Patent Publication Number: US-2021183051-A1

Title: Learning device, inference device, learning model generation method, and inference method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of International Application No. PCT/JP2019/032025 filed on Aug. 15, 2019, and designating the U.S., which is based upon and claims priority to Japanese Patent Application No. 2018-164930, filed on Sep. 3, 2018, the entire contents of which are incorporated herein by reference. 
    
    
     BACKGROUND 
     1. Technical Field 
     The disclosure herein relates to a learning device, an inference device, a learning model generation method, and an inference method. 
     2. Description of the Related Art 
     Semiconductor manufacturers generate physical models of respective manufacturing processes (e.g., dry etching and deposition) and perform simulations to search for optimal recipes and to adjust process parameters based on simulation results. 
     Here, in models in which trials are repeatedly performed, such as physical models, a certain amount of time is required to perform simulations. Thus, in recent years, the application of models learned by using machine learning is studied as an alternative to simulators based on physical models. 
     With respect to the above, in order to replace simulators based on physical models, it is required to reproduce simulations performed in the simulators even in the learned model. 
     The present disclosure provides a learned model to replace a simulator of a manufacturing process. 
     SUMMARY 
     According to one aspect of the present disclosure, with respect to an inference method performed by at least one processor, the method includes inputting, by the at least one processor, into a learned model, second non-processed image data and second parameter data of a simulator, and inferring, by the at least one processor using the learned model, second processed image data. The learned model has been trained so that first processed image data, obtained as an output in response to first non-processed image data and first parameter data of the simulator for the first non-processed image data being input, approaches first simulator processed image data, obtained as a result of the simulator for the first non-processed image data by using the first parameter data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a drawing illustrating an example of an overall configuration of a simulation system; 
         FIG. 2A  and  FIG. 2B  are drawings illustrating examples of hardware configurations of respective devices constituting the simulation system; 
         FIG. 3  is a drawing illustrating an example of training data; 
         FIG. 4  is a drawing illustrating an example of a functional configuration of a learning unit of a learning device according to a first embodiment; 
         FIG. 5  is a drawing illustrating an example of a functional configuration of a data shaping unit of the learning device according to the first embodiment; 
         FIG. 6  is a drawing illustrating a specific example of a process performed by the data shaping unit of the learning device according to the first embodiment; 
         FIG. 7  is a drawing illustrating a specific example of a process performed by a dry etching learning model of the learning device according to the first embodiment; 
         FIG. 8  is a flowchart illustrating a flow of a learning process in the simulation system; 
         FIG. 9  is a drawing illustrating an example of a functional configuration of an executing unit of an inference device; 
         FIG. 10A  and  FIG. 10B  are drawings illustrating the simulation accuracy of a dry etching learned model; 
         FIG. 11A  and  FIG. 11B  are drawings illustrating the simulation accuracy of a deposition learned model; 
         FIG. 12  is a drawing illustrating an example of a functional configuration of a data shaping unit of a learning device according to a second embodiment; 
         FIG. 13  is a drawing illustrating a specific example of a process performed by a dry etching learning model model of the learning device according to the second embodiment; 
         FIG. 14  is a drawing illustrating an example of a functional configuration of a learning unit of a learning device according to a third embodiment; and 
         FIG. 15A  and  FIG. 15B  are drawings illustrating an example of a functional configuration of a data shaping unit of a learning device according to a fourth embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In the following, embodiments will be described in detail with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are referenced by the same reference numerals, and overlapping description is omitted. 
     First Embodiment 
     &lt;Overall Configuration of a Simulation System&gt; 
     First, an overall configuration of a simulation system that simulates a semiconductor manufacturing process will be described.  FIG. 1  is a drawing illustrating an example of the overall configuration of the simulation system. As illustrated in FIG. a simulation system  100  includes a learning device  110 , an inference device  120 , and a simulation device  130 . 
     In the learning device  110 , a data shaping program and a learning program are installed, and when the programs are executed, the learning device  110  functions as a data shaping unit  111  and a learning unit  112 . 
     The data shaping unit  111  is an example of a processing unit. The data shaping unit  111  reads training data that is transmitted from the simulation device  130  and that is stored in a training data storage unit  113 , and processes a portion of the read training data into a predetermined format suitable for being input to a learning model by the learning unit  112 . 
     The learning unit  112  uses the read training data (including the training data processed by the data shaping unit  111 ) to perform machine learning on the learning model and generate a learned model of the semiconductor manufacturing process. The learned model generated by the learning unit  112  is provided to the inference device  120 . 
     In the inference device  120 , a data shaping program and an execution program are installed, and when the programs are executed, the inference device  120  functions as a data shaping unit  121  and an executing unit  122 . 
     The data shaping unit  121  is an example of a processing unit and obtains non-processed image data (i.e., image data of an object to be processed) and parameter data (which will be described in detail later) transmitted from the simulation device  130 . The data shaping unit  121  processes the obtained parameter data in a predetermined format suitable for being input to the learned model by the executing unit  122 . 
     The executing unit  122  inputs the non-processed image data and the parameter data processed in the predetermined format by the data shaping unit  121  into the learned model to perform a simulation, so that the executing unit  122  outputs (or infers to obtain) processed image data (i.e., a simulation result). 
     In the simulation device  130 , an execution program is installed, and when the program is executed, the simulation device  130  functions as an executing unit  131 . 
     The executing unit  131  includes a simulator (i.e., a simulator based on what is called a physical model) that performs a simulation by setting parameter data indicating a predetermined processing condition to a physical model in which a semiconductor manufacturing process is identified using a physical law or the like. The executing unit  131  reads non-processed image data from a non-processed image data storage unit  132  and performs the simulation using the simulator based on the physical model, thereby outputting processed image data (i.e., a simulation result). 
     The non-processed image data read by the executing unit  131  includes image data representing a shape of an object (e.g., a wafer) to be processed in the semiconductor manufacturing process (e.g., dry etching, deposition, and the like). The object to be processed in the semiconductor manufacturing process is composed of multiple materials, and each pixel of the non-processed image data may be represented, for example, by a pixel value in accordance with a composition ratio (or a content ratio) of each material. However, the representation of the non-processed image data is not limited to this, and may be expressed in another representation. 
     The processed image data output by the executing unit  131  executing the simulation is image data representing a shape formed after dry etching when a dry etching simulator is used. 
     The processed image data output by the executing unit  131  executing the simulation is image data representing a shape formed after deposition processing when a deposition simulator is used. 
     The object processed by the semiconductor manufacturing process is also composed of multiple materials, and each pixel of the processed image data may be represented, for example, by a pixel value in accordance with a composition ratio (or a content ratio) of each material. However, the representation of the processed image data is not limited to this. Similarly with the non-processed image data, the image data may be represented in another representation. 
     The executing unit  131  generates training data including the non-processed image data used when the simulation is executed, the corresponding parameter data, and the processed image data, and transmits the generated training data to the learning device  110 . 
     Further, the executing unit  131  transmits, to the inference device  120 , the non-processed image data, the parameter data, and the processed image data that are to be used to verify the learned model by a user of the inference device  120 . 
     The user of the inference device  120  verifies the learned model by contrasting the processed image data output by the executing unit  122  executing the simulation using the learned model with the processed image data transmitted from the executing unit  131 . 
     Specifically, the user of the inference device  120  contrasts the following simulation time periods:
     a simulation time period from when the non-processed image data and the parameter data are input to the data shaping unit  121  to when the processed image data is output from the executing unit  122  in the inference device  120     a simulation time period from when the non-processed image data is input to the executing unit  131  to when the processed image data is output from the executing unit  131  in a state in which the parameter data is set to the simulator based on the physical model in the simulation device  130  This enables the user of the inference device  120  to verify whether the simulation time period of the learned model is shorter than the simulation time period of the simulator based on the physical model.   

     The user of the inference device  120  contrasts the following data:
     the processed image data output from the executing unit  122  by inputting the non-processed image data and the parameter data to the data shaping unit  121  in the inference device  120     the processed image data output from the executing unit  131  by inputting the non-processed image data to the executing unit  131  in a state in which the parameter data is set to the simulator based on the physical model in the simulation device  130  This enables the user of the inference device  120  to verify the simulation accuracy of the learned model for the simulator based on the physical model (i.e., whether the simulation accuracy of the learned model is sufficient to replace the simulator based on the physical model).   

     When the verification is completed, given non-processed image data and given parameter data are input to the inference device  120 , and various simulations are performed. 
     &lt;Hardware Configurations of Respective Devices Constituting the Simulation System&gt; 
     Next, hardware configurations of respective devices (i.e., the learning device  110 , the inference device  120 , and the simulation device  130 ) constituting the simulation system  100  will be described with reference to  FIG. 2A  and  FIG. 2B .  FIG. 2A  and  FIG. 2B  are drawings illustrating examples of the hardware configurations of the respective devices constituting the simulation system. 
     Since the hardware configuration of the learning device  110  is substantially the same as the hardware configuration of the inference device  120 , the hardware configuration of the learning device  110  will be described here. 
     (1) Hardware Configuration of the Learning Device  110   
       FIG. 2A  is a drawing illustrating an example of the hardware configuration of the learning device  110 . As illustrated in  FIG. 2A , the learning device  110  includes a central processing unit (CPU)  201  and a read only memory (ROM)  202 . The learning device  110  also includes a random access memory (RAM)  203  and a graphics processing unit (GPU)  204 . The processor (processing circuit or processing circuitry), such as the CPU  201  and the GPU  204 , and the memory, such as the ROM  202  and the RAM  203 , form what is called a computer. 
     The learning device  110  further includes an auxiliary storage device  205 , an operating device  206 , a display device  207 , an interface (I/F) device  208 , and a drive device  209 . Each hardware component of the learning device  110  is interconnected to one another through a bus  210 . 
     The CPU  201  is an arithmetic device that executes various programs (e.g., a data shaping program, a learning program, and the like) installed in the auxiliary storage device  205 . 
     The ROM  202  is a non-volatile memory that functions as a main storage device. The ROM  202  stores various programs, data, and the like that are necessary for the CPU  201  to execute various programs installed in the auxiliary storage device  205 . Specifically, the ROM  202  stores a boot program such as Basic Input/Output System (BIOS), Extensible Firmware Interface (EFI), or the like. 
     The RAM  203  is a volatile memory such as a dynamic random access memory (DRAM) or a static random access memory (SRAM) and functions as a main storage device. The RAM  203  provides a workspace in which various programs installed in the auxiliary storage device  205  are deployed when the various programs are executed by the CPU  201 . 
     The GPU  204  is an arithmetic device for image processing. When various programs are executed by the CPU  201 , the GPU  204  performs high-speed arithmetic operations on various image data by parallel processing. 
     The auxiliary storage device  205  is a storage unit that stores various programs, various image data on which image processing is performed by the GPU  204  when various programs are executed by the CPU  201 , and the like. For example, the training data storage unit  113  is achieved by the auxiliary storage device  205 . 
     The operating device  206  is an input device used when an administrator of the learning device  110  inputs various instructions to the learning device  110 . The display device  207  is a display that displays internal information of the learning device  110 . 
     The I/F device  208  is a connection device for connecting and communicating with another device (e.g., the simulation device  130 ). 
     The drive device  209  is a device in which a recording medium  220  is set. Here, the recording medium  220  includes a medium that records information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, a magneto-optical disk, or the like. The recording medium  220  may include a semiconductor memory that electrically records information, such as a ROM, a flash memory, or the like. 
     The various programs to be installed in the auxiliary storage device  205  are installed, for example, when the distributed recording medium  220  is set in the drive device  209  and various programs recorded in the recording medium  220  are read by the drive device  209 . Alternatively, various programs to be installed in the auxiliary storage device  205  may be installed by downloading through a network, which is not illustrated. 
     (2) Hardware Configuration of the Simulation Device  130   
       FIG. 2B  is a drawing illustrating an example of the hardware configuration of the simulation device  130 . As illustrated in  FIG. 2B , since the hardware configuration of the simulation device  130  is substantially the same as that of the learning device  110 , the description will be omitted here. 
     &lt;Description of the Training Data&gt; 
     Next, the training data transmitted from the simulation device  130  and stored in the training data storage unit  113  will be described.  FIG. 3  is a drawing illustrating an example of the training data. As illustrated in  FIG. 3 , the training data  300  includes “process”, “simulation ID”, “non-processed image data”, “parameter data”, and “processed image data” as items of information. 
     In the “process”, a name indicating the semiconductor manufacturing process is stored. The example of  FIG. 3  indicates a state in which two names “dry etching” and “deposition” are stored as the “process”. 
     In the “simulation ID”, an identifier for identifying each simulation executed by the executing unit  131  using each simulator is stored. In a first embodiment, the executing unit  131  includes a dry etching simulator  310  and a deposition simulator  320 , and performs the simulation by using each of the simulators. 
     The example of  FIG. 3  indicates a state in which “S 001 ” and “S 002 ” are stored as the “simulation ID” of the simulation executed by the executing unit  131  using the dry etching simulator  310 . The example of  FIG. 3  indicates a state in which “S 101 ” is stored as the “simulation ID” of the simulation executed by the executing unit  131  using the deposition simulator  320 . 
     In the “non-processed image data”, a file name of the non-processed image data that is input by the executing unit  131  when the simulation is executed using each simulator is stored. The example of  FIG. 3  illustrates that if the simulation ID is S 001 , the simulation is executed by inputting the non-processed image data having “shape data LD 001 ” as the “file name” into the dry etching simulator  310 . The example of  FIG. 3  illustrates that if the simulation ID is S 002 , the simulation is executed by inputting the non-processed image data having “shape data LD 002 ” as the “file name” into the dry etching simulator  310 . Further, the example of  FIG. 3  illustrates that if the simulation ID is S 101 , the simulation is executed by inputting the non-processed image data having “shape data LD 101 ” as the “file name” into the deposition simulator  320 . 
     In the “parameter data”, information representing a predetermined processing condition set to each simulator when the executing unit  131  executes the simulation is stored. The example of  FIG. 3  illustrates that “parameters  001 _ 1 ”, “parameters  001 _ 2 ”, and so on have been set by the executing unit  131  when the simulation whose simulation ID is S 001  is performed using the dry etching simulator  310 . 
     Here, “Parameter  001 _ 1 ”, “Parameter  001 _ 2 ”, and so on indicate the following values:
     etch ratio of each material   etch lateral ratio of each material   etch depth of each material   

     In the “processed image data”, the file name of the processed image data output by the executing unit  131  performing the simulation using each simulator is stored. The example of  FIG. 3  illustrates that if the simulation ID is S 001 , when the simulation has been performed using the dry etching simulator  310 , the processed image data having “shape data LD 001 ′” as the “file name” is output. 
     The example of  FIG. 3  illustrates that if the simulation ID is S 002 , when the simulation has been performed using the dry etching simulator  310 , the processed image data having “shape data LD 002 ′” as the “file name” is output. 
     Further, the example of  FIG. 3  illustrates that if the simulation ID is S 101 , when the simulation has been performed using the deposition simulator  320 , the processed image data having “shape data LD 101 ′” as the “file name” is output. 
     The executing unit  131  performs the simulation by using each simulator to generate the training data  300  and transmit the training data  300  to the learning device  110 . Then, the training data  300  is stored in the training data storage unit  113  of the learning device  110 . 
     &lt;Functional Configuration of the Learning Device&gt; 
     Next, functional configurations of respective units (i.e., the data shaping unit  111  and the learning unit  112 ) of the learning device  110  will be described in detail. 
     (1) Details of the Functional Configuration of the Learning Unit 
     First, the functional configuration of the learning unit  112  of the learning device  110  will be described in detail.  FIG. 4  is a drawing illustrating an example of the functional configuration of the learning unit of the learning device according to the first embodiment. As illustrated in  FIG. 4 , the learning unit  112  of the learning device  110  includes a dry etching learning model  420 , a deposition learning model  421 , a comparing unit  430 , and a modifying unit  440 . 
     The non-processed image data and the parameter data of the training data  300  stored in the training data storage unit  113  are read by the data shaping unit  111  and are input to a corresponding learning model. In the present embodiment, the parameter data is processed into a predetermined format by the data shaping unit  111  and is input to the corresponding learning model. However, data that has been processed into the predetermined format may be read by the data shaping unit  111  and may be input to the corresponding learning model. 
     Into the dry etching learning model  420 , the non-processed image data and the parameter data processed into the predetermined format by the data shaping unit  111  (which are limited to the non-processed image data and the parameter to which the “dry etching” of the “process” is associated) are input. When the non-processed image data and the parameter data processed into the predetermined format are input, the dry etching learning model  420  outputs an output result. The dry etching learning model  420  inputs the output result to the comparing unit  430 . 
     Similarly, into the deposition learning model  421 , the non-processed image data and the parameter data processed into the predetermined format by the data shaping unit  111  (which are limited to the non-processed image data and the parameter data to which the “deposition” of the “process” is associated) are input. When the non-processed image data and the parameter data processed in the predetermined format are input, the deposition learning model  421  outputs an output result. The deposition learning model  421  inputs the output result to the comparing unit  430 . 
     The comparing unit  430  compares the output result output from the dry etching learning model  420  with the processed image data of the training data  300  (i.e., the processed image data to which “dry etching” of the “process” is associated) and notifies the modifying unit  440  of differential information. 
     Similarly, the comparing unit  430  compares the output result output from the deposition learning model  421  with the processed image data of the training data  300  (i.e., the processed image data to which the “deposition” of the “process” is associated) and notifies the modifying unit  440  of differential information. 
     The modifying unit  440  updates a model parameter of the dry etching learning model  420  or the deposition learning model  421  based on the differential information notified by the comparing unit  430 . The differential information used to update the model parameter may be a squared error or an absolute error. 
     As described above, the learning unit  112  inputs the non-processed image data and the parameter data processed in the predetermined format into the learning model and updates the model parameter by using machine learning so that the output result output from the learning model approaches the processed image data. 
     (2) Details of the Functional Configuration of the Data Shaping Unit 
     Next, the functional configuration of the data shaping unit  111  of the learning device  110  will be described in detail.  FIG. 5  is a drawing illustrating an example of the functional configuration of the data shaping unit of the learning device according to the first embodiment. As illustrated in  FIG. 5 , the data shaping unit  111  includes a shape data obtaining unit  501 , a channel data generator  502 , a parameter data obtaining unit  511 , a parameter data expanding unit  512 , and a concatenating unit  520 . 
     The shape data obtaining unit  501  reads the processed image data of the training data  300  from the training data storage unit  113  and notifies the channel data generator  502 . 
     The channel data generator  502  is an example of a generator. The channel data generator  502  obtains the non-processed image data notified by the shape data obtaining unit  501  (here, it is assumed that the image data is represented by pixel values in accordance with the composition ratio (or the content ratio) of each material). The channel data generator  502  generates image data having multiple channels corresponding to types of the materials from the obtained non-processed image data. Hereinafter, the image data having the channels corresponding to the types of the materials is called channel data. For example, the channel data generator  502  generates channel data including an air layer and four channel data respectively including four material layers from the non-processed image data. 
     The channel data generator  502  notifies the concatenating unit  520  of the generated multiple channel data. In the present embodiment, although the channel data generator  502  generates the channel data, the channel data may be previously generated. In this case, the channel data generator  502  reads the previously generated channel data and notifies the concatenating unit  520 . 
     The parameter data obtaining unit  511  reads the parameter data of the training data  300  from the training data storage unit  113  and notifies the parameter data expanding unit  512 . 
     The parameter data expanding unit  512  processes the parameter data notified from the parameter data obtaining unit  511  into a predetermined format in accordance with the size of the non-processed image data (i.e., a format of a two-dimensional array in accordance with the width and the height of the non-processed image data). 
     Here, in the parameter data, numerical values of parameters such as “parameter  001 _ 1 ”, “parameter  001 _ 2 ”, “parameter  001 _ 3 ”, and so on are arranged in one dimension. Specifically, in the parameter data, numerical values of N types of parameters are arranged in one dimension. 
     Thus, the parameter data expanding unit  512  extracts a numerical value of one of the N types of parameters included in the parameter data one by one, and arranges the extracted numerical values in two dimensions in accordance with the width and the height of the non-processed image data. As a result, the parameter data expanding unit  512  generates N parameter data respectively arranged in two dimensions. 
     The parameter data expanding unit  512  notifies the concatenating unit  520  of the N parameter data respectively arranged in two dimensions. 
     The concatenating unit  520  concatenates the N parameter data respectively arranged in two dimensions that is notified from the parameter data expanding unit  512  with the multiple channel data notified from the channel data generator  502  as new channels to generate concatenated data. In the present embodiment, the concatenating unit  520  generates the concatenated data, but the concatenated data may have been previously generated. In this case, the concatenating unit  520  reads the previously generated concatenated data and inputs the concatenated data into the learning model. 
     &lt;Specific Example of a Process Performed by Each Unit of the Learning Device&gt; 
     Next, specific examples of a process performed by the above-described data shaping unit  111  and a process performed by the dry etching learning model  420  in the learning unit  112  among the respective units of the learning device  110  will be described. 
     (1) Specific Example of the Process Performed by the Data Shaping Unit 
       FIG. 6  is a drawing illustrating a specific example of the process performed by the data shaping unit. In  FIG. 6 , the non-processed image data  600  is, for example, non-processed image data having “shape data LD 001 ” as the “file name”. 
     As illustrated in  FIG. 6 , the non-processed image data  600  includes a layer of air, a layer of a material A, a layer of a material B, a layer of a material C, and a layer of a material D. In this case, the channel data generator  502  generates channel data  601 ,  602 ,  603 ,  604 , and  605 . 
     As illustrated in  FIG. 6 , in parameter data  610 , numerical values of respective parameters (e.g., “parameter  001   1 ”, “parameter  001   2 ”, “parameter  001 _ 3 ”, and so on) are arrayed in one dimension. 
     In this case, the parameter data expanding unit  512  arrays the parameter  001 _ 1  in two dimensions (i.e., the parameter data expanding unit  512  arrays the same values vertically and horizontally) in accordance with the width and the height of the non-processed image data  600 . Similarly, the parameter data expanding unit  512  arrays the parameter  001 _ 2  in two dimensions in accordance with the width and the height of the non-processed image data  600 . Similarly, the parameter data expanding unit  512  arrays the parameter  001 _ 3  in two dimensions in accordance with the width and the height of the non-processed image data  600 . 
     Parameter data  611 ,  612 ,  613 , and so on respectively arrayed in two dimensions are concatenated by the concatenating unit  520  as new channels with the channel data  601 ,  602 ,  603 ,  604 , and  605 , and concatenated data  620  is generated. 
     (2) Specific Example of a Process Performed Using the Dry Etching Learning Model 
     Next, a specific example of a process performed using the dry etching learning model  420  in the learning unit  112  will be described.  FIG. 7  is a drawing illustrating a specific example of the process performed using the dry etching learning model of the learning device according to the first embodiment. As illustrated in  FIG. 7 , in the present embodiment, a learning model based on a U-shaped convolutional neural network (CNN) (which is what is called UNET) is used as the dry etching learning model  420 . 
     When the UNET is used, typically, image data is input and image data is output. Thus, the UNET is used as a learning model of the learning unit  112 , so that non-processed image data of the semiconductor manufacturing process can be input and the processed image data of the semiconductor manufacturing process can be output. 
     With respect to the above, when the UNET is used, data that is not in an image data format is required to be processed into an image data format. The parameter data expanding unit  512  of the data shaping unit  111  described above is configured to array the parameter data in two dimensions in order to process the data to be input to the UNET into an image data format. The parameter data can be input, so that simulation contents achieved in the simulator based on the physical model can be also achieved in the UNET. 
     The example of  FIG. 7  illustrates a state in which the concatenated data  620  is input to the dry etching learning model  420  using the UNET, and an output result  700  including multiple channel data is output. 
     Here, in the example of  FIG. 7 , a specific example of the process performed by using the dry etching learning model  420  is illustrated. However, a specific example of the process performed by using the deposition learning model  421  is substantially the same. 
     &lt;Flow of a Learning Process in the Simulation System&gt; 
     Next, a flow of a learning process in the simulation system  100  will be described.  FIG. 8  is a flowchart illustrating the flow of the learning process in the simulation system. 
     In step S 801 , the executing unit  131  of the simulation device  130  sets parameter data to the dry etching simulator  310  or the deposition simulator  320  based on an instruction of the administrator of the simulation device  130 . 
     In step S 802 , the executing unit  131  of the simulation device  130  reads the non-processed image data from the non-processed image data storage unit  132 . The executing unit  131  performs the simulation (i.e., the executing unit  131  generates the processed image data) by using the dry etching simulator  310  or the deposition simulator  320 . 
     In step S 803 , the executing unit  131  of the simulation device  130  generates the training data and transmits the training data to the learning device  110 . The training data generated by the executing unit  131  includes parameter data set when the simulation is executed, the non-processed image data input when the simulation is executed, and the processed image data generated when the simulation is executed. 
     In step S 804 , the data shaping unit  111  of the learning device  110  generates the concatenated data based on the non-processed image data and the parameter data included in the training data. 
     In step S 805 , the learning unit  112  of the learning device  110  performs machine learning on the learning model by using the concatenated data as an input and the processed image data as an output, to generate the learned model. 
     In step S 806 , the learning unit  112  of the learning device  110  transmits the generated learned model to the inference device  120 . 
     &lt;Functional Configuration of the Inference Device&gt; 
     Next, a functional configuration of the inference device  120  will be described in detail. In respective units of the inference device  120  (i.e., the data shaping unit  121  and the executing unit  122 ), the details of the functional configuration of the data shaping unit  121  are the same as the details of the functional configuration of the data shaping unit  111  of the learning device  110 . Thus, details of the functional configuration of the data shaping unit  121  will be omitted here, and details of the functional configuration of the executing unit  122  will be described below. 
       FIG. 9  is a drawing illustrating an example of the functional configuration of the executing unit of the inference device. As illustrated in  FIG. 9 , the executing unit  122  of the inference device  120  includes a dry etching learned model  920 , a deposition learned model  921 , and an output unit  930 . 
     When the non-processed image data that is not used as the training data  300  is transmitted to the inference device  120  together with the parameter data from the simulation device  130 , for example, the data shaping unit  121  generates concatenated data and inputs the concatenated data to each learned model in the executing unit  122 . The example of  FIG. 9  illustrates a state in which non-processed image data having “shape data SD 001 ”, “shape data SD 002 ”, . . . , so on as the “file name” is transmitted to the inference device  120  as the non-processed image data that is not used as the training data  300 . 
     As illustrated in  FIG. 9 , the non-processed image data (e.g., the images having “shape data SD 001 ”, “shape data SD 002 ”, . . . , and so on as the “file name”) is also input to the executing unit  131  in parallel. Then, the executing unit  131  performs the simulation by using each simulator and outputs the processed image data (e.g., the images having “shape data SD 001 ′”, “shape data SD 002 ′”, . . . , and so on as the “file name”). 
     The dry etching learned model  920  performs the simulation in response to the concatenated data generated by the data shaping unit  121  being input. The dry etching learned model  920  notifies the output unit  930  of an output result that is output by performing the simulation. 
     Similarly, the deposition learned model  921  performs the simulation in response to the concatenated data generated by the data shaping unit  121  being input. The deposition learned model  921  notifies the output unit  930  of an output result that is output by performing the simulation. 
     The output unit  930  generates processed image data (e.g., image data having “shape data SD 001 ″” as the “file name”) from the output result notified from the dry etching learned model  920  and outputs the processed image data as a simulation result. Similarly, the output unit  930  generates processed image data (e.g., image data having “shape data SD 101 ″” as the “file name”) from the output result notified from the deposition learned model  921 , and outputs the processed image data as a simulation result. 
     Here, the user of the inference device  120  can verify the simulation time period of the inference device  120  by comparing a period until the processed image data is output from the output unit  930  and a period until the processed image data is output from the executing unit  131 . The user of the inference device  120  can verify the simulation accuracy of the inference device  120  by contrasting the processed image data output from the output unit  930  and the processed image data output from the executing unit  131 . 
     According to the first embodiment, the simulation time period of each learned model in the executing unit  122  is shorter than the simulation time period of the corresponding simulator in the executing unit  131 . This is because, in a case of the simulation based on each learned model, there is no need to repeat trials while, in a case of the simulation based on each simulator, there is need to repeat trials, and high-speed computing can be performed by parallel processing of the GPU  204 . 
     Further, according to the first embodiment, each learned model in the executing unit  122  can achieve the simulation accuracy that is sufficient to replace the corresponding simulator in the executing unit  131 . This is because machine learning has been performed on each learned model by using non-processed image data input to the corresponding simulator and processed image data output from the corresponding simulator. 
       FIG. 10A  and  FIG. 10B  are drawings illustrating the simulation accuracy of the dry etching learned model.  FIG. 10A  illustrates the non-processed image data and the processed image data when the simulation is performed by using the dry etching simulator  310  of the simulation device  130  as a comparison target. 
     With respect to the above,  FIG. 10B  illustrates the non-processed image data and the processed image data when the simulation is performed by using the dry etching learned model  920  in the inference device  120 . 
     When the processed image data of  FIG. 10A  is contrasted with the processed image data of  FIG. 10B , there is no difference between them. Thus, the dry etching learned model  920  can be considered to have the simulation accuracy that is sufficient to replace the dry etching simulator  310 . 
     Similarly,  FIG. 11A  and  FIG. 11B  are drawings illustrating the simulation accuracy of the deposition learned model.  FIG. 11A  illustrates the non-processed image data and the processed image data when the simulation is performed by using the deposition simulator  320  in the simulation device  130  as a comparison target. 
     With respect to the above,  FIG. 11B  illustrates the non-processed image data and the processed image data when the simulation is performed by using the deposition learned model  921  in the inference device  120 . 
     When the processed image data of  FIG. 11A  is contrasted with the processed image data of FIG. 
       11 B, there is no difference between them. Thus, it can be considered that the deposition learned model  921  has the simulation accuracy that is sufficient to replace the deposition simulator  320 . 
     &lt;Summary&gt; 
     As is clear from the above description, the learning device according to the first embodiment is configured to:
     obtain the non-processed image data that is input to the simulator based on the physical model and the parameter data that is set in the simulator based on the physical model   array the obtained parameter data in two dimensions in accordance with the width and the height of the obtained non-processed image data to process the obtained parameter data into an image data format, and concatenate the processed parameter data with the non-processed image to generate concatenated data   perform machine learning by inputting the generated concatenated data into a learning model based on a U-shaped convolutional neural network so that an output result output from the learning model approaches the processed image data output from the simulator based on the physical model   

     This enables the learning device according to the first embodiment to generate a learned model that achieves simulation contents achieved in the simulator based on the physical model. 
     Further, this enables the learning device according to the first embodiment to generate a learned model that can reduce a simulation time period to be shorter than a simulation time period of the simulator based on the physical model. The learning device according to the first embodiment can generate a learned model having the simulation accuracy that is sufficient to replace the simulator based on the physical model. 
     Here, in the above description, the simulator based on the physical model is targeted. However, it is needless to say that even if the simulator is not based on the physical model, the learning device according to the first embodiment can similarly generate a learned model. 
     The inference device according to the first embodiment is configured to:
     obtain the non-processed image data and the corresponding parameter data   array the obtained parameter data in two dimensions in accordance with the width and the height of the obtained non-processed image data to process the obtained parameter data into an image data format, and concatenate the processed parameter data with the non-processed image to generate concatenated data   perform simulation by inputting the generated concatenated data into the learned model generated by the learning device   

     This enables the inference device according to the first embodiment to achieve simulation contents achieved in the simulator based on the physical model. Further, this enables the inference device according to the first embodiment to reduce the simulation time period in comparison with the simulator based on the physical model, and to achieve the simulation accuracy that is sufficient to replace the simulator based on the physical model. 
     Here, in the above description, the simulator based on the physical model is targeted. However, it is needless to say that even if the simulator is not based on the physical model, the inference device according to the first embodiment can similarly achieve the simulation contents and the simulation accuracy. 
     As described above, according to the first embodiment, a learned model that replaces the simulator of the semiconductor manufacturing process can be provided. 
     Second Embodiment 
     In the above-described first embodiment, the parameter data is processed into the image data format in accordance with the width and the height of the non-processed image data, and is concatenated with the non-processed image data to be input to a learning model (or a learned model). 
     However, a method of processing the parameter data and a method of inputting the processed parameter data into a learning model (or a learned model) are not limited to this. For example, the processed parameter data may be input to each layer of a learning model (or a learned model). When the parameter data is input to each layer of the learning model (or learned model), the parameter data may also be processed into a predetermined format used when the image data on which a convolution operation is performed at each layer of the learning model (or the learned model) is converted. In the following, a second embodiment will be described focusing on differences between the second embodiment and the above-described first embodiment. 
     &lt;Functional Configuration of a Data Shaping Unit&gt; 
     First, a functional configuration of a data shaping unit of the learning device according to the second embodiment will be described in detail.  FIG. 12  is a drawing illustrating an example of the functional configuration of the data shaping unit of the learning device according to the second embodiment. A difference between this functional configuration and the functional configuration of the data shaping unit  111  illustrated in  FIG. 5  is that a data shaping unit  1200  illustrated in  FIG. 12  includes a concatenating unit  1201  and a normalizing unit  1202 . 
     The concatenating unit  1201  concatenates multiple channel data notified from the channel data generator  502  and generates concatenated data. 
     The normalizing unit  1202  normalizes the parameter data notified from the parameter data obtaining unit  511  and generates normalized parameter data. 
     &lt;Specific Example of a Process Performed by a Learning Model&gt; 
     Next, a specific example of a process performed by a dry etching learning model will be described.  FIG. 13  is a drawing illustrating the specific example of the process performed by the dry etching learning model of the learning device according to the second embodiment. 
     As illustrated in  FIG. 13 , in the learning device according to the second embodiment, concatenated data  1310  generated by the concatenating unit  1201  of the data shaping unit  1200  is input to a dry etching learning model  1300 . 
     As illustrated in  FIG. 13 , in the learning device according to the second embodiment, the normalized parameter data generated by the normalizing unit  1202  of the data shaping unit  1200  is input to the dry etching learning model  1300 . 
     As illustrated in  FIG. 13 , the dry etching learning model  1300  includes a neural network  1301  that is a fully connected learning model in addition to the UNET that is a CNN-based learning model. 
     In response to the normalized parameter data being input, the neural network  1301  outputs predetermined format values (e.g., coefficients γ and β of a linear equation) used to convert a value of each pixel of each image data on which a convolution operation is performed at each layer of the UNET. That is, the neural network  1301  has a function to process the normalized parameter data into a predetermined format (e.g., a format of coefficients of a linear equation). 
     In the example of  FIG. 13 , the UNET is composed of nine layers, so that the neural network  1301  outputs (γ 1 , β 1 ) to (γ 9 , β 9 ) as coefficients of the linear equation. In the example illustrated in  FIG. 13 , due to space limitation, a pair of the coefficients of the linear equation is input for each layer, but multiple pairs of the coefficients of the linear equation are input for each layer for each channel data. 
     In each layer of the UNET, a value of each pixel of image data of each channel data (which is defined as “h” here) on which a convolution operation is performed is converted by using, for example, a linear equation: h×y+β (i.e., in the first layer, h×γ 1 +β 1 ). 
     Here, the coefficients (γ 1 , β 1 ) to (γ 9 , β 9 ) of the linear equation can be regarded as an index indicating which image data is important among image data of respective channel data on which a convolution operation is performed in each layer of the UNET, for example. That is, the neural network  1301  performs a process of calculating an index indicating the importance of each image data processed at each layer of the learning model based on the normalized parameter data. 
     Under the above-described configuration, when the concatenated data  1310  and the normalized parameter data are input to the dry etching learning model  1300 , the output result  700  including multiple channel data is output. The output result  700  is compared with the processed image data by the comparing unit  430  and differential information is calculated. In the learning device according to the second embodiment, the modifying unit  440  updates model parameters of the UNET and model parameters of the neural network  1301  in the dry etching learning model  1300  based on the differential information. 
     As described above, the learning device according to the second embodiment can extract highly important image data in each layer of the UNET based on the normalized parameter data when machine learning is performed on the dry etching learning model  1300 . 
     &lt;Summary&gt; 
     As is clear from the above description, the learning device according to the second embodiment is configured to:
     obtain the non-processed image data input to the simulator based on the physical model and the parameter data set in the simulator based on the physical model   normalize the obtained parameter data to process the obtained parameter data into a coefficient format of a linear equation used to convert a value of each pixel of each image data on which a convolution operation is performed in each layer of the learning model   convert the value of each pixel of each image data on which a convolution operation is performed in each layer by using the linear equation when the learning unit performs machine learning   

     This enables the learning device according to the second embodiment to generate a learned model that achieves the simulation contents achieved in the simulator based on the physical model. 
     Additionally, the learning device according to the second embodiment can generate a learned model that can reduce the simulation time period in comparison with the simulator based on the physical model. Further, the learning device according to the second embodiment can generate a learned model that has the simulation accuracy sufficient to replace the simulator based on the physical model. 
     Although the learning device has been described in the second embodiment, when the executing unit performs the simulation in the inference device, substantially the same process is performed. 
     Thus, according to the second embodiment, in the simulation of the semiconductor manufacturing process, a learned model that replaces the simulator based on the physical model can be provided. 
     Third Embodiment 
     In the first and second embodiments described above, when the learning unit performs machine learning, events specific to the semiconductor manufacturing process are not particularly mentioned. However, events specific to the semiconductor manufacturing process exist, and the simulation accuracy can be further improved by reflecting a specific event in machine learning performed by the learning unit (i.e., by reflecting domain knowledge in machine learning performed by the learning unit). In the following, a third embodiment in which domain knowledge is reflected will be described focusing on differences between the third embodiment and the first and second embodiments described above. 
     &lt;Details of a Functional Configuration of the Learning Model&gt; 
       FIG. 14  is a drawing illustrating an example of a functional configuration of a learning unit of a learning device according to the third embodiment. An internal configuration within the learning model differs from the functional configuration of the learning unit  112  illustrated in  FIG. 4 . Here, the internal configuration within the learning model will be described using the dry etching learning model  1410 , but the deposition learning model has substantially the same internal configuration. 
     As illustrated in  FIG. 14 , the dry etching learning model  1410  in the learning unit  1400  includes a sigmoid function unit  1412  and a multiplier  1413  in addition to the UNET  1411 . 
     The sigmoid function unit  1412  is an example of a processing unit. As illustrated in  FIG. 14 , a first output result that is an output of the UNET  1411  is converted by a sigmoid function  1420  to output a second output result  1421 . 
     The multiplier  1413  obtains the second output result  1421  from the sigmoid function unit  1412 . The multiplier  1413  obtains the non-processed image data from the data shaping unit  111 . The multiplier  1413  multiplies the obtained non-processed image data by the obtained second output result  1421  to notify a final output result  1422  to the comparing unit  430 . 
     As described above, the dry etching learning model  1410  is configured to output the final output result  1422  by multiplying the non-processed image data, so that the image data representing the etch ratio is output from the UNET  1411  as the first output result when machine learning is performed on the dry etching learning model  1410 . 
     Here, the etch rate indicates a value of the change rate that indicates how much a layer of each material included in the non-processed image data has been etched in the processed image data. By performing machine learning on the dry etching learning model  1410 , the etch rate approaches a value obtained by dividing the processed image data by the non-processed image data. However, the first output result output from the UNET  1411  during machine learning may be any value. 
     In dry etching, there is a constraint condition (i.e., domain knowledge) that “materials do not increase over the course of processing” with respect to the change in shape. Thus, in dry etching, the etch rate is within the range from 0 to 1. 
     Here, the sigmoid function unit  1412  is a function that converts any value to a value from 0 to 1, and the above-described domain knowledge can be reflected by the sigmoid function unit  1412  converting the first output result to the second output result. 
     Although not illustrated in  FIG. 14 , substantially the same process can be performed in the deposition learning model by providing the sigmoid function unit, the multiplier, and the like. Specifically, as the first output result, image data representing the deposition rate is output from the UNET when machine learning is performed on the deposition learning model. 
     Here, the deposition rate indicates a value of the change rate that indicates how much a thin film is deposited in the processed image data for a layer of each material included in the non-processed image data. By performing machine learning on the deposition learning model, the deposition rate approaches a value obtained by dividing a difference between the non-processed image data and the processed image data by the non-processed image data. However, the first output result output from UNET during machine learning may be any value. 
     In deposition, there is a constraint condition (i.e., domain knowledge) that “materials do not decrease over the course of processing” with respect to the change in shape. Thus, in deposition, the deposition rate is within the range from 0 to 1. 
     As described above, the sigmoid function unit is a function that converts any value to a value from 0 to 1, and the domain knowledge can be reflected by the sigmoid function unit converting the first output result to the second output result. 
     As described above, according to the learning unit  1400  of the learning device  110  of the third embodiment, the domain knowledge can be reflected in the machine learning, and the simulation accuracy can be further improved. 
     Fourth Embodiment 
     In the first to third embodiments described above, the data shaping unit generates the concatenated data having a height and a width in accordance with the width and the height of the non-processed image data. However, the width and the height of the concatenated data generated by the data shaping unit are determined as desired, and the data shaping unit may be configured to compress the non-processed image data and generate the concatenated data. In the following, a fourth embodiment will be described focusing on differences between the fourth embodiment and the first to third embodiments described above. 
     &lt;Details of a Functional Configuration of a Data Shaping Unit&gt; 
       FIG. 15A  and  FIG. 15B  are drawings illustrating an example of a functional configuration of a data shaping unit of a learning device according to the fourth embodiment.  FIG. 15A  illustrates a data shaping unit  1510  in which a compressing unit  1511  is added to the data shaping unit  111  of the learning device according to the first embodiment. 
     The compressing unit  1511  compresses the non-processed image data obtained by the shape data obtaining unit  501 . The compressing unit  1511 , for example, calculates an average value of pixel value of a adjacent pixels (n is an integer that is two or greater; for example, n=4 indicates two pixels in the vertical direction and two pixels in the horizontal direction), and the calculated average value is defined as a pixel value of one pixel that groups the n pixels. This enables the compressing unit  1511  to compress the non-processed image data by a factor of 1/n. 
     As described above, the compressing unit  1511  performs a compression process so that the composition ratio (or the content ratio) of the materials is maintained as much as possible over the course of compression in view of the fact that the non-processed image data is image data representing the composition ratio (or the content ratio) of the materials. The compression rate of the compression process performed by the compressing unit  1511  is not limited to an integer multiple, and in the compressing unit  1511 , the compression process can be performed with a desired compression rate. 
     Similarly,  FIG. 15B  illustrates a data shaping unit  1520  in which a compressing unit  1511  is added to the data shaping unit  1200  of the learning device according to the second embodiment. 
     The compressing unit  1511  included in the data shaping unit  1520  has substantially the same function as the compressing unit  1511  included in the data shaping unit  1510 . Thus, the detailed description is omitted here. 
     As described above, by adding the compressing unit  1511  to the data shaping unit  1510  or  1520 , the size of the concatenated data that is input to the learning units  112  and  1400  (or the executing unit  122 ) can be reduced. As a result, according to the fourth embodiment, a learning time period required when the learning units  112  and  1400  perform machine learning or a simulation time period required when the executing unit  122  performs the simulation can be reduced. 
     Other Embodiments 
     In the first embodiment described above, the dry etching learning model  420  and the deposition learning model  421  are provided in the learning unit  112 , and machine learning is performed separately using different training data. 
     However, dry etching and deposition may be simultaneously performed in a semiconductor manufacturing process. Assuming such a case, one learning model may be provided in the learning unit  112  so that machine learning can be performed with respect to a case in which dry etching and deposition are performed simultaneously. 
     In this case, the learning unit  112  performs machine learning on the one learning model by using training data including non-processed image data obtained before dry etching and deposition are performed and processed image data obtained after dry etching and deposition have been performed. 
     In the executing unit  131  of the simulation device  130 , it is necessary to provide both the dry etching simulator  310  and the deposition simulator  320 , but the learning unit  112  of the learning device  110  can integrate the learning models. 
     In the first embodiment described above, the non-processed image data and the processed image data have been described as two-dimensional image data. However, the non-processed image data and the processed image data are not limited to two-dimensional image data, but may be three-dimensional image data (what is called voxel data). 
     If the non-processed image data is two-dimensional image data, the concatenated data is an array of the number of channels×width×height. If the non-processed image data is three-dimensional image data, the concatenated data is an array of the number of channels×width×height×depth. 
     In the first embodiment described above, the two-dimensional image data is processed as it is. However, two-dimensional image data may be modified or three-dimensional image data may be modified for processing. For example, when three-dimensional image data is obtained, two-dimensional image data of a predetermined cross section may be generated, and the generated data may be input as non-processed image data. Alternatively, three-dimensional image data may be generated based on two-dimensional image data of successive predetermined cross sections and the generated data may be input as non-processed image data. 
     In the first embodiment described above, the channel data generator  502  generates channel data for a layer of air and each layer of a material. However, the method of generating channel data is not limited to this. The channel data generator  502  may generate channel data based on larger classifications, such as oxides, silicon, organics, nitrides, and so on, rather than based on specific film types. 
     In the first to fourth embodiments described above, the inference device  120  outputs the processed image data and terminates the process in response to the non-processed image data and parameter data being input. However, the configuration of the inference device  120  is not limited to this. For example, the embodiments may be configured to input, for example, the processed image data output by inputting the non-processed image data and the parameter data together with corresponding parameter data to the inference device  120  again. This enables the inference device  120  to continuously output shape changes. The corresponding parameter data can be changed as desired when the processed image data is input to the inference device  120  again. 
     In the first to fourth embodiments described above, the learning device  110 , the inference device  120 , and the simulation device  130  are illustrated separately. However, any two devices may be configured as a single unit, and all devices may be configured as a single unit. 
     In the first to fourth embodiments described above, one computer constitutes the learning device  110 , but multiple computers may constitute the learning device  110 . Similarly, in the first to fourth embodiments described above, one computer constitutes the inference device  120 , but multiple computers may constitute the inference device  120 . 
     In the first to fourth embodiments described above, the learning device  110 , the inference device  120 , and the simulation device  130  have been applied to a semiconductor manufacturing process, but it is needless to say that they may be applied to processes other than a semiconductor manufacturing process. The processes other than a semiconductor manufacturing process include manufacturing processes other than a semiconductor manufacturing process and non-manufacturing processes. 
     In the first to fourth embodiments described above, the learning device  110  and the inference device  120  are achieved by causing a general-purpose computer to execute various programs, but a method of achieving the learning device  110  and the inference device  120  is not limited to this. 
     For example, the learning device  110  and the inference device  120  may be achieved by a dedicated electronic circuit (i.e., hardware), such as an integrated circuit (IC) that implements a processor, memory, and the like. Multiple components may be implemented in one electronic circuit, one component may be implemented in multiple electronic circuits, and components and electronic circuits may be implemented on a one-to-one basis. 
     It should be noted that the present invention is not limited to the above-described configurations, such as the configurations described in the above-described embodiments, and combinations with other elements. In these respects, various modifications can be made within the scope of the invention without departing from the spirit of the invention, and the configurations may be appropriately determined according to an application form.