WO2025004959A1 - 情報処理方法、コンピュータプログラム及び情報処理装置 - Google Patents

情報処理方法、コンピュータプログラム及び情報処理装置 Download PDF

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WO2025004959A1
WO2025004959A1 PCT/JP2024/022391 JP2024022391W WO2025004959A1 WO 2025004959 A1 WO2025004959 A1 WO 2025004959A1 JP 2024022391 W JP2024022391 W JP 2024022391W WO 2025004959 A1 WO2025004959 A1 WO 2025004959A1
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model
data
information processing
scale
input data
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French (fr)
Japanese (ja)
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遼路 安崎
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Tokyo Electron Ltd
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Tokyo Electron Ltd
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Priority to JP2025529702A priority patent/JPWO2025004959A1/ja
Priority to CN202480039826.0A priority patent/CN121336217A/zh
Publication of WO2025004959A1 publication Critical patent/WO2025004959A1/ja
Priority to US19/433,274 priority patent/US20260118861A1/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P95/00—Generic processes or apparatus for manufacture or treatments not covered by the other groups of this subclass

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  • This disclosure relates to an information processing method, a computer program, and an information processing device.
  • Patent Document 1 proposes a prediction method that includes an operation for obtaining a machine learning model that predicts performance metrics for the operation of a semiconductor manufacturing tool, and an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool, and that uses one or more machine learning models to estimate the performance of the process definition used in the semiconductor manufacturing tool and presents the estimated results of the product manufacturing performance on a display.
  • This disclosure provides an information processing method, computer program, and information processing device that are expected to speed up verification using large-scale models.
  • an information processing method is an information processing method in which an information processing device performs a simulation using a large-scale model composed of multiple small-scale models, and the information processing device pre-stores in a storage unit the correspondence between input data and output data for a specific small-scale model included in the large-scale model, and when data is input to the specific small-scale model in a simulation, generates a proxy model that represents the specific small-scale model in a local range including the input data based on the data pre-stored in the storage unit, and generates output data corresponding to the input data to the specific small-scale model using the generated proxy model.
  • This disclosure is expected to speed up verification and other processes using large-scale models.
  • FIG. 1 is a schematic diagram for explaining an overview of an information processing system according to an embodiment of the present invention
  • 1 is a schematic diagram for explaining an overview of an information processing system according to an embodiment of the present invention
  • 1 is a block diagram showing an example of a configuration of an information processing device according to an embodiment of the present invention
  • FIG. 13 is a schematic diagram for explaining a surrogate model.
  • 10 is a flowchart showing an example of a procedure of a representative model generation process performed by the information processing device according to the present embodiment.
  • 6 is a flowchart showing an example of a procedure of an abnormality determination process performed by the information processing device according to the present embodiment.
  • 10 is a flowchart showing an example of a procedure of an optimization process performed by the information processing device according to the present embodiment.
  • ⁇ System Overview> 1 and 2 are schematic diagrams for explaining the outline of an information processing system according to the present embodiment.
  • the information processing system according to the present embodiment is a system that reproduces a physical system existing in a real space (physical space) in a virtual space (digital space or cyberspace) and manages and controls the physical system. This is a technology that can be called a digital twin.
  • the information processing system handles a digital twin in which a substrate processing apparatus 3 that performs substrate processing such as manufacturing semiconductor wafers is reproduced in a virtual space as an example of a physical system.
  • the physical system handled by the information processing system according to the present embodiment is not limited to the substrate processing apparatus 3.
  • the information processing system can be applied to any physical system that can be virtualized such as a digital twin.
  • a large-scale model 100 that models the substrate processing apparatus 3 is created based on various data collected by the substrate processing apparatus 3.
  • the large-scale model includes a power supply model 101, a heater model 102, and a chiller model 103, etc., which are small-scale models that individually model multiple components that make up the substrate processing apparatus 3, such as a power supply, heater, and chiller.
  • the large-scale model 100 also includes various other small-scale models, such as an RF (Radio Frequency) model 104, a surface temperature model 105, and a surface reaction model 106.
  • RF Radio Frequency
  • the large-scale model 100 simulates the operation or processing of the components corresponding to these multiple small-scale models, and transmits and receives data on the simulation results between the small-scale models, thereby making it possible to simulate the operation or processing of the entire substrate processing apparatus 3.
  • the large-scale model 100 is configured to perform a simulation of an etching process performed for input data on the set voltage and set temperature of the substrate processing apparatus 3, and to output a predicted etching rate in the etching process as output data as a result of the simulation. Note that although this embodiment will be described using an etching process as an example, the process simulated using the large-scale model 100 is not limited to an etching process and may be various other processes.
  • the power supply model 101 provided in the large-scale model 100 models the power supply of the substrate processing apparatus 3, and provides output data of the results of simulating the operation of the power supply to the heater model 102, chiller model 103, and RF model 104.
  • the heater model 102 models the heater of the substrate processing apparatus 3, simulates the operation of the heater based on input data from the power supply model 101, and provides output data of the simulation results to the surface temperature model 105.
  • the chiller model 103 models the chiller of the substrate processing apparatus 3, simulates the operation of the chiller based on input data from the power supply model 101, and provides output data of the simulation results to the surface temperature model 105.
  • the RF model 104 models the high-frequency circuit (not shown) and other components of the substrate processing apparatus 3, and simulates the operation of the high-frequency circuit based on input data from the power supply model 101, and provides output data of the simulation result to the surface temperature model 105.
  • the surface temperature model 105 models the surface temperature characteristics of a substrate such as a wafer processed by the substrate processing apparatus 3, and simulates changes in the substrate surface temperature based on input data provided by the heater model 102, chiller model 103, and RF model 104, and provides output data of the simulation result to the surface reaction model 106. In this example, an etching process is assumed as the substrate processing performed by the substrate processing apparatus 3.
  • the surface reaction model 106 models the characteristics of the reaction on the substrate surface during the etching process, and simulates the reaction on the substrate surface due to the etching process based on input data provided by the surface temperature model 105, and outputs etching rate data as the simulation result.
  • the etch rate obtained when the substrate processing apparatus 3 is operated at a certain set voltage and set temperature can be predicted by simulating the large-scale model 100 according to the input data of the set voltage and set temperature.
  • the predicted value of the etch rate output by the large-scale model 100 according to the input data of the set voltage and set temperature can be compared with the measured value of the etch rate obtained as a result of operating the substrate processing apparatus 3 at the same set voltage and set temperature, and if the difference between the predicted value and the measured value exceeds a threshold value, it can be determined that an abnormality has occurred in the substrate processing apparatus 3.
  • the error between the predicted value of the etch rate output by the large-scale model 100 according to the input data of a certain set voltage and set temperature and the target value of the etching rate desired by the user can be calculated, and the set voltage and set temperature can be increased or decreased so as to reduce this error, and the simulation can be repeated to obtain a set voltage and set temperature that can set the etching rate to a target value or a value close to the target value.
  • small-scale models provided in the large-scale model 100 are created in advance by extracting input data to the components and output data of the components from the data collected about the substrate processing apparatus 3, and adjusting the parameters of the model to reproduce the corresponding relationship between input and output based on the extracted data.
  • the small-scale model may be expressed, for example, by an arithmetic formula based on the physical characteristics of the components, or may be a machine learning model such as a neural network, or may be a model with a configuration other than those.
  • the multiple small-scale models included in such a large-scale model 100 vary in the amount of calculations involved in the simulation processing of each small-scale model, the amount of data handled in the processing, and the time required for processing.
  • the amount of data required to generate each small-scale model and the time required to generate each small-scale model also vary.
  • the power supply model 101, heater model 102, chiller model 103, RF model 104, and surface temperature model 105 included in the large-scale model 100 are models (light models) that require a relatively small amount of calculations, processing time, generation time, etc.
  • the surface reaction model 106 is a model (heavy model) that requires a relatively large amount of calculations, processing time, generation time, etc.
  • Such heavy models require a lot of data to generate a model with good accuracy, and generation takes a long time.
  • the information processing device 1 that performs a simulation using the large-scale model 100 generates a surrogate model 120 that performs processing of the surface reaction model 106, which is a heavy model, on behalf of the large-scale model.
  • the surrogate model 120 does not perform processing for the entire range of possible values of input data for the surface reaction model 106, but instead performs processing for the surface reaction model 106 only in a part of the entire range of possible values of input data.
  • the surrogate model 120 is a model that reduces the amount of calculation, processing time, generation time, and the like by limiting the range of the corresponding input data to a local range.
  • the information processing device 1 may generate a surrogate model 120 for a light model.
  • the information processing device 1 may also generate a surrogate model 120 for multiple small-scale models included in the large-scale model 100.
  • the information processing device 1 performs a simulation using, for example, the large-scale model 100, and generates a proxy model 120 of the surface reaction model 106 when processing of the surface reaction model 106 becomes necessary in the simulation. Therefore, at the start of the simulation, the power supply model 101, heater model 102, chiller model 103, RF model 104, and surface temperature model 105 of the large-scale model 100 must be generated in advance, but the surface temperature model 105 does not have to be generated in advance.
  • the information processing device 1 acquires the correspondence between the input data and output data of the surface reaction model 106 in advance and stores it in the input/output DB (database) 20.
  • the input data and output data stored in the input/output DB 20 are, for example, data of the range and amount capable of generating a surface reaction model 106 corresponding to the entire range of the input data.
  • the information processing device 1 starts a simulation using the large-scale model 100, and when the data output by the surface temperature model 105 and input to the surface reaction model 106 is calculated, it reads out the input data of a local range including the value of this input data and the corresponding output data from the input/output DB 20.
  • the information processing device 1 generates a proxy model 120 based on the input data and output data of the local range read out from the input/output DB 20.
  • the information processing device 1 inputs the output data of the surface temperature model 105 to the generated proxy model 120, and obtains the output data of the proxy model 120 (predicted etch rate data in this example), thereby simulating the large-scale model 100.
  • the information processing device 1 In a simulation using the large-scale model 100, the information processing device 1 repeatedly performs calculations on each small-scale model, and repeatedly calculates the predicted value of the etch rate. At this time, data from the surface temperature model 105 is repeatedly input to the surface reaction model 106, and the value of the input data changes each time. The information processing device 1 monitors changes in the value of the input data from the surface temperature model 105 to the surface reaction model 106, and updates the proxy model 120 by regenerating and replacing it when a value that exceeds the local range that the generated proxy model 120 can handle is input. By the information processing device 1 updating the proxy model 120 as necessary, the simulation using the large-scale model 100 can be maintained regardless of the value of the input data to the surface reaction model 106.
  • ⁇ Device Configuration> 3 is a block diagram showing an example of the configuration of an information processing device 1 according to the present embodiment.
  • the information processing device 1 according to the present embodiment is a device that predicts the results of substrate processing and detects abnormalities by simulation or the like using a large-scale model 100 that models a substrate processing device 3 as a target device.
  • the information processing device 1 according to the present embodiment is connected to the substrate processing device 3 via, for example, a communication cable, and can obtain data from the substrate processing device 3 and control the substrate processing device 3 according to the results of prediction and abnormality detection.
  • the information processing device 1 does not need to be communicably connected to the substrate processing device 3, and may only perform processing such as simulation using the large-scale model 100 without controlling the substrate processing device 3.
  • the information processing device 1 can be realized by installing a computer program according to this embodiment in a general-purpose information processing device such as a personal computer or a server computer.
  • the information processing device 1 according to this embodiment is configured with a processing unit 11, a storage unit 12, a communication unit 13, a display unit 14, an operation unit 15, etc. Note that in this embodiment, the processing is described as being performed by a single information processing device 1, but the processing of the information processing device 1 may be distributed among multiple devices.
  • the processing unit 11 is configured using an arithmetic processing device such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit) or a quantum processor, a ROM (Read Only Memory), and a RAM (Random Access Memory).
  • the processing unit 11 reads out and executes a program 12a stored in the memory unit 12 to perform various processes such as a simulation using a large-scale model 100 of the substrate processing apparatus 3, and a process of generating, as necessary, a proxy model of one or more small-scale models included in the large-scale model 100.
  • the storage unit 12 is configured using a large-capacity storage device such as a hard disk.
  • the storage unit 12 stores various programs executed by the processing unit 11 and various data required for the processing of the processing unit 11.
  • the storage unit 12 stores the program 12a executed by the processing unit 11.
  • the storage unit 12 also includes a model information storage unit 12b that stores information related to the large-scale model 100 of the substrate processing apparatus 3, and an input/output DB 20 that stores data used to generate the model.
  • the program (computer program, program product) 12a is provided in a form recorded on a recording medium 99 such as a memory card or an optical disk, and the information processing device 1 reads the program 12a from the recording medium 99 and stores it in the memory unit 12.
  • the program 12a may be written to the memory unit 12, for example, during the manufacturing stage of the information processing device 1.
  • the program 12a may be distributed by a remote server device or the like and acquired by the information processing device 1 through communication.
  • the program 12a may be read from the recording medium 99 by a writing device and written to the memory unit 12 of the information processing device 1.
  • the program 12a may be provided in a form distributed via a network, or may be provided in a form recorded on the recording medium 99.
  • the model information storage unit 12b of the storage unit 12 stores information about the large-scale model 100 of the substrate processing apparatus 3 that has been generated in advance, and about one or more small-scale models contained therein.
  • the information about the large-scale model 100 may include, for example, information about what small-scale models are contained in the large-scale model 100, and how the multiple small-scale models are connected.
  • the information about the small-scale models may also include, for example, information indicating the configuration of the model, and information about internal parameters of the model determined by machine learning or the like.
  • the information processing apparatus 1 constructs the large-scale model 100 by reading out this information previously stored in the model information storage unit 12b, and can use it for processing such as simulation of the substrate processing apparatus 3.
  • model information storage unit 12b may store information about the surface reaction model 106, for example, information about the input data and output data for this model, but the internal parameter values are not stored in advance.
  • the internal parameter values of the proxy model 120 of the surface reaction model 106 generated in the proxy model generation process described below may be stored in the model information storage unit 12b.
  • the method for generating the large-scale model 100 of the substrate processing apparatus 3 and the multiple small-scale models contained therein is an existing technology, so detailed description will be omitted in this embodiment.
  • the large-scale model 100 and the small-scale models can be generated, for example, by performing machine learning processing using various data obtained from the substrate processing apparatus 3. These models may be generated by the information processing apparatus 1, or the information processing apparatus 1 may acquire information on models generated by an apparatus other than the information processing apparatus 1 and store it in the model information storage unit 12b.
  • the large-scale model 100 of the substrate processing apparatus 3 as shown in FIG. 1 is generated in advance by an appropriate method. Information on the generated large-scale model 100 and small-scale models is stored in advance in the model information storage unit 12b so that it can be used when the information processing apparatus 1 performs a simulation of the substrate processing apparatus 3, etc.
  • the input/output DB 20 is a database that stores data necessary to generate the surrogate model 120. For example, data that associates input data for the surrogate model 120 with output data that the surrogate model 120 outputs when this input data is input is stored in advance in the input/output DB 20.
  • data that associates input data for the surrogate model 120 with output data that the surrogate model 120 outputs when this input data is input is stored in advance in the input/output DB 20.
  • temperature distribution data as input data from the surface temperature model 105 to the surface reaction model 106 and etching rate data when etching is performed for the input temperature distribution as output data of the surface reaction model 106 are stored in association with each other in the input/output DB 20. These data are collected in advance, for example, by a designer of this system, who measures the temperature distribution and etching rate when etching is performed in the substrate processing apparatus 3, and are stored in the input/output DB 20.
  • the input/output DB 20 of the information processing device 1 associates input data and output data into a set, and stores the sets of data in groups based on the values of the input data.
  • the information processing device 1 can classify the input data into a plurality of clusters using a clustering method of unsupervised learning, thereby dividing the sets of input data and output data into groups.
  • the information processing device 1 may group the sets of input data and output data based on the range of values contained in the input data, or may store the data in ascending or descending order of any of the values contained in the input data.
  • the sets of input data and output data may be grouped according to the values of the output data, rather than according to the values of the input data.
  • Data stored in the input/output DB 20 may be added and deleted at appropriate times, and when adding data, the information processing device 1 classifies the data to be added into appropriate groups according to a predetermined rule and stores the data.
  • the communication unit 13 is connected to the substrate processing apparatus 3 via a cable such as a communication line or a signal line, and transmits and receives data to and from the substrate processing apparatus 3 via this cable.
  • the information processing apparatus 1 can, for example, acquire data obtained when the substrate processing apparatus 3 performs substrate processing, and perform processing such as predicting the results of the substrate processing by a simulation using the large-scale model 100.
  • the information processing apparatus 1 can determine setting values for the substrate processing apparatus 3 by a simulation using the large-scale model 100, and control the operation of the substrate processing apparatus 3 by transmitting the determined setting values to the substrate processing apparatus 3.
  • the communication unit 13 transmits data provided by the processing unit 11 to the substrate processing apparatus 3, receives data transmitted from the substrate processing apparatus 3, and provides the received data to the processing unit 11.
  • the display unit 14 is configured using a liquid crystal display or the like, and displays various images and characters based on the processing of the processing unit 11.
  • the display unit 14 displays, for example, information related to the results of a simulation using the large-scale model 100, and information related to the operating status of the substrate processing apparatus 3.
  • the operation unit 15 accepts user operations and notifies the processing unit 11 of the accepted operations.
  • the operation unit 15 accepts user operations through an input device such as a mechanical button or a touch panel provided on the surface of the display unit 14.
  • the operation unit 15 may be an input device such as a mouse and keyboard, and these input devices may be configured to be removable from the information processing apparatus 1.
  • the storage unit 12 may be an external storage device connected to the information processing device 1.
  • the information processing device 1 may be a multi-computer including multiple computers, or may be a virtual machine virtually constructed by software.
  • the information processing device 1 is not limited to the above configuration, and may not include, for example, the display unit 14 and the operation unit 15.
  • the processing unit 11 reads out and executes the program 12a stored in the memory unit 12, whereby the simulation processing unit 11a, the proxy model generating unit 11b, the update determination unit 11c, the abnormality determination unit 11d, the display processing unit 11e, and the like are realized in the processing unit 11 as software-based functional units.
  • the simulation processing unit 11a performs a simulation using the large-scale model 100 of the substrate processing apparatus 3 based on the information stored in the model information storage unit 12b.
  • the simulation processing unit 11a configures a number of necessary small-scale models based on the information stored in the model information storage unit 12b, and configures the large-scale model 100 by connecting these small-scale models.
  • the simulation processing unit 11a acquires input data for the large-scale model 100 based on, for example, simulation conditions set by a user, inputs the acquired input data to the corresponding small-scale model, and acquires output data output by this small-scale model.
  • the simulation processing unit 11a repeats inputting output data of a small-scale model to the next small-scale model and acquiring output data of the next small-scale model according to the connection relationship of the multiple small-scale models included in the large-scale model 100, and sets the data output by the final small-scale model as output data of the large-scale model 100, and sets this output data as a simulation result.
  • the simulation processing unit 11a can, for example, input input data that changes in time series to the large-scale model 100 in sequence, and acquire output data that changes in time series as a simulation result from the large-scale model 100.
  • the surrogate model generation unit 11b When the simulation processing unit 11a performs a simulation using the large-scale model 100, the surrogate model generation unit 11b performs a process of generating a surrogate model 120 of a specific small-scale model included in the large-scale model 100.
  • the surrogate model 120 is a model that guarantees operation only in a local range with respect to the entire range of input data that the small-scale model corresponds to.
  • the surrogate model generation unit 11b acquires a set of input data and output data from the input/output DB 20 for a local range including the input data, and generates a surrogate model using the acquired multiple sets of data.
  • the method of generating the surrogate model 120 by the surrogate model generation unit 11b may employ various methods, such as DMD (Dynamic Mode Decomposition), SINDy (Sparse Identification of Nonlinear Dynamical systems), the least squares method, spline interpolation, machine learning, or a genetic algorithm.
  • DMD Dynamic Mode Decomposition
  • SINDy Sese Identification of Nonlinear Dynamical systems
  • the least squares method spline interpolation
  • machine learning or a genetic algorithm.
  • the update determination unit 11c performs processing to determine whether or not the proxy model 120 generated by the proxy model generation unit 11b needs to be updated.
  • the update determination unit 11c determines whether or not this input data is within the range of input data guaranteed by the generated proxy model 120. If the input data is within the guaranteed range, the update determination unit 11c determines that updating the proxy model 120 is unnecessary, and if the input data is outside the guaranteed range, it determines that updating is necessary. If updating is necessary, the update determination unit 11c causes the proxy model generation unit 11b to generate a proxy model 120 that corresponds to the new input data.
  • the display processing unit 11e performs processing to display various characters and images on the display unit 14.
  • the display processing unit 11e displays various information, such as information on the results of a simulation performed by the simulation processing unit 11a, or information on the results of an abnormality determination of the substrate processing apparatus 3 performed by the abnormality determination unit 11d.
  • ⁇ Surrogate model generation process> data is collected using various sensors or measuring devices for the substrate processing apparatus 3 to be simulated, and a large-scale model 100 is generated by modeling the substrate processing apparatus 3 based on the collected data.
  • a large-scale model 100 is generated by modeling the substrate processing apparatus 3 based on the collected data.
  • small-scale models other than the surface reaction model 106 are generated based on the collected data.
  • These small-scale models may be generated by the information processing apparatus 1, or may be generated by a device different from the information processing apparatus 1.
  • these small-scale models may be models of any configuration, and may be generated by various methods such as DMD, SINDy, least squares method, spline interpolation, machine learning, or genetic algorithm.
  • the information processing device 1 stores in advance in the model information storage unit 12b small-scale models other than the surface reaction model 106, which have been generated in advance by an appropriate method for the large-scale model 100 shown in, for example, Figures 1 and 2.
  • the information processing device 1 also stores in advance in the input/output DB 20 a large amount of data for generating a proxy model 120 for the surface reaction model 106 included in the large-scale model 100.
  • the data for generating the proxy model 120 is data obtained by extracting input data and output data for the surface reaction model 106 from a large amount of data collected in the above-mentioned data collection regarding the substrate processing device 3, and associating the extracted input data with output data.
  • the information processing device 1 can predict the etching rate as a result of the etching process of the substrate processing device 3 when, for example, the set temperature and set voltage are determined, by performing a simulation using the large-scale model 100.
  • the information processing device 1 performs simulations for a plurality of small-scale models included in the large-scale model 100 individually, and by transferring the simulation results between the small-scale models, it is possible to finally calculate a predicted value of the etch rate.
  • the information processing device 1 first performs a simulation of the power supply model 101 and provides the results to the heater model 102, the chiller model 103, and the RF model 104.
  • the information processing device 1 provides the results of each simulation of the heater model 102, the chiller model 103, and the RF model 104 to the surface temperature model 105.
  • the information processing device 1 performs a simulation of the surface temperature model 105 based on the simulation results of the heater model 102, the chiller model 103, and the RF model 104, calculates predicted values such as the surface temperature distribution of the substrate, and inputs them to the surface reaction model 106 as the simulation results.
  • the surface reaction model 106 is not generated in advance.
  • the information processing device 1 determines whether or not it is necessary to generate a proxy model 120 for the surface reaction model 106 (whether or not an update is required). If it is determined that it is necessary to generate the proxy model 120, the information processing device 1 acquires the necessary data from the input/output DB 20 and generates the proxy model 120 for the surface reaction model 106.
  • the proxy model 120 may be a model of any configuration, and may be generated by various methods such as DMD, SINDy, least squares method, spline interpolation, machine learning, or genetic algorithm.
  • FIG. 4 is a schematic diagram for explaining the proxy model 120.
  • FIG. 4 shows an example of the correspondence between the input data and output data of the surface reaction model 106 in a two-dimensional graph, with the horizontal axis (x-axis) of the graph representing temperature, which is the input data, and the vertical axis (y-axis) representing the etch rate, which is the output data. Note that in this example, to simplify the explanation, both the input data and output data of the surface reaction model 106 are assumed to be one-dimensional (i.e., one numerical value), but this is not limited thereto, and the input data and output data may be multidimensional vectors.
  • the solid curve in Figure 4 indicates the correspondence between the input and output of the surface reaction model 106.
  • This curve corresponds to a graph of the relationship between the input data and output data for the surface reaction model 106 that was measured in advance in the substrate processing apparatus 3.
  • Input/Output DB 20 Input/Output DB 20 stores at least an amount of data that can reproduce this graph.
  • the correspondence between the input and output of the surface reaction model 106 is complex, and if one were to try to model this graph with some kind of mathematical formula, it is estimated that a complex, high-dimensional formula would need to be used.
  • the surrogate model 120 generated by the information processing device 1 is a model that reproduces only a portion of the input/output correspondence of the surface reaction model 106.
  • the dashed curve in FIG. 4 indicates the input/output correspondence of the surrogate model 120 generated to represent the surface reaction model 106 for the local temperature range of x1 to x2.
  • the guaranteed range of input data by the surrogate model 120 is narrowed to x1 to x2, but when modeling is performed only in this local range, for example, it is expected that modeling can be performed using low-dimensional formulas of about second to third order.
  • the information processing device 1 determines a local range that includes the values of the input data from the surface temperature model 105 to the surface reaction model 106, reads data in a range that can guarantee the operation of the model in this local range from the input/output DB 20, and generates the surrogate model 120. For example, the information processing device 1 determines a local range x1 to x2 for the input data x0 to the surface reaction model 106, and generates the surrogate model 120 by reading data in the range x3 to x4, for example, as data necessary for generating the surrogate model 120 that can guarantee the operation in this local range x1 to x2 (where x3 ⁇ x1 ⁇ x0 ⁇ x2 ⁇ x4).
  • the information processing device 1 inputs the input data from the surface temperature model 105 to the generated surrogate model 120, and obtains data on the predicted value of the etch rate output by the surrogate model 120.
  • the information processing device 1 stores information on the generated surrogate model 120 in the storage unit 12, and can reuse it in subsequent processing.
  • the information processing device 1 determines that it is not necessary to update the surrogate model 120 and calculates the predicted value of the etch rate by reusing the already generated surrogate model 120. In contrast, if the input data x5 from the surface temperature model 105 to the surrogate model 120 is subsequently outside the guaranteed range x1 to x2 (x5 ⁇ x1 or x5>x2), the information processing device 1 determines that it is necessary to update the surrogate model 120 and generates a surrogate model 120 in a local range that includes the new input data x5.
  • Data range determination method 1 In the first method, for example, the range of x ⁇ r is set as the guaranteed range for input data x to the surface reaction model 106, the range of x ⁇ rN is set as the range of data to be read from the input/output DB 20, and a user such as a designer or administrator of this system sets the values of r and N in advance.
  • the information processing device 1 accepts and stores input of the set values of r and N from the user, reads data in the range of x ⁇ rN from the input/output DB 20 according to the stored set values when generating the proxy model 120, and generates the proxy model 120 based on the read data.
  • the second method is a method of determining the data range or the number of pieces of data, etc., based on a predetermined formula for calculating an error and a permissible error value.
  • the order of the surrogate model 120 to be generated is n
  • the number of pieces of data is M
  • the permissible error is Acc
  • the inequality Error(D, M, n) ⁇ Acc is obtained.
  • the information processing device 1 can generate the surrogate model 120 using data in range D that includes the input data x to the surface reaction model 106, for example, data in the range from x-D/2 to x+D/2.
  • the order n or the number of pieces of data M can be determined, rather than the data range D.
  • the order n is also possible to determine the order n or the number of pieces of data M, rather than the data range D.
  • the order n is determined by an appropriate method (for example, based on the above-mentioned "Data range determination method 1")
  • the number of pieces of data M can be calculated based on the above inequality. The same applies to the order n.
  • This formula is determined in advance by, for example, a designer or manager of the information processing system, and is stored in advance in the storage unit 12 of the information processing device 1.
  • the information processing device 1 may generate the proxy model 120 by appropriately thinning the data rather than using all of the data in the determined range.
  • the information processing device 1 may thin the data, for example, by randomly selecting a predetermined number of data from the data in the determined range, or may thin the data by selecting data at equal intervals in the order in which the data is stored in the input/output DB 20, or may thin the data using methods other than these.
  • Whether or not the information processing device 1 thins out the data can be set in advance by, for example, the user.
  • the user sets a maximum value for the number or amount of data to be used when generating the proxy model 120, and if the target data stored in the input/output DB 20 exceeds the set number or amount, the information processing device 1 thins out the data to generate the proxy model 120.
  • the user can set an upper limit for the time required for the process of generating the proxy model 120, and the information processing device 1 can estimate the time required for generation from the configuration and amount of parameters of the proxy model 120, and thin out the data so as not to exceed the set upper limit.
  • the information processing device 1 which has generated the surrogate model 120 using the data stored in the input/output DB 20, inputs the input data for the surface reaction model 106 to the surrogate model 120, obtains the output data output by the surrogate model 120 in response to this, and provides this output data to a subsequent small-scale model or the like as output data for the surface reaction model 106.
  • the information processing device 1 stores information about the generated surrogate model 120 in the storage unit 12, and can read out and use the generated surrogate model 120 from the storage unit 12 if the data subsequently input to the surface reaction model 106 is within the range guaranteed by the stored surrogate model 120. It is preferable that the information processing device 1 stores, as information about the surrogate model 120, for example, information about the structure and internal parameters of the surrogate model 120, as well as information about the range of values of the input data guaranteed by this surrogate model 120.
  • the information processing device 1 needs to generate and update the surrogate model 120.
  • the previous surrogate model 120 may be stored without being deleted from the storage unit 12.
  • the information processing device 1 can reuse the stored surrogate model 120 without generating a new surrogate model 120.
  • the information processing device 1 may generate multiple surrogate models 120 with different guaranteed ranges and store them in the storage unit 12.
  • the information processing device 1 when the information processing device 1 generates and stores the surrogate models 120, if the information processing device 1 stores enough surrogate models 120 to cover the entire range of possible input data to the surface reaction model 106, it is only necessary to appropriately select and use the generated surrogate models 120, and the information processing device 1 does not need to generate the surrogate models 120.
  • FIG. 5 is a flowchart showing an example of the procedure of the proxy model generation process performed by the information processing device 1 according to this embodiment.
  • the simulation processing unit 11a of the processing unit 11 of the information processing device 1 according to this embodiment performs a simulation of the substrate processing device 3 using the large-scale model 100.
  • the processing unit 11 determines whether or not data has been input from other small-scale models to a small-scale model to be represented included in the large-scale model 100, for example, the surface reaction model 106 (step S1). If there is no input to the surface reaction model 106 (S1: NO), the processing unit 11 continues to perform the simulation using the large-scale model 100 until data is input to the surface reaction model 106.
  • the update determination unit 11c of the processing unit 11 determines whether or not it is necessary to update the proxy model 120 of the surface reaction model 106 based on the data input to the surface reaction model 106 (step S2).
  • the proxy model generation unit 11b of the processing unit 11 reads out data of a local range corresponding to the data input to the surface reaction model 106 from the data stored in the input/output DB 20 (step S3).
  • the proxy model generation unit 11b generates the proxy model 120 of the surface reaction model 106 by appropriately using an existing model generation algorithm based on the data in which the input data and the output data read from the input/output DB 20 are associated (step S4).
  • the proxy model generation unit 11b stores information about the generated proxy model 120, such as information indicating the structure of the proxy model 120 and information such as internal parameters, in the storage unit 12 (step S5), and proceeds to step S7. Furthermore, if updating of the proxy model 120 is not necessary (S2: NO), the processing unit 11 reads information about the proxy model 120 stored in the memory unit 12 (step S6) and proceeds to step S7.
  • the processing unit 11 After generating the surrogate model 120 in steps S3 to S5, or after reading out the surrogate model 120 in step S6, the processing unit 11 inputs the input data input to the surface reaction model 106 in step S1 to the surrogate model 120 (step S7).
  • the processing unit 11 acquires output data output by the surrogate model 120 in response to the data input in step S7 (step S8).
  • the processing unit 11 provides the output data of the surrogate model 120 acquired in step S8 as output data for the surface reaction model 106, for example, to the next-stage small-scale model (step S9), and returns the process to step S1.
  • the information processing device 1 determines whether or not the proxy model 120 needs to be updated depending on whether or not the value of the input data to the small-scale model that is the surrogate of the proxy model 120 is within the guaranteed range of the proxy model 120, but the conditions for determining whether or not an update is necessary are not limited to this.
  • the information processing device 1 according to the modified example determines whether or not an update is necessary depending on, for example, the data accuracy required for the output data of the small-scale model that is the surrogate of the proxy model 120.
  • the information processing device 1 according to the modified example accepts settings related to the accuracy of the simulation or settings related to the accuracy of the proxy model 120 from the user.
  • the surrogate model generating unit 11b of the information processing device generates a surrogate model 120 capable of outputting output data according to the accuracy set by the user.
  • the surrogate model 120 can change the accuracy of the output data of the surrogate model 120 by changing the configuration of the surrogate model 120, for example, the number of parameters included in the surrogate model 120 or the number of terms in the arithmetic expression.
  • the surrogate model 120 can change the accuracy of the output data of the surrogate model 120 to be generated by increasing or decreasing the number of sets of input data and output data read from the input/output DB 20 and used for generation.
  • the surrogate model generating unit 11b can change the accuracy of the output data of the surrogate model 120 by increasing or decreasing the range of values of the input data guaranteed by the surrogate model 120.
  • the surrogate model generating unit 11b may change the accuracy of the output data of the surrogate model 120 by any of these means, may adopt other means, or may use a combination of multiple means.
  • the proxy model generating unit 11b can generate the proxy model 120 of the quadratic function with improved accuracy by using the values of the parameters (coefficients) b and c of the already generated proxy model 120 of the linear function as the parameters b and c of the proxy model 120 of the quadratic function as they are, and determining the value of a new parameter a of the quadratic function using the data of the input/output DB 20.
  • the proxy model generation unit 11b may not use the parameters b and c as they are, but may use them as initial values of the parameters b and c of the new quadratic function proxy model 120, and may determine the final values of the parameters b and c from these initial values. Furthermore, the reuse of parameters as described above is one example, and is not limited to this, and the proxy model generation unit 11b may use any information of the already generated proxy model 120 to generate a new proxy model 120.
  • the information processing device 1 stores in the storage unit 12 information on the range of input data values guaranteed by the proxy model 120 and information on the accuracy output by the proxy model 120, in association with information on the configuration, parameters, etc. of the generated proxy model 120.
  • the information processing device 1 according to the modified example can obtain the accuracy setting made by the user and determine whether or not the proxy model 120 needs to be updated depending on whether or not the stored proxy model 120 matches this accuracy setting.
  • the first application example is abnormality determination of the substrate processing device 3.
  • the information processing device 1 can determine an abnormality of the arm device by using a large-scale model 100 that models an arm device that transports a substrate as the substrate processing device 3.
  • the large-scale model 100 accepts, for example, the torque of the arm device as input data, and outputs a predicted value of the movement speed of the arm as output data.
  • the information processing device 1 acquires input data of the torque to the arm device, performs a simulation of the large-scale model 100, and predicts the movement speed of the arm of the arm device according to the input torque.
  • the information processing device 1 acquires an actual measurement value of the movement speed of the arm of the arm device according to the same input data, and determines that an abnormality has occurred in the arm device when the difference value between the predicted value and the actual value of the movement speed exceeds a predetermined threshold value.
  • FIG. 6 is a flow chart showing an example of the procedure of the abnormality determination process performed by the information processing device 1 according to this embodiment.
  • the abnormality determination unit 11d of the processing unit 11 of the information processing device 1 according to this embodiment communicates with the substrate processing device 3, for example, via the communication unit 13, and acquires input data of torque for the arm device (step S21).
  • the simulation processing unit 11a of the processing unit 11 inputs the acquired input data to the large-scale model 100 (step S22), and performs a simulation of the operation of the substrate processing device 3 using the large-scale model 100.
  • the information processing device 1 according to this embodiment can perform a simulation for one or more small-scale models included in the large-scale model 100, using the above-mentioned proxy model 120.
  • the simulation processing unit 11a acquires output data output by the large-scale model 100 through the simulation (step S23).
  • the abnormality determination unit 11d communicates with the substrate processing apparatus 3, for example, through the communication unit 13, and acquires an actual measurement value of the movement speed of the arm apparatus relative to the input data acquired in step S21 (step S24).
  • the abnormality determination unit 11d calculates the difference between the predicted value of the output data acquired in step S23 and the actual value of the output data acquired in step S24 (step S25).
  • the abnormality determination unit 11d judges whether the difference calculated in step S25 exceeds a predetermined threshold value (step S26).
  • the abnormality determination unit 11d judges that there is an abnormality in the substrate processing apparatus 3 (step S27) and ends the process. If the difference does not exceed the threshold value (S26: NO), the abnormality determination unit 11d judges that there is no abnormality in the substrate processing apparatus 3 (step S28) and ends the process.
  • the second use case is the optimization of the settings of the substrate processing apparatus 3.
  • the user sets a target value for the result of substrate processing by the substrate processing apparatus 3, such as an etch rate, and the information processing apparatus 1 optimizes the settings of the substrate processing apparatus 3 so as to achieve this target value.
  • FIG. 7 is a flow chart showing an example of the procedure of the optimization process performed by the information processing apparatus 1 according to this embodiment.
  • the processing unit 11 of the information processing apparatus 1 determines the initial values of the settings of the substrate processing apparatus 3 to be optimized, i.e., the initial values of the input data to the large-scale model 100 (step S41).
  • the initial values may be determined by the user, in which case the information processing apparatus 1 determines the initial values by accepting input from the user.
  • the simulation processing section 11a of the processing section 11 inputs the determined or updated input data to the large-scale model 100 that models the substrate processing apparatus 3 (step S42), and performs a simulation of the operation of the substrate processing apparatus 3 using the large-scale model 100.
  • the information processing apparatus 1 can perform a simulation using the above-mentioned proxy model 120 for one or more small-scale models included in the large-scale model 100.
  • the simulation processing section 11a acquires the output data output by the large-scale model 100 through the simulation (step S43).
  • the processing unit 11 calculates the error between a preset target value and the value of the output data acquired in step S43 (step S44). Next, the processing unit 11 determines whether or not an optimization termination condition, such as the error calculated in step S44 being smaller than a predetermined threshold, is satisfied (step S45). Note that termination conditions may include not only those based on error, but also various other conditions, such as the processing time reaching an upper limit or the number of iterations reaching an upper limit.
  • the processing unit 11 updates the input data to the large-scale model 100 based on an existing algorithm such as the steepest descent method or Newton's method so that the output data approaches the target value (step S46), and returns the process to step S42.
  • the processing unit 11 repeats the processes of steps S42 to S46 until the termination condition is satisfied, thereby optimizing the input data to the large-scale model 100, i.e., optimizing the setting values to be input to the substrate processing apparatus 3.
  • the processing unit 11 stores the value of the input data at this point in time as an optimal value in the storage unit 12 (step S47), and terminates the process.
  • the information processing device 1 performs a simulation using a large-scale model 100 including a plurality of small-scale models.
  • the information processing device 1 stores in advance in the input/output DB 20 of the storage unit 12 the correspondence between input data and output data for a predetermined small-scale model (e.g., the surface reaction model 106) included in the large-scale model 100.
  • the other small-scale models included in the large-scale model 100 are generated in advance and information such as parameters is stored in the model information storage unit 12b, but the predetermined small-scale model is not generated in advance, and the large-scale model 100 includes information regarding the predetermined small-scale model, such as the format of input/output data.
  • the information processing device 1 When data is input to the predetermined small-scale model in a simulation using the large-scale model 100, the information processing device 1 generates a proxy model 120 that represents the small-scale model in a local range including the input data, based on the data stored in the input/output DB 20. The information processing device 1 uses the generated proxy model 120 to input input data for a specified small-scale model to the proxy model 120 and obtains output data output by the proxy model 120, thereby generating output data corresponding to the input data to the specified small-scale model.
  • the information processing system according to this embodiment does not need to generate in advance small-scale models that are difficult to model or take a long time to model, for example, for the entire range of inputs and outputs, and therefore can perform simulations using the large-scale model 100 at an early stage.
  • the local proxy model 120 can be generated in a short time, and simulations using the proxy model 120 can also be performed quickly. Therefore, compared to a case in which all small-scale models of the large-scale model 100 are generated in advance and a simulation is performed, the information processing system according to this embodiment is expected to shorten the time required from modeling the target device to completing the simulation, and is expected to speed up verification by simulations using large-scale models, etc.
  • the information processing device 1 determines whether or not an update is required for the generated proxy model 120, depending on the data input to a specific small-scale model in a simulation using the large-scale model 100. If the information processing device 1 determines that an update is required, it generates a proxy model 120 according to the data input to the small-scale model, based on the data stored in the input/output DB 20. By generating the proxy model 120 only when it is determined that generation is required, rather than generating it every time, the information processing system according to this embodiment is expected to shorten the time required for a simulation using the large-scale model 100, compared to generating the proxy model 120 every time.
  • the information processing device 1 stores the generated proxy model 120 in the storage unit 12.
  • the information processing device 1 determines that updating is unnecessary if a proxy model 120 corresponding to input data input to a specific small-scale model in a simulation using the large-scale model 100 has already been stored.
  • the information processing device 1 uses the generated proxy model 120 stored in the storage unit 12 to generate output data for a specific small-scale model corresponding to the input data.
  • the information processing system according to this embodiment can reuse the proxy model 120 that has been generated once, which is expected to reduce the frequency of generating the proxy model 120 and speed up the simulation.
  • the information processing device 1 determines that the proxy model 120 needs to be updated if the data input to a specified small-scale model exceeds the local range guaranteed by the generated proxy model 120. Furthermore, in the information processing system according to this embodiment, the information processing device 1 acquires information regarding the accuracy required for the data output by the proxy model 120, and determines that the proxy model 120 needs to be updated if the generated proxy model 120 does not meet this accuracy. By determining whether an update is necessary based on these conditions, the information processing system according to this embodiment is expected to appropriately generate the proxy model 120.
  • the information processing device 1 selects a set of input data and output data to be used for generating the proxy model 120 according to the input data to a predetermined small-scale model from among the sets of input data and output data stored in the input/output DB 20, and generates the proxy model 120 using the selected data.
  • the information processing system according to this embodiment can be expected to select appropriate data from the large number of data stored in the input/output DB 20 in order to generate the proxy model 120 that can guarantee output data within a local range of the input data.
  • the information processing device 1 groups sets of input data and output data related to a predetermined small-scale model that have been collected in advance according to the values of the input data or output data, and stores them in the input/output DB 20.
  • the information processing system according to this embodiment is expected to facilitate the selection of sets of input data and output data to be used in generating the proxy model 120.
  • the information processing device 1 compares actual values obtained by measuring the operation of a target device, such as the substrate processing device 3, with predicted values obtained by a simulation using a large-scale model 100 that models the target device, and detects an abnormality in the target device based on the comparison result. For example, when the difference between the actual value and the predicted value exceeds a threshold value, the information processing device 1 determines that there is an abnormality in the target device.
  • the information processing system according to this embodiment is expected to accurately predict the operation of the target device through a simulation using the large-scale model 100, and accurately determine an abnormality in the target device.
  • the information processing device 1 predicts output data of the target device by a simulation using the large-scale model 100 based on input data to the target device, calculates the error between the predicted value of the output data and a target value, and updates the input data to the target device based on the calculated error.
  • the information processing device 1 repeatedly performs a simulation using the large-scale model 100 and updates the input data based on the error to determine input data for the target device that will achieve the target value.
  • the information processing system according to this embodiment is expected to accurately determine input data that will enable the target device to achieve the target value by a simulation using the large-scale model 100, and achieve the target value by operating the target device using the determined input data.
  • Information processing device (computer) 3 Substrate processing apparatus 11 Processing section 11a Simulation processing section 11b Representative model generating section 11c Update determining section 11d Abnormality determining section 11e Display processing section 12 Storage section 12a Program (computer program) 12b Model information storage unit 20 Input/output DB 99 Recording medium 100 Large-scale model 101 Power supply model (small-scale model) 102 Heater model (small scale model) 103 Chiller model (small-scale model) 104 RF model (small-scale model) 105 Surface temperature model (small scale model) 106 Surface reaction model (prescribed small scale model) 120 surrogate model

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JP2021512422A (ja) * 2018-02-05 2021-05-13 ジール・アベッグ エスエー ファンの動作やファン配置を最適化する方法
JP2022021214A (ja) * 2020-07-21 2022-02-02 富士通株式会社 最適化装置、最適化方法および最適化プログラム
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JP2022021214A (ja) * 2020-07-21 2022-02-02 富士通株式会社 最適化装置、最適化方法および最適化プログラム
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