almost a monomolecular layer, on a layer formed on a substrate. In the ALD and ALE, predetermined processing is performed by repeatedly executing the same processing for one substrate. 
     PATENT DOCUMENT OF THE RELATED ART 
     Patent Document 1: JP-A-2012-209593 
     SUMMARY 
     Technical Problem 
     The present disclosure provides an information processing method and an information processing apparatus capable of improving accuracy of feature value extraction of a time series data group measured during repetitive processing. 
     An information processing method according to an aspect of the present disclosure acquires a time series data group measured during a processing cycle for a substrate. The information processing method calculates a statistical value in each cycle of the processing cycle for each of time series data included in the acquired time series data group. The information processing method generates statistical data based on the calculated statistical value. The information processing method divides the generated statistical data or time series data into predetermined sections. The information processing method calculates a representative value for each section based on the divided statistical data or time series data. 
     According to the present disclosure, it is possible to improve the accuracy of feature value extraction of the time series data group measured during repetitive processing. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram illustrating an example of an information processing system according to an embodiment of the present disclosure. 
         FIG.  2    is a block diagram illustrating an example of a hardware configuration of an information processing apparatus according to an embodiment of the present disclosure. 
         FIG.  3    is a functional block diagram illustrating an example of a functional configuration of the information processing apparatus according to the embodiment of the present disclosure. 
         FIG.  4    is a diagram illustrating an example of time series data. 
         FIG.  5    is a diagram illustrating an example in which a part of the time series data is enlarged. 
         FIG.  6    is a diagram illustrating an example of calculation of statistical values from the time series data. 
         FIG.  7    is a diagram illustrating an example of a section set by Bayesian optimization. 
         FIG.  8    is a diagram illustrating an example of a relationship between representative values of sections and measurement data. 
         FIG.  9    is a diagram illustrating an example of a case where the representative values of sections of statistical data are obtained from the time series data. 
         FIG.  10    is a diagram illustrating an example by comparison with the related art in detection of abnormality of a process. 
         FIG.  11    is a flowchart illustrating an example of feature value extraction processing according to the present embodiment. 
         FIG.  12    is a flowchart illustrating an example of prediction processing according to the present embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, embodiments of an information processing method and an information processing apparatus disclosed herein will be described in detail with reference to the drawings. The disclosed technology is not limited to the following embodiments. 
     In a process of repeatedly performing processing such as the ALD and ALE, a cycle of injecting processing gas, reacting by inputting energy such as heat, and purging the processing gas is repeated hundreds of times in a short time, and therefore, time series data representing tick of a process is greatly increased. Therefore, cycles of similar tendency are repeated very finely for the time series data, and thus, it is difficult to extract a portion that contributes to important features such as a defect or performance of a process even when the time series data is referred to as it is. For example, in Patent Document 1, when a sub-recipe is repeatedly executed, feature values are extracted from the time series data by using data of a specific number of times out of execution times of the sub-recipe. However, the extracted feature values are not the feature values for the entire repetitive processing. Accordingly, in a case where similar cycles are repeated several hundred times, it is difficult to extract feature values that accurately reflect a processing state. Further, it is difficult to determine how many times processing data is used for the processing that is repeatedly performed several hundred times. That is, because accuracy of feature value extraction is low, deep knowledge and time are required to perform setting regarding the process. Therefore, it is expected to improve the accuracy of feature value extraction of a time series data group measured during the repetitive processing. 
     Configuration of Information Processing System  1   
       FIG.  1    is a block diagram illustrating an example of an information processing system according to an embodiment of the present disclosure. An information processing system  1  illustrated in  FIG.  1    includes a substrate processing apparatus  10 , a result data acquisition device  20 , and an information processing apparatus  100 . The substrate processing apparatus  10 , the result data acquisition device  20 , and the information processing apparatus  100  may each be plural. The substrate processing apparatus  10  is, for example, a layer formation device or an etching device configured to perform a process of atomic layer deposition (ALD) or atomic layer etching (ALE) on a substrate (a semiconductor wafer, hereinafter referred to as a wafer) of a processing target. The substrate processing apparatus  10  performs process processing on a wafer and transmits a time series data group measured during the processing to the information processing apparatus  100 . 
     The result data acquisition device  20  performs a predetermined inspection (for example, a layer formation rate) on the substrate in which the processing is ended in the substrate processing apparatus  10  so as to acquire result data. The result data acquisition device  20  transmits the acquired result data to the information processing apparatus  100  as data for model generation. 
     The information processing apparatus  100  receives the time series data group from the substrate processing apparatus  10  and receives the result data from the result data acquisition device  20 . Based on various types of information of the received time series data group and so on, the information processing apparatus  100  extracts feature values and generates a model for outputting a prediction result regarding a result of a process. Further, the information processing apparatus  100  receives a new time series data group from the substrate processing apparatus  10  and outputs the prediction result regarding the result of the process in the substrate processing apparatus  10  based on the received new time series data group. The prediction result includes, for example, abnormality detection information of a process, and various types of prediction information on a wafer or a substrate processing apparatus. 
     Hardware Configuration of Information Processing Apparatus  100   
       FIG.  2    is a block diagram illustrating an example of a hardware configuration of an information processing apparatus according to an embodiment of the present disclosure. As illustrated in  FIG.  2   , the information processing apparatus  100  includes circuitry such as a central processing unit (CPU)  101 , a read only memory (ROM)  102 , and a random access memory (RAM)  103 . A processor (a processing circuit or processing circuitry) such as the CPU  101 , and a memory such as the ROM  102  and the RAM  103  constitute a so-called computer. 
     Furthermore, the information processing apparatus  100  includes an auxiliary storage device  104 , a display device  105 , an operation device  106 , an interface (UF) device  107 , and a drive device  108 . The devices of hardware in the information processing apparatus  100  are connected to each other via a bus  109 . 
     The CPU  101  is an arithmetic device that executes various programs (for example, a prediction program and the like) installed in the auxiliary storage device  104 . 
     The ROM  102  is a nonvolatile memory, and serves as a main memory device. The ROM  102  stores various types of programs, data, and the like necessary for the CPU  101  to execute the various types of programs installed in the auxiliary storage device  104 . Specifically, the ROM  102  stores boot programs and the like such as BIOS (basic input/output system) and EFI (extensible firmware interface). 
     The RAM  103  is a volatile memory such as a DRAM (dynamic random access memory) and an SRAM (static random access memory), and serves as a main memory device. The RAM  103  provides a work area to which the various types of programs installed in the auxiliary storage device  104  are loaded when executed by the CPU  101 . 
     The auxiliary storage device  104  stores various types of programs, and stores various types of data and the like used when the various types of programs are executed by the CPU  101 . For example, a time series data group storage to be described below is implemented in the auxiliary storage device  104 . 
     The display device  105  is a display device that displays an internal state of the information processing apparatus  100 . The operation device  106  is an input device used when a manager of the information processing apparatus  100  inputs various types of instructions to the information processing apparatus  100 . The I/F device  107  is a connection device for connecting to, and communicating with, a network (not shown). 
     The drive device  108  is a device to which a recording medium  110  is set. Here, the recording medium  110  includes a medium for optically, electrically, or magnetically recording information, such as a CD-ROM, a flexible disk, a magneto-optical disk, or the like. The recording medium  110  may also include a semiconductor memory or the like that electrically records information, such as a ROM, a flash memory, or the like. 
     The various types of programs to be installed in the auxiliary storage device  104  are installed by the drive device  108  reading the various types of programs recorded in the recording medium  110  upon the recording medium  110  being supplied and set in the drive device  108 , for example. Alternatively, the various types of program to be installed in the auxiliary storage device  104  may be installed upon being downloaded via a network. 
     Functional Configuration of Information Processing Apparatus  100   
       FIG.  3    is a functional block diagram illustrating an example of a functional configuration of the information processing apparatus according to the embodiment of the present disclosure. The information processing apparatus  100  includes a storage  220  and a controller  230 . 
     The storage  220  is implemented by, for example, the RAM  103 , a semiconductor memory element such as a flash memory, and a storage device such as a hard disk or an optical disk. The storage unit includes a time series data group storage  221  and a result data storage  222 . Further, the storage  220  stores information used for processing at the controller  230 . The controller  230  is implemented by, for example, the CPU  101 . 
     The time series data group storage  221  stores respective time series data groups measured in a process of performing a processing cycle for a plurality of wafers in the substrate processing apparatus  10 . As the time series data included in the time series data group, the time series data group storage  221  stores information, for example, a voltage (RF Vpp) of a high frequency power supply of the substrate processing apparatus  10 .  FIG.  4    is a diagram illustrating an example of time series data. As an example of time series data, a graph  150  illustrated in  FIG.  4    is a graph of a voltage of the high frequency power supply according to time elapse of a process, that is, the processing cycle. 
       FIG.  5    is a diagram illustrating an example in which a part of the time series data is enlarged. A graph  151  illustrated in  FIG.  5    is a partially enlarged graph of the graph  150  illustrated in  FIG.  4   . As illustrated in the graph  151 , it turns out that the voltage of the high frequency power supply repeats a cycle with a peak. The time series data groups respectively corresponding to wafers are stored in the time series data group storage  221  in association with a wafer number. 
     Referring back to  FIG.  3   , the result data storage  222  stores result data regarding a result of a process for each wafer. As the result data, for example, various types of measurement data can be used such as measurement data like a layer thickness regarding performance of a wafer in which a process is completed. The data input from the operation device  106  or the I/F device  107  is stored as the result data. 
     The storage  220  additionally stores statistical data, information of a section, a model, and the like. The statistical data is data obtained by arranging in time series a statistical value in each cycle of the processing cycle calculated for each of time series data. That is, a trend of the entire time series data can be easily grasped by the statistical data. The information of the section is information for dividing the statistical data or time series data into predetermined sections. In the division of the sections, features of a process can be accurately grasped by adjusting a manner of the division. Further, accuracy of the model can be improved by using a representative value based on the statistical data or time series data of an appropriately divided section. The model is generated by performing multivariate analysis or machine learning based on the statistical data or time series data. In the generating of the model, the result data may be used. The model is generated by using, for example, a Mahalanobis distance based on  36  of a normal distribution of data. For example, in a case of abnormality detection, a model that detects abnormality can be used when the Mahalanobis distance continuously exceeds a threshold. Further, as the model, another model such as a linear regression model generated by using partial least squares (PLS) regression may be used. 
     The controller  230  is implemented when, for example, the CPU  101 , a micro processing unit (MPU), a graphics processing unit (GPU) (graphics processor), or the like execute a program stored in an internal storage device by using the RAM  103  as a work area. Further, for example, the controller  230  may be implemented by an integrated circuit such as an application specific integrated circuit (ASIC) or a FIELD programmable gate array (FPGA). 
     The controller  230  includes an acquirer  231 , a first calculator  232 , a first generator  233 , a divider  234 , a second calculator  235 , a second generator  236 , and a predictor  237 , and implements or executes a function and an operation of information processing to be described below. An internal configuration of the controller  230  is not limited to the configuration illustrated in  FIG.  3    and may be another configuration as long as the internal configuration can perform the information processing to be described below. 
     In a case of feature value extraction processing, the acquirer  231  acquires respective time series data groups corresponding to respective wafers from the substrate processing apparatus  10 . Further, the acquirer  231  may acquire result data of process processing of a substrate such as inspection data from the result data acquisition device  20 . Furthermore, in a case of prediction processing, the acquirer  231  acquires a time series data group corresponding to a new wafer to be predicted from the substrate processing apparatus  10 . The acquirer  231  stores the acquired time series data group in the time series data group storage  221  and stores the acquired result data in the result data storage  222 . 
     By referring to the time series data group storage  221 , the first calculator  232  calculates the statistical value in each cycle of the processing cycle for each of the time series data included in the time series data group. For example, values such as an average value, a minimum value, a maximum value, a variance, and a gradient can be used as the statistical values. The first calculator  232  outputs a set of the calculated statistical values for the time series data to the first generator  233 . In the feature value extraction processing, the statistical values for the time series data are calculated in a similar manner for each of the time series data groups in a plurality of wafers. Further, in the following description, in a case where processing of each processor is performed for each time series data in the time series data groups of the plurality of wafers or each time series data in the time series data group of one wafer, one time series data will be representatively described, and descriptions on the other time series data will be omitted. Here, calculation of statistical values will be described with reference to  FIG.  6   . 
       FIG.  6    is a diagram illustrating an example of calculation of statistical values from the time series data. As illustrated in  FIG.  6   , the first calculator  232  extracts, for example, a specific cycle  152  from the graph  150  of the time series data. With respect to the extracted cycle  152 , the first calculator  232  calculates, as statistical values, values such as a maximum value  152   a , a median value  152   b , an average value  152   c , and a minimum value  152   d.    
     Further, the first calculator  232  may remove data to be excluded from the calculation of statistical values for each cycle included in the time series data. The data to be excluded can be, for example, data such as a step switching portion in the cycle. For example, the first calculator  232  may take out only a second step section  152 - 2  at a step switching timing in a cycle  152 , exclude other data, and calculate statistical values. 
     Referring back to  FIG.  3   , when the set of the statistical values for the time series data is input from the first calculator  232 , the first generator  233  generates the statistical data based on the set of the statistical values for the time series data. For example, the first generator  233  generates the statistical data corresponding to the time series data by arranging the statistical values in time series. The first generator  233  outputs the generated statistical data to the divider  234 . The first generator  233  may be integrated with the first calculator  232 . 
     When the statistical data is input from the first generator  233 , the divider  234  divides the input statistical data into one or more sections. When it is desired to avoid a decrease in accuracy due to repeated statistical processing, the divider  234  may divide time series data included in a time series data group into one or more sections by referring to the time series data group storage  221 . In the case of the feature value extraction processing, the divider  234  divides the statistical data or time series data into sections based on a manner of the division for a predetermined sections. Further, in a case where the second generator  236  instructs to change the manner of the division for the sections, the divider  234  divides the statistical data or time series data into the sections, for example, by changing a ratio of division, the number of divisions, and the like. For example, the divider  234  divides the statistical data or time series data into two sections in the first half of a process, one section in the middle part thereof, and two sections in the second half thereof. In this case, for example, a section I1 indicates a first cycle of a process, and a section I2 indicates a section from a second cycle to a 10 th  cycle on a head side. A section I3 indicates a section from an 11 th  cycle on the head side to an 11 th  cycle on a tail side. That is, the section I3 includes the majority of several hundred cycles constituting the process. A section I4 indicates a section from a second cycle to the 10 th  cycle on the tail side, and a section I5 indicates the last cycle on the tail side. In the aforementioned division of the sections, features of the process can be accurately grasped by adjusting the manner of the division. In the case of the prediction processing, the divider  234  divides the statistical data or time series data into sections based on the information of the sections stored in the storage  220 . The divider  234  outputs the divided statistical data or divided time series data to the second calculator  235 . 
     In a case where a section previously obtained by Bayesian optimization is used as the information of the sections for dividing the statistical data or time series data, the divider  234  calculates the section in advance by referring to the time series data group storage  221  and the result data storage  222 . Hereinafter, setting of the sections by Bayesian optimization will be described with reference to  FIGS.  7  and  8   . 
       FIG.  7    is a diagram illustrating an example of a section set by Bayesian optimization. As illustrated in  FIG.  7   , a time series data group  170  and a measurement data group  171  respectively have the same wafer numbers associated with each other. That is, time series data  170   a ,  170   b ,  170   c ,  170   d , . . . are respectively associated with measurement data  171   a ,  171   b ,  171   c ,  171   d , . . . Further, the time series data group  170  and the measurement data group  171  use a data group of a plurality of wafers, for example, a data group of three to several tens of wafers. The divider  234  performs Bayesian optimization by using the time series data group  170  as an explanatory function and using the measurement data group  171  as an objective function. 
     The divider  234  performs the Bayesian optimization by using, for example, a range (section) of a cycle to be extracted as a parameter. In the same manner as the second calculator  235  to be described below, the divider  234  calculates a representative value of a section. The divider  234  determines a relationship between the calculated representative value of the section and measurement data, for example, by using a determination coefficient R 2 . The determination coefficient R 2  ranges from 0 to 1. 
       FIG.  8    is a diagram illustrating an example of a relationship between representative values of sections and measurement data. In the example of a graph  173  illustrated in  FIG.  8   , it is assumed that the determination coefficient R 2  is 0.7955. In this case, for example, the divider  234  changes the above-described parameters to perform a search until the determination coefficient R 2  becomes 0.8 or more or until a preset number of times and calculation times are satisfied. That is, the divider  234  sets a predetermined section such that a prediction error of a model is reduced. In addition to the determination coefficient R 2 , a root mean square error (RMSE) or PLS may be used as a relationship between the representative values of the sections and the measurement data. 
     The divider  234  can obtain, as a result of Bayesian optimization, for example, a section  172  illustrated in  FIG.  7    as section information for dividing the statistical data or time series data. The divider  234  stores the calculated section  172  in the storage  220 . That is, in the example of the section  172 , the number of prediction targets in the model can be reduced from five to one, compared to the above-described sections I1 to I5. That is, search time can be shortened by using Bayesian optimization. Instead of the Bayesian optimization, the divider  234  may obtain a section for dividing the statistical data or time series data by using another parameter searching method. 
     Referring back to  FIG.  3   , when the divided statistical data or time series data is input from the divider  234 , the second calculator  235  calculates the representative value (summary) for each section based on the divided statistical data or time series data. As representative values for each section, the second calculator  235  calculates values such as an average value, a minimum value, a maximum value, a variance, and a gradient. For example, in a case where the statistical data or time series data are divided into the above-described sections I1 to I5, the second calculator  235  calculates the average value as a representative value for each of the sections I1 to I5. The second calculator  235  outputs the calculated representative value for each section to the second generator  236  in the feature value extraction processing, and outputs the representative value to the predictor  237  in the prediction processing. 
     Hereinafter, a case where the representative value of the section of the statistical data is obtained from the time series data will be described with reference to  FIG.  9   .  FIG.  9    is a diagram illustrating an example of a case where the representative values of sections of statistical data are obtained from the time series data. As illustrated in  FIG.  9   , the information processing apparatus  100  calculates statistical data  190  based on statistical values of respective cycles from the graph  150  of the time series data. Next, the information processing apparatus  100  calculates, for example, representative values  191  to  195  for sections  181  to  185  corresponding to the above-described sections I1 to I5. In the example of  FIG.  9   , the representative value  191  indicates a lower value as compared with the other representative values  192  to  195  in the statistical data  190 . This indicates that the section  181  of the representative value  191 , that is, a voltage of the high frequency power supply at a first cycle of a process is low and rise of plasma is poor. That is, a wafer with statistical data  190  is defective due to the wafer being processed in the process in which the rise of plasma is abnormal; thus, in such a case, abnormality is to be detected. 
     Referring back to  FIG.  3   , the second generator  236  receives a representative value for each section from the second calculator  235  in the feature value extraction processing. The second generator  236  performs multivariate analysis based on a representative value for each section based on the statistical data or time series data to generate the model. The model is, for example, a prediction function f(x). The prediction function f(x) is a function that employs, for example, a Mahalanobis distance, PLS regression, and the like. Further, in a case of using the result data, by referring to the result data storage  222 , the second generator  236  performs multivariate analysis based on the representative value for each section based on the statistical data or time series data and based on the result data so as to generate the model. The second generator  236  inputs, as x, the representative value for each section that is a feature value, to the generated model, that is, the prediction function f(x), and obtains y=f(x). Y represents a prediction result. With respect to the prediction result, the second generator  236  determines whether or not prediction accuracy is higher than or equal to a threshold by using an evaluation function such as RMSE. When it is determined that the prediction accuracy is not greater than or equal to the threshold, the second generator  236  instructs the divider  234  to change the manner of the division for the sections. When it is determined that the prediction accuracy is greater than or equal to the threshold, the second generator  236  stores the information of the sections and the model in the storage  220 . 
     The predictor  237  receives the representative value for each section from the second calculator  235  in the prediction processing. The predictor  237  inputs, as x, the representative value for each section that is the feature value, to the prediction function f(x) that is a model used when the feature value stored in the storage  220  is extracted, and obtains the prediction result, that is, y=f(x). The predictor  237  determines whether or not the prediction result is greater than or equal to the threshold. When it is determined that the prediction result is greater than or equal to the threshold, the predictor  237  outputs the prediction result, and executes the following preset operation, for example: a change of a set value of a recipe in the substrate processing apparatus  10 ; notifying an alarm to the substrate processing apparatus  10 ; and sending a mail to an operator. When it is determined that the prediction result is not greater than or equal to the threshold, the predictor  237  outputs the prediction result and does not execute the preset operation. 
     Depending on used models, the prediction result includes the following information: abnormality detection information of a process; prediction information regarding a result of a process; prediction information for a maintenance period of the substrate processing apparatus  10 ; correction information in a set value of the substrate processing apparatus  10 ; and correction information in a set value of a process. Further, information that classifies abnormality of a process may be output as the prediction result. The prediction result can be used for various purposes: for example, the result is stored in the storage  220  to be used for other processing of statistical processing and the like, or is transmitted to the substrate processing apparatus  10  to be used for correction in a set value. 
     Here, an example of the prediction result will be described with reference to  FIG.  10   .  FIG.  10    is a diagram illustrating an example by comparison with the related art in detection of abnormality of a process. As illustrated in  FIG.  10   , for example, in a case where the abnormality detection information of the process is used as the prediction result, the summary  197 , which is a prediction result generated in the present embodiment, can detect the occurrence of an abnormality because the second and seventh wafers are deviated from the population, as compared with the summary  196  generated from the entire conventional time series data. 
     Feature Value Extraction Method 
     Next, an operation of the information processing apparatus  100  according to the present embodiment will be described. First, the feature value extraction processing will be described with reference to  FIG.  11   .  FIG.  11    is a flowchart illustrating an example of feature value extraction processing according to the present embodiment.  FIG.  11    will be described by taking a case where the statistical data is divided into sections as an example. 
     The acquirer  231  of the information processing apparatus  100  acquires time series data groups respectively corresponding to wafers from the substrate processing apparatus  10  (step S 1 ). In a case of using result data, the acquirer  231  acquires the result data for each wafer from the result data acquisition device  20 . The acquirer  231  stores the acquired time series data group in the time series data group storage  221  and stores the acquired result data in the result data storage  222 . 
     By referring to the time series data group storage  221 , the first calculator  232  calculates statistical values in each cycle of a processing cycle for each of time series data included in a time series data group (step S 2 ). The first calculator  232  outputs a set of the calculated statistical values for the time series data to the first generator  233 . 
     When the set of the statistical values for the time series data is input from the first calculator  232 , the first generator  233  generates the statistical data based on the set of the statistical values for the time series data (step S 3 ). The first generator  233  outputs the generated statistical data to the divider  234 . 
     When the statistical data is input from the first generator  233 , the divider  234  divides the input statistical data into one or more sections (step S 4 ). The divider  234  outputs the divided statistical data to the second calculator  235 . 
     When the divided statistical data is input from the divider  234 , the second calculator  235  calculates the representative value for each section based on the divided statistical data (step S 5 ). The second calculator  235  outputs the calculated representative value for each section to the second generator  236 . 
     When the representative value for each section are input from the second calculator  235 , the second generator  236  performs multivariate analysis based on the representative value for each section to generate a model (prediction function f(x)) (step S 6 ). In a case of using result data, the second generator  236  performs multivariate analysis based on the representative value for each section and the result data by referring to the result data storage  222  to generate the model. The second generator  236  inputs the representative value (a feature value x) for each section to the generated model (f(x)) to obtain a prediction result. That is, y=f(x) is obtained (step S 7 ). 
     With respect to the prediction result, the second generator  236  determines whether or not prediction accuracy is greater than or equal to a threshold by using an evaluation function such as RMSE (step S 8 ). When it is determined that the prediction accuracy is not greater than or equal to the threshold (step S 8 : No), the second generator  236  returns to step S 4  and restarts from the section division. When it is determined that the prediction accuracy is greater than or equal to the threshold (step S 8 : Yes), the second generator  236  stores information of sections and the model in the storage  220  and ends the feature value extraction processing. In this way, the information processing apparatus  100  can improve accuracy of feature value extraction of a time series data group measured during repetitive processing. 
     Prediction Method 
     Next, the prediction processing will be described with reference to  FIG.  12   .  FIG.  12    is a flowchart illustrating an example of prediction processing according to the present embodiment.  FIG.  12    will be described by taking a case where the statistical data is divided into sections as an example. 
     The acquirer  231  of the information processing apparatus  100  acquires a time series data group corresponding to a wafer from the substrate processing apparatus  10  (step S 11 ). The acquirer  231  stores the acquired time series data group in the time series data group storage  221 . 
     By referring to the time series data group storage  221 , the first calculator  232  calculates a statistical value in each cycle of the processing cycle for each of the time series data included in the time series data group by the same manner as that in the extraction of the feature value (step S 12 ). The first calculator  232  outputs a set of the calculated statistical values for the time series data to the first generator  233 . 
     When the set of statistical values for the time series data is input from the first calculator  232 , the first generator  233  generates the statistical data based on the set of the statistical values for the time series data (step S 13 ). The first generator  233  outputs the generated statistical data to the divider  234 . 
     The divider  234  divides the input statistical data into the same sections as that in the extraction of the feature value (step S 14 ). The divider  234  outputs the divided statistical data to the second calculator  235 . 
     When the divided statistical data is input from the divider  234 , the second calculator  235  calculates the representative value for each section based on the divided statistical data by the same manner as that in the extraction of the feature value (step S 15 ). The second calculator  235  outputs the calculated representative value (the feature value x) for each section to the predictor  237 . 
     When the representative value for each section is input from the second calculator  235 , the predictor  237  inputs the representative value for each section to a model used when the feature value is extracted so as to obtain a prediction result (step S 16 ). That is, the feature value x is substituted into y=f(x). The predictor  237  determines whether or not a prediction result is greater than or equal to a threshold (step S 17 ). When it is determined that the prediction result is greater than or equal to the threshold (step S 17 : Yes), the predictor  237  executes a preset operation (step S 18 ) and ends the prediction processing. The preset operation includes: a change in the set value of the recipe in the substrate processing apparatus  10 ; notifying the substrate processing apparatus  10  of an alarm; sending a mail to an operator, and the like. Meanwhile, when it is determined that the prediction result is not greater than or equal to the threshold (step S 17 : No), the predictor  237  ends the prediction processing without performing an operation in particular. In this way, the information processing apparatus  100  can improve the accuracy of feature value extraction of a time series data group measured during repetitive processing and can perform abnormality detection, prediction, and the like by using the prediction result. 
     As described above, according to the present embodiment, the information processing apparatus  100  acquires a time series data group measured during the processing cycle for a substrate. Further, the information processing apparatus  100  calculates a statistical value in each cycle of the processing cycle for each of the time series data included in the acquired time series data group. Further, the information processing apparatus  100  generates statistical data based on the calculated statistical values. Further, the information processing apparatus  100  divides the generated statistical data or time series data into predetermined sections. Further, the information processing apparatus  100  calculates representative values for each section based on the divided statistical data or time series data. The calculated representative values represent features of a process in the processing cycle performed for a substrate. As a result, it is possible to improve the accuracy of feature value extraction of the time series data group measured during the repetitive processing. 
     Further, according to the present embodiment, the information processing apparatus  100  further acquires result data regarding a result of a process for a substrate. Further, the information processing apparatus  100  generates a model based on the calculated representative value for each section and the result data. As a result, it is possible to generate a model that improves the accuracy of feature value extraction of the time series data group measured during the repetitive processing. 
     Further, according to the present embodiment, the information processing apparatus  100  sets a predetermined section such that a prediction error of the model is reduced. As a result, it is possible to further improve the accuracy of feature value extraction of a time series data group. 
     Further, according to the present embodiment, the information processing apparatus  100  divides the statistical data or time series data into respective sections of at least a first half, a middle part, and a second half. As a result, feature values at a head and a last tail of time series data can be accurately extracted. 
     Further, according to the present embodiment, the information processing apparatus  100  obtains a section by Bayesian optimization. As a result, it is possible to obtain a section regardless of the known knowledge. 
     Further, according to the present embodiment, the information processing apparatus  100  uses at least one of multivariate analysis and neural networking. As a result, it is possible to further improve the accuracy of feature value extraction of a time series data group. 
     Further, according to the present embodiment, a statistical value is any one of an average value, a minimum value, a maximum value, a variance, and a gradient in each cycle. As a result, a feature value can be extracted according to features of time series data, and thus, accuracy can be further improved. 
     Further, according to the present embodiment, the representative value is any one of an average value, a minimum value, a maximum value, a variance, and a gradient in a predetermined section. As a result, a feature value can be extracted according to features of time series data, and thus, accuracy can be further improved. 
     Further, according to the present embodiment, the information processing apparatus  100  acquires the time series data group measured during the processing cycle performed for a new substrate. Further, the information processing apparatus  100  calculates a statistical value in each cycle of the processing cycle for each of the time series data included in the acquired time series data group. Further, the information processing apparatus  100  generates statistical data based on the calculated statistical values. Further, the information processing apparatus  100  divides the generated statistical data or time series data into predetermined sections. Further, the information processing apparatus  100  calculates representative values for each section based on the divided statistical data or time series data. Further, the information processing apparatus  100  inputs the calculated representative value for each section to a model and outputs a prediction result. As a result, prediction can be performed more accurately. 
     Further, according to the present embodiment, the prediction result is one or more of: abnormality detection information of a process; prediction information regarding a process result; prediction information for a maintenance period of the substrate processing apparatus  10 ; correction information of a set value of the substrate processing apparatus  10 ; and correction information in a set value of a process. As a result, abnormality in a process can be detected. Further, a processing plan of a wafer can be easily constructed. Further, a maintenance period of the substrate processing apparatus  10  can be easily known. Further, set values of the substrate processing apparatus  10  and a process can be corrected. 
     The embodiments disclosed herein are exemplary in all respects and can be considered to be non-restrictive. The embodiments described above may be omitted, replaced, or modified in various forms without departing from the scope and idea of the appended claims. 
     Further, in the embodiments described above, a voltage of a high frequency power supply of the substrate processing apparatus  10  is provided as one example of time series data but the present embodiment is not limited thereto. For example, information relating to performance for a wafer, such as a flow rate of processing gas and pressure in a chamber can be used as time series data. 
     Further, in the embodiment described above, a model is generated by using multivariate analysis but the present disclosure is not limited thereto. For example, according to abnormality detection, a trained model is generated by machine learning such as a convolutional neural network (CNN) by using as training data a set of a plurality of statistical data and measurement data and abnormal or normal information, and abnormality may be detected by using the generated learnt model as a model. Furthermore, abnormality detection by a trend chart focusing on one measurement data may be combined. 
     Further, in the embodiment described above, with respect to the predetermined sections that divide the statistical data, a preset case and a case obtained by Bayesian optimization are described, but the present disclosure is not limited thereto. For example, in various processes, a trained model is generated by machine learning such as CNN by using as training data a set of statistical data and a section obtained by Bayesian optimization, and a predetermined section in statistical data of a new process may be determined by using the generated learned model. 
     Further, in the embodiment described above, the information processing apparatus  100 , which acquires time series data from the substrate processing apparatus  10 , performs data processing such as the feature value extraction processing and prediction processing, but the present disclosure is not limited thereto. For example, a controller of the substrate processing apparatus  10  may perform various types of data processing such as the feature value extraction processing and prediction processing described above. 
     Further, in the embodiment described above, an example is described in which a semiconductor wafer is used as a substrate of a processing target in the substrate processing apparatus  10 , but the present disclosure is not limited thereto. For example, time series data may be acquired from a substrate processing apparatus in which a substrate such as a flat panel display (FPD) is used as processing target. 
     Furthermore, all or a certain part of various processing functions performed by each device may be performed by a CPU (or a microcontroller such as an MPU or a micro controller unit (MCU)). Further, it is needless to say that all or a certain part of various processing functions may be performed on a program analyzed and executed by a CPU (or a microcontroller such as an MPU or an MCU) or on hardware by a wired logic. 
     The present disclosure is not limited to only the above-described embodiments, which are merely exemplary. It will be appreciated by those skilled in the art that the disclosed systems and/or methods can be embodied in other specific forms without departing from the spirit of the disclosure or essential characteristics thereof. The presently disclosed embodiments are therefore considered to be illustrative and not restrictive. The disclosure is not exhaustive and should not be interpreted as limiting the claimed invention to the specific disclosed embodiments. In view of the present disclosure, one of skill in the art will understand that modifications and variations are possible in light of the above teachings or may be acquired from practicing of the disclosure. 
     Reference to an element in the singular is not intended to mean “one and only one” unless explicitly so stated, but rather “one or more.” Moreover, where a phrase similar to “at least one of A, B, or C” is used in the claims, it is intended that the phrase be interpreted to mean that A alone may be present in an embodiment, B alone may be present in an embodiment, C alone may be present in an embodiment, or that any combination of the elements A, B and C may be present in a single embodiment; for example, A and B, A and C, B and C, or A and B and C. 
     No claim element herein is to be construed under the provisions of 35 U.S.C. 112(f) unless the element is expressly recited using the phrase “means for.” As used herein, the terms “comprises,” “comprising,” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. 
     The scope of the invention is indicated by the appended claims, rather than the foregoing description.