Patent Publication Number: US-2021166120-A1

Title: Abnormality detecting device and abnormality detecting method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application is based on and claims priority to Japanese Patent Application No. 2019-217438 filed on Nov. 29, 2019, the entire contents of which is incorporated herein by reference. 
     TECHNICAL FIELD 
     The present disclosure relates to an abnormality detecting device, an abnormality detecting method, and an abnormality detecting computer program product. 
     BACKGROUND 
     Conventionally, in the field of various manufacturing processes, an abnormality detection technique is known, which detects abnormalities that have occurred during various manufacturing processes from measurement data (sets of multiple types of time series data, hereinafter referred to as a “time series data set”) measured during processing of an object. 
     For example, in a semiconductor manufacturing process, by using a pre-generated abnormality detection model, time series data sets measured during wafer processing are monitored, to determine occurrence of any abnormalities, degree of such abnormalities, and the like. 
     RELATED ART DOCUMENT 
     Patent Document 
     [Patent Document 1] Japanese Laid-open Patent Application Publication No. 2011-100211 
     SUMMARY 
     The present disclosure provides an abnormality detecting device, an abnormality detecting method, and an abnormality detecting computer program product capable of implementing a high-precision abnormality detecting process. 
     An abnormality detection device according to one aspect of the present disclosure includes a processor, and a non-transitory computer readable medium that has stored therein a computer program that, when executed by the processor, configures the processor to acquire one or more time series data sets measured along with processing of an object at a predetermined unit of process in a manufacturing process, and apply the one or more time series data sets in a neural network to develop a trained model. The neural network includes a plurality of network sections each configured to process the acquired time series data sets, and a concatenation section configured to combine output data output from each of the plurality of network sections as a result of processing the acquired time series data sets, and to output, as a combined result, a result of combining the output data output from each of the plurality of network sections. The computer program further configures the processor to compare the combined result with an abnormality level to train the trained model such that the combined result output from the concatenation section progressively approaches information indicating the abnormality level. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a first diagram illustrating an example of an overall configuration of a system including a device for performing a semiconductor manufacturing process and the abnormality detecting device; 
         FIGS. 2A and 2B  are diagrams each illustrating an example of a predetermined unit of process in the semiconductor manufacturing process; 
         FIG. 3  is another diagram illustrating examples of the predetermined unit of process in the semiconductor manufacturing process; 
         FIG. 4  is a diagram illustrating an example of the hardware configuration of the abnormality detecting device; 
         FIG. 5  is a first diagram illustrating an example of training data; 
         FIGS. 6A and 6B  are diagrams illustrating examples of time series data sets; 
         FIG. 7  is a first diagram illustrating an example of the functional configuration of a training unit; 
         FIG. 8  is a first diagram illustrating a specific example of processing performed in a branch section; 
         FIG. 9  is a second diagram illustrating a specific example of the processing performed in the branch section; 
         FIG. 10  is a third diagram illustrating a specific example of the processing performed in the branch section; 
         FIG. 11  is a diagram illustrating a specific example of processing performed by a normalizing unit included in each network section; 
         FIG. 12  is a fourth diagram illustrating a specific example of the processing performed in the branch section; 
         FIG. 13  is a first diagram illustrating an example of the functional configuration of an inference unit; 
         FIG. 14  is a first flowchart illustrating a flow of an abnormality detecting process; 
         FIG. 15  is a second diagram illustrating an example of the overall configuration of the system including the device performing a semiconductor manufacturing process and the abnormality detecting device; 
         FIG. 16  is a second diagram illustrating an example of the training data; 
         FIG. 17  is a diagram illustrating an example of optical emission spectrometer (OES) data; 
         FIG. 18  is a diagram illustrating a specific example of processing performed by normalizing units included in the respective network sections into which OES data is input; 
         FIGS. 19A and 19B  are diagrams illustrating specific examples of processing of each of the normalizing units; 
         FIG. 20  is a diagram illustrating a specific example of processing performed by pooling units; 
         FIG. 21  is a second diagram illustrating an example of the functional configuration of the inference unit; and 
         FIG. 22  is a second flowchart illustrating the flow of the abnormality detecting process. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments will be described with reference to the drawings. For substantially the same components in the present specification and drawings, overlapping descriptions are omitted by giving the same reference numerals. 
     First Embodiment 
     &lt;Overall Configuration of the System Including the Semiconductor Manufacturing Process and the Abnormality Detecting Device&gt; 
     First, the overall configuration of a manufacturing process (a semiconductor manufacturing process in the present embodiment) and a system including an abnormality detecting device will be described.  FIG. 1  is a first diagram illustrating an example of an overall configuration of the system including a device for performing a semiconductor manufacturing process and the abnormality detecting device. As illustrated in  FIG. 1 , the system  100  includes a device for performing a semiconductor manufacturing process, time series data acquiring devices  140 _ 1  to  140 _ n , and the abnormality detecting device  160 . 
     In the semiconductor manufacturing process, an object (e.g., wafer before processing  110 ) is processed at a predetermined unit of process  120  to produce a result (e.g., wafer after processing  130 ). The unit of process  120  described herein is a specialized term related to a particular semiconductor manufacturing process performing in a processing chamber, and details will be described below. Also, a wafer before processing  110  refers to a wafer (substrate) before being processed at the chamber(s) that perform unit of process  120 , and wafer after processing  130  refers to a wafer (substrate) after being processed in the chamber (s) that that perform the unit of process  120 . 
     The time series data acquiring devices  140 _ 1  to  140 _ n  each acquire time series data measured along with processing of the wafer before processing  110  at the unit of process  120 . The time series data acquiring devices  140 _ 1  to  140 _ n  each measure different properties. It should be noted that the number of measurement items that each of the time series data acquiring devices  140 _ 1  to  140 _ n  measures may be one, or more than one. The time series data measured in accordance with the processing of the wafer before processing  110  includes not only time series data measured during the processing of the wafer before processing  110  but also time series data measured during pre-processing or post-processing of the wafer before processing  110 . These processes may include pre-processing and post-processing performed without a wafer (substrate). 
     The time series data sets acquired by the time series data acquiring devices  140 _ 1  to  140 _ n  are stored in a training data storage unit  163  (a non-transitory memory device) in the abnormality detecting device  160 , as training data (input data in the training data). 
     Information indicating a degree of abnormality (may also be referred to as an “abnormality level”), which is acquired during processing of the wafer before processing  110  at the unit of process  120 , is stored in the training data storage unit  163  in the abnormality detecting device  160 , as training data (correct answer data in the training data, or ground truth data) in association with the time series data sets. 
     An abnormality detecting program (code that is executed on a processor to implement the algorithms discussed herein) is installed in the abnormality detecting device  160 . By executing the error detecting program, the abnormality detecting device  160  functions as a training unit  161  and an inference unit  162 . 
     The training unit  161  performs machine learning using the training data (time series data sets acquired by the time series data acquiring devices  140 _ 1  to  140 _ n  and information indicating the abnormality level associated with the time series data sets). Specifically, the training unit  161  processes the time series data sets, which are input data, using multiple network sections, and performs machine learning with respect to the network sections such that a combined result of output data obtained from each of the network sections approaches the information indicating the abnormality level, which is the correct answer data. 
     The inference unit  162  inputs time series data sets acquired by the time series data acquiring devices  140 _ 1  to  140 _ n  in accordance with processing of a new object (e.g., wafer before processing) in the unit of process  120 , to multiple network sections to which machine learning has been applied. Accordingly, the inference unit  162  infers (outputs) information indicating a degree of abnormality based on the time series data sets acquired in accordance with the processing of the new wafer. 
     As described above, by processing time series data sets acquired along with processing of an object at the predetermined unit of process  120  in the semiconductor manufacturing process, by using multiple network sections, it is possible to analyze the time series data sets acquired in the predetermined unit of process in a multifaceted manner. As a result, according to the abnormality detecting device  160 , a model (inference unit  162 ) for realizing an abnormality detecting process with high-precision can be generated, as compared to a configuration in which a single network section is used. Once trained by the training unit  161 , the interference unit embodies a learned model that is able to accurately identify an anomaly level for an object yet to be processed, and thus can be used to control/adjust semiconductor manufacturing equipment to accommodate the anomaly level for the object yet to be processed and reliably produce an object despite the anomaly level associated with the object to be processed, and the process steps used to make the produced object. While the term “unit” is used herein for devices such as the training unit and the inference unit, it should be understood that the term “circuitry” may be used as well (e.g., “training circuitry” or “inference circuitry”). This is because the circuit device(s) that execute the operations implemented as software code and/or logic operations are configured by the software code and/or logic operations to execute the algorithms described herein. 
     &lt;Predetermined “Unit of Process” in Semiconductor Manufacturing Process&gt; 
     Next, the predetermined unit of process  120  in the semiconductor manufacturing process will be described.  FIGS. 2A and 2B  are diagrams each illustrating an example of a predetermined unit of process in the semiconductor manufacturing process. As illustrated in  FIG. 2A or 2B , a semiconductor manufacturing device  200 , which is an example of a substrate processing apparatus, includes multiple chambers. Each of the chambers is an example of a processing space. In the example of  FIG. 2 , the semiconductor manufacturing device  200  includes chambers A to C, and wafers are processed in each of the chambers A to C. 
       FIG. 2A  illustrates a case in which processes performed in the multiple chambers are respectively defined as a unit of process  120 . Wafers are processed in the chamber A, the chamber B, and the chamber C, in sequence. In this case, a wafer before processing  110  ( FIG. 1 ) refers to a wafer before being processed in the chamber A, and a wafer after processing  130  refers to a wafer after being processed in the chamber C. 
     Time series data sets measured in accordance with processing of the wafer before processing  110  in the unit of process  120  of  FIG. 2A  include: 
     a time series data set output in accordance with a wafer process performed in the chamber A (first processing space), 
     a time series data set output in accordance with a wafer process performed in the chamber B (second processing space), and 
     a time series data set output in accordance with a wafer process performed in the chamber C (third processing space). 
     Meanwhile,  FIG. 2B  illustrates a case in which a process performed in a single chamber (in the example of  FIG. 2B , the “chamber B”) is defined as a unit of process  120 . In this case, a wafer before processing  110  refers to a wafer that has been processed in the chamber A and that is to be processed in the chamber B, and a wafer after processing  130  refers to a wafer that has been processed in the chamber B and is to be processed in the chamber C. 
     Further, in reference to  FIG. 2B , time series data sets measured along with processing of the wafer before processing  110  ( FIG. 1 ) include time series data set measured in accordance with processing of the wafer before processing  110  ( FIG. 1 ) performed in the chamber B. 
       FIG. 3  is another diagram illustrating examples of the predetermined unit of process in the semiconductor manufacturing process. Similar to  FIG. 2A or 2B , the semiconductor manufacturing device  200  includes multiple chambers, in each of which a different type of treatment is applied to wafers. However, in another embodiment, the same type of treatment may be applied to wafers in at least two chambers in the multiple chambers. 
     A time-diagram (a) of  FIG. 3  illustrates a case in which a process (called “wafer processing”) excluding preprocessing and post-processing among processes performed in the chamber B is defined as a unit of process  120 . In this case, a wafer before processing  110  ( FIG. 1 ) refers to a wafer before the wafer processing is performed (after the pre-processing is performed), and a wafer after processing  130  ( FIG. 1 ) refers to a wafer after the wafer processing is performed (before the post-processing is performed). 
     In the unit of process  120  of the time-diagram (a) in  FIG. 3 , time series data sets measured along with processing of the wafer before processing  110  include time series data sets measured along with the wafer processing of the wafer before processing  110  performed in the chamber B. 
     The time-diagram (a) in  FIG. 3  illustrates a case in which preprocessing, wafer processing (this process), and post-processing are performed in the same chamber (chamber B) and in which the wafer processing is defined as the unit of process  120 . However, in a case in which each of the processing is performed in a different chamber, (e.g., a case in which the preprocessing, the wafer processing, and the post-processing are performed in the chambers A, B, and C, respectively) processing performed in the chamber B may be defined as a unit of process  120 . Alternatively, in another embodiment, processing performed in the chamber A or C may be defined as a unit of process  120 . Thus, it should be understood that a unit of process may be a process performed solely in one chamber, or a process performed sequentially in more than one chambers. 
     In contrast, a time-diagram (b) of  FIG. 3  illustrates a case in which processing according to one process recipe (“process recipe III” in the example of the time-diagram (b)) included in wafer processing, among processes performed in the chamber B, is defined as a unit of process  120 . In this case, a wafer before processing  110  refers to a wafer before a process according to the process recipe III is applied (and after a process according to the process recipe II has been applied). A wafer after processing  130  refers to a wafer after a process according to the process recipe III has been applied (and before a process according to the process recipe IV (not illustrated) is applied). 
     Further, in the unit of process  120  of the time-diagram (b) in  FIG. 3 , time series data sets measured along with processing of the wafer before processing  110  include time series data sets measured during the processing according to the process recipe III performed in the chamber B. 
     &lt;Hardware Configuration of Abnormality Detecting Device&gt; 
     Next, the hardware configuration of the abnormality detecting device  160  will be described.  FIG. 4  is a diagram illustrating an example of the hardware configuration of the abnormality detecting device  160 . As illustrated in  FIG. 4 , the abnormality detecting device  160  includes a CPU (Central Processing Unit)  401 , a ROM (Read Only Memory)  402 , and a RAM (Random Access Memory)  403 . The abnormality detecting device  160  also includes a GPU (Graphics Processing Unit)  404 . Processors (processing circuitry) such as the CPU  401  and the GPU  404 , and memories such as the ROM  402  and the RAM  403  constitute a so-called computer, wherein the processors (circuitry) may be configured by software to execute the algorithms described herein. 
     The abnormality detecting device  160  further includes an auxiliary storage device  405 , a display device  406 , an operating device  407 , an interface (I/F) device  408 , and a drive device  409 . Each hardware element in the abnormality detecting device  160  is connected to each other via a bus  410 . 
     The CPU  401  is an arithmetic operation processing device that executes various programs (e.g., abnormality detecting program) installed in the auxiliary storage device  405 . 
     The ROM  402  is a non-volatile memory that functions as a main memory unit. The ROM  402  stores programs and data required for the CPU  401  executing the various programs installed in the auxiliary storage device  405 . Specifically, the ROM  402  stores a boot program such as BIOS (Basic Input/Output System) or EFI (Extensible Firmware Interface). 
     The RAM  403  is a volatile memory, such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory), and functions as a main memory unit. The RAM  403  provides a work area on which the various programs installed in the auxiliary storage device  405  are loaded when the various programs are executed by the CPU  401 . 
     The GPU  404  is an arithmetic operation processing device for image processing. When the CPU  401  executes the abnormality detecting program, the GPU  404  performs high-speed calculation of various image data (i.e., the time series data sets in the present embodiment) by using parallel processing. The GPU  404  includes an internal memory (GPU memory) to temporarily retain information needed to perform parallel processing of the various image data. 
     The auxiliary storage device  405  stores the various programs and various data used when the various programs are executed by the CPU  401 . For example, the training data storage unit  163  is implemented by the auxiliary storage device  405 . 
     The display device  406  displays an internal state of the abnormality detecting device  160 . The operating device  407  is an input device used by an administrator of the abnormality detecting device  160  when the administrator inputs various instructions to the abnormality detecting device  160 . The I/F device  408  is a connecting device for connecting and communicating with a network (not illustrated). 
     The drive device  409  is a device into which a recording medium  420  is loaded. Examples of the recording medium  420  include a medium for optically, electrically, or magnetically recording information, such as a CD-ROM, a flexible disk, and a magneto-optical disk. In addition, examples of the recording medium  420  may include a semiconductor memory or the like that electrically records information, such as a ROM, and a flash memory. 
     The various programs (computer executable code) installed in the auxiliary storage device  405  are installed when, for example, a recording medium  420  distributed is loaded into the drive device  409  and the various programs recorded in the recording medium  420  are read out by the drive device  409 . Alternatively, the various programs installed in the auxiliary storage device  405  may be installed by being downloaded via a network (not illustrated). 
     &lt;Example of Training Data&gt; 
     Next, training data that is read out from the training data storage unit  163  when the training unit  161  performs machine learning will be described.  FIG. 5  is a first diagram illustrating an example of the training data. As illustrated in  FIG. 5 , the training data  500  includes “APPARATUS”, “RECIPE TYPE”, “TIME SERIES DATA SET”, and “ABNORMALITY LEVEL” as items of information. Here, a case in which the predetermined unit of process  120  is a process according to one process recipe will be described. 
     The “APPARATUS” field stores an identifier indicating a semiconductor manufacturing device (e.g., semiconductor manufacturing device  200 ) to be monitored as to whether abnormality occurs. The “RECIPE TYPE” field stores an identifier (e.g., process recipe I) indicating a process recipe, which is performed when a corresponding time series data set is measured, among process recipes performed in the corresponding semiconductor manufacturing device (e.g., EqA). 
     The “TIME SERIES DATA SET” field stores time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n  when processing according to the process recipe indicated by the “RECIPE TYPE” is performed in the semiconductor manufacturing device indicated by the “APPARATUS”. 
     The “ABNORMALITY LEVEL” field stores information indicating a degree of abnormality (numerical representation of the degree of abnormality), which is acquired when the corresponding time series data sets (for example, time series data set 1) are measured by the time series data acquiring devices  140 _ 1  to  140 _ n . Incidentally, in the example of  FIG. 5 , as the information indicating the degree of abnormality, one of “LEVEL 0”, “LEVEL 1”, “LEVEL 2”, “LEVEL 3” is stored. 
     &lt;Example of Time Series Data Set&gt; 
     Next, specific examples of the time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n  will be described.  FIGS. 6A and 6B  are diagrams illustrating examples of the time series data sets. In the example of  FIGS. 6A and 6B , to simplify the explanation, each of the time series data acquiring devices  140 _ 1  to  140 _ n  measures one-dimensional data. However, at least one of the time series data acquiring devices  140 _ 1  to  140 _ n  may measure two-dimensional data (set of multiple types of one-dimensional data). 
       FIG. 6A  represents time series data sets in which the unit of process  120  is as illustrated in any of  FIG. 2B , the diagram (a) of  FIG. 3 , and the diagram (b) of  FIG. 3 . In this case, each of the time series data acquiring devices  140 _ 1  to  140 _ n  acquires time series data measured during processing of a wafer before processing  110  in the chamber B. Each of the time series data acquiring devices  140 _ 1  to  140 _ n  acquires time series data measured within the same time frame as the time series data set. 
     In contrast,  FIG. 6B  represents time series data sets when the unit of process  120  is as illustrated in  FIG. 2A . In this case, the time series data acquiring devices  140 _ 1  to  140 _ 3  acquire, for example, the time series data set 1 measured along with processing of a wafer before processing in the chamber A. The time series data acquiring device  140 _ n− 2, acquires, for example, the time series data set 2 measured along with processing of the wafer in the chamber B. The time series data acquiring devices  140 _ n− 1   and  140 _ n  acquire the time series data set 3, which is measured along with processing of the wafer in the chamber C, for example. 
       FIG. 6A  illustrates a case in which each of the time series data acquiring devices  140 _ 1  to  140 _ n  acquires, as the time series data set, time series data measured along with the processing of the wafer before processing in the chamber B during the same time frame. However, each of the time series data acquiring devices  140 _ 1  to  140 _ n  may acquire, as the time series data sets, multiple sets of time series data each measured during a different range of time along with processes of a wafer before processing performed in the chamber B. 
     Specifically, the time series data acquiring devices  140 _ 1  to  140 _ n  may acquire time series data measured during preprocessing, as the time series data set 1. The time series data acquiring devices  140 _ 1  to  140 _ n  may acquire time series data measured during wafer processing, as the time series data set 2. Further, the time series data acquiring devices  140 _ 1  to  140 _ n  may acquire time series data measured during post-processing, as the time series data set 3. 
     Alternatively, the time series data acquiring devices  140 _ 1  to  140 _ n  may acquire time series data measured during processing in accordance with the process recipe I, as the time series data set  1 . The time series data acquiring devices  140 _ 1  to  140 _ n  may acquire time series data measured during processing in accordance with the process recipe II, as the time series data set 2. Further, the time series data acquiring devices  140 _ 1  to  140 _ n  may acquire time series data measured during processing in accordance with the process recipe III, as the time series data set 3. 
     &lt;Functional Configuration of Training Unit&gt; 
     Next, the functional configuration of the training unit  161  will be described.  FIG. 7  is a first diagram illustrating an example of the functional configuration of the training unit  161 . The training unit  161  includes a branch section  710 , multiple network sections including a first network section  720 _ 1 , a second network section  720 _ 2 , . . . , and an M-th network section  720 _M, a concatenation section  730 , and a comparing section  740 . 
     The branch section  710  is an example of an acquisition unit, and reads out the time series data sets from the training data storage unit  163 . The branch section  710  processes the time series data sets so that the time series data sets that are read out from the training data storage unit  163  are processed by the network sections of the first network section  720 _ 1  to the M-th network section  720 _M. 
     The first to M-th network sections ( 720 _ 1  to  720 _M) are configured based on a convolutional neural network (CNN), which include multiple layers. 
     Specifically, the first network section  720 _ 1  has a first layer  720 _ 11 , a second layer  720 _ 12 , . . . , and an N-th layer  720 _ 1 N. Similarly, the second network section  720 _ 2  has a first layer  720 _ 21 , a second layer  720 _ 22 , . . . , and an N-th layer  720 _ 2 N. Other network sections are also configured similarly. For example, the M-th network section  720 _M has a first layer  720 _M 1 , a second layer  720 _M 2 , . . . , and an N-th layer  720 _MN. 
     Each of the first to N-th layers ( 720 _ 11  to  720 _ 1 N) in the first network section  720 _ 1  performs various types of processing such as normalization processing, convolution processing, activation processing, and pooling processing. Similar types of processing are performed at each of the layers in the second to M-th network sections ( 720 _ 2  to  720 _M). 
     The concatenation section  730  combines each output data output from the N-th layers ( 720 _ 1 N to  720 _MN) of the first to M-th network sections ( 720 _ 1  to  720 _M), and outputs a combined result to the comparing section  740 . Similar to the network sections ( 720 _ 1  to  720 _M), the concatenation section  730  may be configured to be trained by machine learning. The concatenation section  730  may be implemented as a convolutional neural network or other type of neural network. 
     The comparing section  740  compares the combined result output from the concatenation section  730 , with the information indicating the degree of abnormality (correct answer data) read out from the training data storage unit  163 , to calculate error. The training unit  161  performs machine learning with respect to the first to M-th network sections ( 720 _ 1  to  720 _M) and the concatenation section  730  by error backpropagation, such that error calculated by the comparing section  740  satisfies the predetermined condition. 
     By performing machine learning, model parameters of each of the first to M-th network sections  720 _ 1  to  720 _M and the model parameters of the concatenation section  730  are optimized to determine abnormality level for the semiconductor manufacturing processes used to produce a processed substrate. 
     &lt;Details of Processing in Each Part of the Training Unit&gt; 
     Next, details of the processing performed in each part (in particular, the branch section) of the training unit  161  will be described with reference to specific examples. 
     (1) Details of Processing (1) Performed in the Branch Section 
     First, the processing of the branch section  710  will be described in detail.  FIG. 8  is a first diagram illustrating a specific example of the processing performed in the branch section  710 . In the case illustrated in  FIG. 8 , the branch section  710  generates time series data set 1 (first time series data set) by processing the time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n  in accordance with a first criterion, and inputs the time series data set 1 into the first network section  720 _ 1 . 
     The branch section  710  also generates time series data set 2 (second time series data set) by processing the time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n  in accordance with a second criterion, and inputs the time series data set 2 into the second network section  720 _ 2 . 
     As described above, because the training unit  161  is configured such that multiple sets of data (e.g., time series data set 1 and time series data set 2 in the above-described example) are generated by processing the time series data sets in accordance with each of the different criteria (e.g., first criterion and second criterion) and that each of the multiple sets of data is processed in a different network section, and because machine learning is performed on the above-described configuration, the unit of process  120  can be analyzed in a multifaceted manner. As a result, a model (inference unit  162 ) that realizes a high inference accuracy can be generated as compared to a case in which time series data sets are processed using a single network section. 
     The example of  FIG. 8  illustrates a case in which two sets of data are generated by processing the time series data sets in accordance with each of the two types of criteria. However, more than two sets of data may be generated by processing the time series data sets in accordance with each of three or more types of criteria. Further, various types of criteria may be used for processing time series data sets. For example, if the time series data sets includes data obtained by optical emission spectroscopy, an average of intensity of light may be used as a criterion. In addition, a characteristic value of a wafer such as a film thickness of a wafer, or a characteristic value of wafers in a production lot, may be used as a criterion. Further, a value indicating a state of a chamber, such as a usage time of the chamber or the number of times of preventive maintenance, may also be used as a criterion. 
     (2) Details of Processing (2) Performed in the Branch Section 
     Next, another processing performed in the branch section  710  will be described in detail.  FIG. 9  is a second diagram illustrating a specific example of the processing performed in the branch section  710 . In the case of  FIG. 9 , the branch section  710  generates the time series data set 1 (first time series data set) and the time series data set 2 (second time series data set) by classifying the time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n  in accordance with data types. The branch section  710  inputs the generated time series data set 1 into the third network section  720 _ 3  and inputs the generated time series data set 2 into the fourth network section  720 _ 4 . 
     As described above, because the training unit  161  is configured to classify the time series data sets into multiple sets of data (e.g., time series data set 1 and time series data set 2 in the above-described example) in accordance with data type, and to process each of the multiple sets of data in a different network section, and because machine learning is performed on the above-described configuration, the unit of process  120  can be analyzed in a multifaceted manner. As a result, it is possible to generate a model (inference unit  162 ) that achieves a high inference accuracy, as compared to a case in which machine learning is performed by inputting time series data sets into a single network section. 
     In the example of  FIG. 9 , the time series data sets are grouped (classified) in accordance with differences in data type due to differences in the time series data acquiring devices  140 _ 1  to  140 _ n . For example, the time series data sets may be grouped into a data set acquired by optical emission spectroscopy and a data set acquired by mass spectrometry. However, time series data sets may be grouped in accordance with a time range for which data is acquired. For example, in a case in which the time series data sets consist of time series data measured along with processes according to multiple process recipes (e.g., process recipes I to III), the time series data sets may be grouped into three groups (e.g., time series data sets 1 to 3) according to the time ranges of the respective process recipes. Alternatively, the time series data sets may be grouped in accordance with environmental data (e.g., ambient pressure, air temperature). Further, the time series data sets may be grouped in accordance with data obtained during operations performed before or after a process of acquiring the time series data sets, such as conditioning or cleaning of a chamber. 
     (3) Details of Processing (3) Performed in the Branch Section 
     Next, yet another processing performed in the branch section  710  will be described in detail.  FIG. 10  is a third diagram illustrating a specific example of the processing performed in the branch section  710 . In the case of  FIG. 10 , the branch section  710  inputs the same time series data sets acquired by the time series data acquiring devices  140 _ 1  to  140 _ n  to each of the fifth network section  720 _ 5  and the sixth network section  720 _ 6 . In each of the fifth network section  720 _ 5  and the sixth network section  720 _ 6 , a different process (normalization process) is applied to the same time series data sets. 
       FIG. 11  is a diagram illustrating a specific example of processing performed by a normalizing unit included in each of the network sections. As illustrated in  FIG. 11 , each of the layers of the fifth network section  720 _ 5  includes a normalizing unit, a convolving unit, an activation function unit, and a pooling unit. 
     The example of  FIG. 11  illustrates a normalizing unit  1101 , a convolving unit  1102 , an activation function unit  1103 , and a pooling unit  1104  included in the first layer  720 _ 51  in the fifth network section  720   5 . 
     Among these, the normalizing unit  1101  applies a first normalization process to the time series data sets that are input from the branch section  710 , to generate the normalized time series data set 1 (first time series data set). 
     In addition, the example of  FIG. 11  also illustrates a normalizing unit  1111 , a convolving unit  1112 , an activation function unit  1113 , and a pooling unit  1114  included in the first layer  720 _ 61  in the sixth network section  720 _ 6 . 
     Among these, the normalizing unit  1111  applies a second normalization process to the time series data sets that are input from the branch section  710 , to generate the normalized time series data set 2 (second time series data set). 
     As described above, because the training unit  161  is configured to process time series data sets using multiple network sections each including a normalizing unit that performs normalization using a different method from other normalizing units, and because machine learning is performed on the above-described configuration, the unit of process  120  can be analyzed in a multifaceted manner. As a result, a model (inference unit  162 ) that achieves a high inference accuracy can be generated, as compared to a case in which a single type of normalization is applied to the time series data sets using a single network section. 
     (4) Details of Processing (4) Performed in the Branch Section 
     Next, still another processing performed in the branch section  710  will be described in detail.  FIG. 12  is a fourth diagram illustrating a specific example of the processing performed in the branch section  710 . In the example of  FIG. 12 , the branch section  710  inputs the time series data set 1 (first time series data set) measured along with processing of a wafer in the chamber A to the seventh network section  720 _ 7 , among the time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n.    
     The branch section  710  inputs the time series data set 2 (second time series data set) measured along with the processing of the wafer in the chamber B to the eighth network section  720 _ 8 , among the time series data sets measured by the time series data acquiring devices  140 _ 1  to  140 _ n.    
     As described above, because the training unit  161  is configured to process different time series data sets, each being measured along with processing in a different chamber (first processing space and second processing space), by using respective network sections, and because machine learning is performed on the above-described configuration, the unit of process  120  can be analyzed in a multifaceted manner. As a result, a model (inference unit  162 ) that achieves a high inference accuracy can be generated, as compared to a case in which each of the time series data sets is configured to be processed using a single network section. 
     &lt;Functional Configuration of Inference Unit&gt; 
     Next, the functional configuration of the inference unit  162  will be described.  FIG. 13  is a first diagram illustrating an example of the functional configuration of the inference unit  162 . As illustrated in  FIG. 13 , the inference unit  162  includes a branch section  1310 , first to M-th network sections  1320 _ 1  to  1320 _M, and a concatenation section  1330 . 
     The branch section  1310  acquires the time series data sets newly measured by the time series data acquiring devices  140 _ 1  to  140 _N after the time series data sets, which were used by the training unit  161  for machine learning, were measured. The branch section  1310  is also configured to cause the first to M-th network sections ( 1320 _ 1  to  1320 _M) to process the time series data sets acquired by the branch section  1310 . 
     The first to M-th network sections ( 1320 _ 1  to  1320 _M) are implemented, by performing machine learning in the training unit  161  to optimize model parameters of each of the layers in the first to M-th network sections ( 720 _ 1  to  720 _M). 
     The concatenation section  1330  is implemented by the concatenation section  730  whose model parameters have been optimized by performing machine learning in the training unit  161 . The concatenation section  1330  combines output data output from an N-th layer  1320 _ 1 N of the first network section  1320 _ 1  to an N-th layer  1320 _ 1 N of the M-th network section  1320 _M, to output the information indicating the degree of abnormality. 
     As described above, the inference unit  162  is generated by machine learning being performed in the training unit  161 , which analyzes the time series data sets with respect to the predetermined unit of process  120  in a multifaceted manner. Thus, the inference unit  162  can also be applied to different process recipes, different chambers, and different devices. Alternatively, the inference unit  162  can be applied to a chamber before maintenance and to the same chamber after its maintenance. That is, the inference unit  162  according to the present embodiment eliminates the need, for example, to maintain or retrain a model after maintenance of a chamber is performed, which is required in conventional systems. Moreover, the model developed in the training unit  161  may be employed in the inference unit  162  to identify processes that will likely result in abnormalities of differing levels. In turn, by applying the trained model, the control of semiconductor manufacturing equipment may be controlled to trigger supervised or automated maintenance operations on a process chamber; adjustment of at least one of a RF power system (e.g., adjustment of RF power levels and/or RF waveform)for generating plasma or a gas input and/or gas exhaust operation, supervised or automated calibration operations (e.g., gas flow and/or RF waveforms for generating plasma, supervised or automated adjustment of gas flow levels, supervised or automated replacement of components such as electrostatic chuck, which may become wasted over time, and the like. 
     &lt;Flow of Abnormality Detecting Process&gt; 
     Next, an overall flow of the abnormality detecting process performed by the abnormality detecting device  160  will be described.  FIG. 14  is a first flowchart illustrating the flow of the abnormality detecting process. 
     In step S 1401 , the training unit  161  acquires time series data sets and information indicating an abnormality level, as training data. 
     In step S 1402 , the training unit  161  performs machine learning by using the acquired training data. Of the acquired training data, the time series data sets are used as input data, and the information indicating the abnormality level is used as correct answer data. 
     In step S 1403 , the training unit  161  determines whether to continue the machine learning. If machine learning is continued by acquiring further training data (in a case of YES in step S 1403 ), the process returns to step S 1401 . Meanwhile, if the machine learning is terminated (in a case of NO in step S 1403 ), the process proceeds to step S 1404 . 
     In step S 1404 , the inference unit  162  generates the first to M-th network sections  1320 _ 1  to  1320 _M by reflecting model parameters optimized by the machine learning. 
     In step S 1405 , the inference unit  162  infers the information indicating the abnormality level by inputting time series data sets measured along with the processing of a new wafer before processing. 
     In step S 1406 , the inference unit  162  outputs a result of inference, associated with an identifier indicating a corresponding semiconductor manufacturing device, an identifier indicating a corresponding type of process recipe, and the like. 
     &lt;Summary&gt; 
     As is apparent from the above description, the abnormality detecting device according to the first embodiment performs the following steps: 
     a) acquiring time series data sets measured along with processing of an object at a predetermined unit of process in the manufacturing process, 
     b) with respect to the acquired time series data sets,
         b-1) generating a first time series data set and a second time series data set by processing the acquired time series data sets in accordance with the first and second criteria respectively, processing the first and second time series data sets by using multiple network sections, and combining output data output from each of the multiple network sections,   b-2) classifying the acquired time series data sets into multiple groups in accordance with data types or time ranges, processing the groups by using multiple network sections, and combining output data output from each of the multiple network sections, or   b-3) inputting the acquired time series data sets to multiple network sections each performing normalization based on a different method, to process the acquired time series data sets in each of the multiple network sections, and combining output data output from each of the multiple network sections,       

     c) performing machine learning with respect to the multiple network sections, such that a result of the combining of the output data output from each of the multiple network sections approaches the information indicating abnormality level obtained when processing the object at the predetermined unit of process in the manufacturing process, 
     d) processing newly obtained time series data sets, which are measured by time series data acquiring devices along with processing of a new object, by using the multiple network sections to which a result of machine learning is applied, and outputting a result of combining output data output from each of the multiple network sections as inference information indicating abnormality level. 
     As described above, because the abnormality detecting device according to the first embodiment is configured to perform machine learning by inputting time series data sets to multiple network sections, the time series data sets of a predetermined unit of process in the semiconductor manufacturing process can be analyzed in a multifaceted manner. As a result, a model that realizes a high-precision abnormality detecting process can be generated, as compared to a case in which machine learning is performed by inputting time series data sets to a single network section. 
     That is, according to the first embodiment, an abnormality detecting device capable of performing a high-precision abnormality detecting process can be provided. 
     Second Embodiment 
     In the abnormality detecting device  160  according to the first example embodiment, with respect to the configuration in which acquired time series data sets are processed using multiple network sections, four types of configurations are illustrated. The second embodiment further describes, among these four configurations, a configuration in which time series data sets are processed using multiple network sections, each including a normalizing unit that performs normalization using a different method from other normalizing units. In the following description, a case in which 
     a time series data acquiring device is an optical emission spectrometer, and 
     time series data sets are optical emission spectroscopy data (hereinafter referred to as “OES data”), which are data sets including the number, corresponding to the number of types of wavelengths, of sets of time series data of emission intensity will be described. 
     Hereinafter, the second embodiment will be described focusing on the differences from the above-described first embodiment. 
     &lt;Overall Configuration of a System Including a Device Performing a Semiconductor Manufacturing Process and an Abnormality Detecting Device&gt; 
     First, the overall configuration of a system including a device performing a semiconductor manufacturing process and an abnormality detecting device will be described, in which the time series data acquiring device in the system is an optical emission spectrometer.  FIG. 15  is a second diagram illustrating an example of the overall configuration of the system including a device performing a semiconductor manufacturing process and the abnormality detecting device. As illustrated in  FIG. 15 , the system  1500  includes a device for performing a semiconductor manufacturing process, an optical emission spectrometer  1501 , and the abnormality detecting device  160 . 
     In the system  1500  illustrated in  FIG. 15 , by using optical emission spectroscopy, the optical emission spectrometer  1501  measures OES data as time series data sets, along with processing of a wafer before processing  110  at the unit of process  120 . Part of the OES data measured by the optical emission spectrometer  1501  is stored in the training data storage unit  163  of the abnormality detecting device  160  as training data (input data) that is used when performing machine learning. 
     &lt;Example of Training Data&gt; 
     Next, the training data, which is read out from the training data storage unit  163  when the training unit  161  performs machine learning, will be described.  FIG. 16  is a second diagram illustrating an example of the training data. As illustrated in  FIG. 16 , the training data  1600  includes items of information, which are similar to those in the training data  500  illustrated in  FIG. 5 . The difference from  FIG. 5  is that the training data  1600  includes “OES DATA” as an item of information, instead of “TIME SERIES DATA SET” of  FIG. 5 , and OES data measured by the optical emission spectrometer  1501  is stored in the “OES DATA” field. 
     &lt;Specific Example of OES Data&gt; 
     Next, a specific example of the OES data measured in the optical emission spectrometer  1501  will be described.  FIG. 17  is a diagram illustrating an example of OES data. 
     In  FIG. 17 , the graph  1710  is a graph illustrating characteristics of OES data, which is of time series data sets measured by the optical emission spectrometer  1501 . The horizontal axis indicates a wafer identification number for identifying each wafer processed at the unit of process  120 . The vertical axis indicates a length of time of the OES data measured in the optical emission spectrometer  1501  along with the processing of each wafer. 
     As illustrated in the graph  1710 , the OES data measured in the optical emission spectrometer  1501  differs in length of time in each wafer to be processed. 
     In the example of  FIG. 17 , for example, OES data  1720  represents OES data measured along with the processing of a wafer before processing with wafer identification number=“745”. The vertical size (height) of the OES data  1720  depends on the wavelength range (number of wavelength components) measured in the optical emission spectrometer  1501 . In the second embodiment, the optical emission spectrometer  1501  measures emission intensity within a predetermined wavelength range. Therefore, the vertical size of the OES data  1720  is, for example, the number of types of wavelength N λ  (N λ  is a natural number representing the number of wavelength components measured by the optical emission spectrometer  1501 ) included within the predetermined wavelength range. Note that, in the present embodiment, the number of wavelength components may also be referred to as the “number of wavelengths”. 
     Meanwhile, the lateral size (width) of the OES data  1720  depends on the length of time over which optical emission spectrometer  1501  performs measurement. In the example of  FIG. 17 , the lateral size of the OES data  1720  is “LT”. 
     Thus, the OES data  1720  can be said to be a set of time series data that groups together a predetermined number of wavelengths, where there is one-dimensional time series data of a predetermined length of time for each of the wavelengths. 
     When the OES data  1720  is input to the fifth network section  720 _ 5  and the sixth network section  720 _ 6 , the branch section  710  resizes the data on a per minibatch basis, such that the data size is the same as that of the OES data of other wafer identification numbers. 
     &lt;Example of Normalization Part Processing&gt; 
     Next, a specific example of processing performed by the normalizing units in the fifth network section  720 _ 5  and the sixth network section  720 _ 6 , into each of which the OES data  1720  is input from the branch section  710 , will be described. 
       FIG. 18  is a diagram illustrating a specific example of the processing performed by the normalizing units included in the respective network sections into which OES data is input. As illustrated in  FIG. 18 , among layers included in the fifth network section  720 _ 5 , the first layer  720 _ 51  includes the normalizing unit  1101 . The normalizing unit  1101  generates normalized data (normalized OES data  1810 ) by normalizing the OES data  1720  using a first method (normalization based on an average value and a standard deviation of the emission intensity is applied with respect to the entire wavelength). 
     As illustrated in  FIG. 18 , among layers included in the sixth network section  720 _ 6 , the first layer  720 _ 61  includes the normalizing unit  1111 . The normalizing unit  1111  generates normalized data (normalized OES data  1820 ) by normalizing the OES data  1720  using a second method (normalization based on an average value and a standard deviation of the emission intensity is applied to each wavelength). 
       FIGS. 19A and 19B  are diagrams illustrating specific examples of processing of each of the normalizing units.  FIG. 19A  illustrates the processing of the normalizing unit  1101 . As illustrated in  FIG. 19A , in the normalizing unit  1101 , normalization is performed with respect to the entire wavelength using the mean and standard deviation of the emission intensity. Meanwhile,  FIG. 19B  illustrates the processing of the normalizing unit  1111 . In the normalizing unit  1111 , normalization using the average and the standard deviation of the emission intensity is applied to each wavelength. 
     Thus, even though the same OES data  1720  is used, information that will be found out from the same OES data  1720  differs depending on what is used as a reference (i.e., depending on analysis methods). The abnormality detecting device  160  according to the second embodiment causes different network sections, each of which is configured to perform different normalization, to process the same OES data  1720 . Thus, by combining multiple normalization processes, it is possible to analyze the OES data  1720  in the unit of process  120  in a multifaceted manner. As a result, a model (inference unit  162 ) that realizes the high inference accuracy can be generated, as compared to a case in which a single type of normalization process is applied to the OES data  1720  using a single network section. 
     The above-described example describes a case in which normalization is performed using an average value of emission intensity and a standard deviation of emission intensity. However, a statistical value used for normalization is not limited thereto. For example, the maximum value and a standard deviation of emission intensity may be used for normalization, or other statistics may be used. In addition, the abnormality detecting device  160  may be configured such that a user can select types of a statistical value to be used for normalization. 
     &lt;Example of Process Performed in Pooling Unit&gt; 
     Next, a specific example of the processing performed by the pooling units included in the final layer of the fifth network section  720 _ 5  and in the final layer of the sixth network section  720 _ 6  will be described.  FIG. 20  is a diagram illustrating the specific example of the processing performed by the pooling units. 
     Because data size differs between minibatches, the pooling units  1104  and  1114  included in the respective final layers of the fifth network section  720 _ 5  and the sixth network section  720 _ 6  perform pooling processes such that fixed-length data is output between minibatches (i.e., size of output data according to each minibatch becomes the same). 
       FIG. 20  is a diagram illustrating a specific example of the processing performed in the pooling units. As illustrated in  FIG. 20 , the pooling units  1104  and  1114  apply global average pooling (GAP) processing to feature data that is output from the activation function units  1103  and  1113 . 
     In  FIG. 20 , feature data  2011 _ 1  to  2011 _ m  represent feature data generated based on the OES data belonging to the minibatch  1 , and are input to the pooling unit  1104  of the N-th layer  720 _ 5 N of the fifth network section  720   5 . Each of the feature data  2011 _ 1  to  2011 _ m  represents feature data corresponding to one channel. 
     Feature data  2012 _ 1  to  2012 _ m  represent feature data generated based on the OES data belonging to the minibatch  2 , and are input to the pooling unit  1104  of the N-th layer  720 _ 5 N of the fifth network section  720   5 . Each of the feature data  2012 _ 1  to  2012 _ m  represents feature data corresponding to one channel. 
     Also, feature data  2031 _ 1  to  2031 _ m  and feature data  2032 _ 1  to  2032 _ m  are similar to the feature data  2011 _ 1  to  2011 _ m  or the feature data  2012 _ 1  to  2012 _ m . However, each of the feature data  2031 _ 1  to  2031 _ m  and  2032 _ 1  to  2032 _ m  is feature data corresponding to Nx channels. 
     Here, the pooling units  1104  and  1114  calculate an average value of feature values included in the input feature data on a per channel basis, to output the fixed-length output data. Thus, the data output from the pooling units  1104  and  1114  can have the same data size between minibatches. 
     &lt;Functional Configuration of Inference Unit&gt; 
     Next, the functional configuration of the inference unit  162  will be described.  FIG. 21  is a second diagram illustrating an example of the functional configuration of the inference unit  162 . As illustrated in  FIG. 21 , the inference unit  162  includes a branch section  1310 , a fifth network section  1320 _ 5 , a sixth network section  1320 _ 6 , and a concatenation section  1330 . 
     The branch section  1310  acquires OES data newly measured by the optical emission spectrometer  1501 , after the OES data used by the training unit  161  for machine learning, was measured. The branch section  1310  is also configured to cause both the fifth network section  1320 _ 5  and the sixth network section  1320 _ 6  to process the same acquired OES data. 
     The fifth network section  1320 _ 5  and the sixth network section  1320 _ 6  are implemented, by performing machine learning in the training unit  161  to optimize model parameters of each of the layers in the fifth network section  720 _ 5  and the sixth network section  720 _ 6 . 
     The concatenation section  1330  is implemented by the concatenation section  730  whose model parameters have been optimized by performing machine learning in the training unit  161 . The concatenation section  1330  combines output data that is output from an N-th layer  1320 _ 5 N of the fifth network section  1320 _ 5  and from an N-th layer  1320 _ 6 N of the sixth network section  1320 _ 6 , to output information indicating a degree of abnormality. 
     As described above, the inference unit  162  is generated by machine learning being performed in the training unit  161 , which analyzes the OES data with respect to the predetermined unit of process  120  in a multifaceted manner. Thus, the inference unit  162  can also be applied to different process recipes, different chambers, and different devices. Alternatively, the inference unit  162  can be applied to a chamber before maintenance and to the same chamber after its maintenance. That is, the inference unit  162  according to the present embodiment eliminates the need, for example, to maintain or retrain a model after performing maintenance on the chamber, which is required in conventional systems. 
     &lt;Flow of Abnormality Detecting Process&gt; 
     Next, an overall flow of the abnormality detecting process performed by the abnormality detecting device  160  will be described.  FIG. 22  is a second flowchart illustrating the flow of the abnormality detecting process. 
     In step S 2201 , the training unit  161  acquires OES data and information indicating an abnormality level, as training data. 
     In step S 2202 , the training unit  161  performs machine learning by using the acquired training data. Specifically, the OES data in the acquired training data is used as input data, and the information indicating the abnormality level in the acquired training data is used as correct answer data. 
     In step S 2203 , the training unit  161  determines whether to continue the machine learning. If machine learning is continued by acquiring further training data (in a case of YES in step S 2203 ), the process returns to step S 2201 . Meanwhile, if the machine learning is terminated (in a case of NO in step S 2203 ), the process proceeds to step S 2204 . 
     In step S 2204 , the inference unit  162  generates the fifth network section  1320 _ 5  and the sixth network section  1320 _ 6  by reflecting model parameters optimized by the machine learning. 
     In step S 2205 , the inference unit  162  infers the information indicating the abnormality level by inputting OES data measured by the optical emission spectrometer  1501  along with the processing of a new wafer before processing. 
     In step S 2206 , the inference unit  162  outputs a result of inference, associated with an identifier indicating a corresponding semiconductor manufacturing device, an identifier indicating a corresponding process recipe, and the like. 
     &lt;Summary&gt; 
     As is apparent from the above description, the abnormality detecting device according to the second embodiment performs the following steps: 
     acquiring OES data measured by an optical emission spectrometer, along with processing of an object at a given unit of process in a manufacturing process; 
     inputting the acquired OES data to two network sections each of which performs normalization using a different method from each other; 
     combining output data output from each of the two network sections; 
     performing machine learning with respect to the two network sections such that a result of the combining of the output data output from each of the two network sections approaches information indicating an abnormality level obtained during the processing of the object at the predetermined unit of process in the manufacturing process; 
     processing OES data measured along with processing of a new object by the optical emission spectrometer, by using the two network sections to which machine learning has been applied; and 
     inferring information indicating an abnormality level, by outputting a result of combining output data output from each of the two network sections to which machine learning has been applied. 
     As described above, because the abnormality detecting device according to the second embodiment is configured to perform machine learning by inputting OES data to two network sections, the OES data of a predetermined unit of process in the semiconductor manufacturing process can be analyzed in a multifaceted manner. As a result, a model that realizes a high-precision abnormality detecting process can be generated, as compared to a case in which machine learning is performed by inputting OES data to a single network section. 
     That is, according to the second embodiment, an abnormality detecting device capable of performing a high-precision abnormality detecting process can be provided. 
     Third Embodiment 
     In the second embodiment, as an example of a time series data acquiring device, an optical emission spectrometer is described. However, types of the time series data acquiring device applicable to the first embodiment are not limited to the optical emission spectrometer. 
     For example, examples of the time series data acquiring device described in the first embodiment may include a process data acquiring device that acquires various process data, such as temperature data, pressure data, or gas flow rate data, as one-dimensional time series data. Alternatively, the time series data acquiring device described in the first embodiment may include a radio-frequency (RF) power supply device for plasma configured to acquire various RF data, such as voltage data of the RF power supply, as one-dimensional time series data. 
     Although the above-described first and second embodiments do not mention any specific type of abnormality, any type of abnormality may be included as the type of abnormality that occurs in the unit of process  120 . Types of abnormalities that occur in the unit of process  120  may include, for example, abnormalities caused by aging and abnormalities occurring unexpectedly. 
     Examples of the abnormalities caused by aging include abnormalities occurring in the result, such as abnormalities in wafer thickness or etch rate. Examples of the abnormalities caused by aging may also include aging of parts in the semiconductor manufacturing device and aging of equipment connected to the semiconductor manufacturing device, such as abrasion of parts, abrasion of electrodes, deterioration of the equipment, and deposition of films on parts in the semiconductor manufacturing device. In addition, examples of the abnormalities caused by aging may include abnormalities caused by aging of parts in the semiconductor manufacturing device or caused by aging of equipment connected to the semiconductor manufacturing device, such as fluctuations in a gas flow rate, or abnormalities in temperature. 
     Meanwhile, examples of the abnormalities occurring unexpectedly include abnormal discharging, droplets (an event in which a large number of micron-sized particles are deposited), unstable behavior of discharging, and air or helium leakage. 
     In the above-described first and second embodiments, information indicating an abnormality level is stored as training data (correct answer data). However, instead of the information indicating the abnormality level, information indicating the presence or absence of an abnormality (i.e., result of comparing the information indicating the abnormality level with a predetermined threshold value) may be stored as training data (correct answer data). In this case, the training unit  161  performs machine learning so that output of the training unit  161  coincides with the presence or absence of an abnormality included in the training data, and the inference unit  162  infers the presence or absence of abnormality. 
     Alternatively, the training unit  161  may perform mechanical learning as described in the above-described first and second embodiments, and the inference unit  162  may be configured to convert the information indicating the abnormality level output from the concatenation section  1330  into information indicating the presence or absence of an abnormality, to output the presence or absence of an abnormality as a result of inference. 
     For example, a sigmoid function may be used for converting information indicating abnormality level to information indicating the presence or absence of an abnormality. Specifically, the inference unit  162  may input information indicating the abnormality level to a sigmoid function, and if output of the sigmoid function when inputting the information indicating the abnormality level is equal to or greater than 0.5, the inference unit  162  may output “abnormal” as the information indicating the presence or absence of an abnormality. Conversely, if the output of the sigmoid function when inputting the information indicating the abnormality level is smaller than 0.5, the inference unit  162  may output “no abnormality” as the information indicating the presence or absence of an abnormality. 
     In the above-described first and second embodiments, the training unit is described as performing machine learning using the same training data regardless of the type of abnormality. However, the method of machine learning performed by the training unit is not limited thereto, and the training unit may be configured to perform machine learning using different training data depending on the type of abnormality. Specifically, for example, the training unit may be configured to include: 
     a first set of network sections to which machine learning is applied such that information indicating the presence of an abnormality is output when abnormal discharging occurs, and 
     a second set of network sections separate from the first set of network sections, to which machine learning is applied such that information indicating the presence of an abnormality is output when droplets occur. Also, machine learning with respect to the first set of network sections may be performed by using training data different from that used for machine learning with respect to the second set of network sections. 
     Also, in the abnormality detecting device  160  according to the first and second embodiments described above, the training unit performs machine learning so that information indicating the degree of abnormality is output regardless of the types of abnormality that have occurred. 
     However, the method of machine learning performed by the training unit is not limited thereto, and the training unit may be configured to perform machine learning such that information indicating the type of abnormality is output in addition to the information indicating the degree of abnormality. Specifically, the concatenation section may be provided with the same number of output sections as number of types of abnormality. Each of the output sections may be associated with a corresponding type of abnormality, and may be configured to output a degree of certainty of occurrence of the corresponding type of abnormality. 
     Other Embodiments 
     The above-described first to third embodiments have been described such that a machine learning algorithm for each of the network sections in the training unit  161  is configured based on a convolutional neural network. However, the machine learning algorithm for each of the network sections in the training unit  161  is not limited to the convolutional neural network, and may be based on other machine learning algorithms. 
     In the first to third embodiments described above, it has been described that the abnormality detecting device  160  functions as the training unit  161  and the inference unit  162 . However, an apparatus serving as the training unit  161  needs not be integrated with an apparatus serving as the inference unit  162 , and an apparatus serving as the training unit  161  and an apparatus serving as the inference unit  162  may be provided separately. That is, the abnormality detecting device  160  may function as the training unit  161  without including the inference unit  162 , or the abnormality detecting device  160  may function as the inference unit  162  without including the training unit  161 . 
     It should be noted that the present invention is not limited to the above-described configurations, such as configurations described in the embodiments described above, or configurations combined with other elements. Configurations may be changed to an extent not departing from the spirit of the invention, and can be appropriately determined in accordance with their application forms.