Process result prediction method, information processing device, and program

Abstract

This technology provides a method to improve the prediction accuracy of predictive models that predict process result information according to process recipes. [Solution] A method for predicting process results performed by an information processing device, comprising: extracting multiple feature items from a process recipe of a dataset associated with a process recipe, a process log of a substrate processing device that executed a process according to the process recipe, and process result information of a substrate processing device that executed a process according to the process recipe; receiving a selection from an operator of feature items to be used for classifying the dataset from the extracted multiple feature items; and predicting process result information using a classification-specific prediction model that has been machine-trained using the dataset that matches the feature items selected by the operator.

Description

Technical Field

[0001] The present disclosure relates to a process result prediction method, an information processing apparatus, and a program.

Background Art

[0002] For example, a semiconductor manufacturing process is performed by a process of repeatedly depositing films and etching on a substrate to be processed. In the development site, the search for an optimal process recipe is carried out every day. The search for an optimal process recipe may be performed using a model that machine-learns the correspondence between a process recipe, a process log, and a process result (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present disclosure provides a technique for improving the prediction accuracy of a prediction model that predicts process result information according to a process recipe.

Means for Solving the Problems

[0005] One aspect of the present disclosure is a process result prediction method performed by an information processing device, comprising: extracting a plurality of feature items from a process recipe of a dataset associated with a process recipe, a process log of a substrate processing device that has executed a process according to the process recipe, and process result information of a substrate processing device that has executed a process according to the process recipe; receiving a selection from an operator of the feature items to be used for classifying the dataset from the extracted plurality of feature items; and predicting the process result information using a prediction model for each classification that has been machine-trained using the dataset that matches the feature items selected by the operator. [Effects of the Invention]

[0006] This disclosure provides a technology to improve the prediction accuracy of a predictive model that predicts process result information according to a process recipe. [Brief explanation of the drawing]

[0007] [Figure 1] This is a diagram illustrating an example of a process result prediction system according to this embodiment. [Figure 2] This is a hardware configuration diagram of an example of an information processing device. [Figure 3] This is a functional block diagram of an example of a server device that performs the process result prediction method according to this embodiment. [Figure 4] This is an example image of a screen that displays multiple extracted feature items and allows the user to select which feature items to use for classifying the dataset. [Figure 5] This is a flowchart illustrating an example of the processing of the process result prediction system according to this embodiment. [Figure 6] This is an explanatory diagram of an example of a process recipe included in the dataset. [Figure 7] This is a diagram illustrating an example of several feature items extracted from the process recipe of the dataset. [Figure 8] This is a diagram illustrating an example of several feature items extracted from the process recipe of the dataset. [Figure 9] This is an example image of a screen that displays multiple feature items extracted in step S12 and allows the operator to select the feature items to be used for classifying the dataset. [Figure 10] This is an example image of a screen that displays process result information predicted using a prediction model for each classification. [Figure 11] This is a flowchart illustrating an example of a process for creating a new classification prediction model using machine learning with outliers. [Figure 12] This is an example image of a screen that displays process result information predicted using prediction models for each further subdivided category. [Figure 13] This is an example image of a screen that displays process result information predicted using a prediction model for each classification. [Figure 14] This is an example image of a screen displaying process result information for substrate processing equipment predicted using a prediction model for each classification. [Figure 15] This is an illustrative image of an example screen that searches for and displays process recipes that meet the target process result information for the operator. [Modes for carrying out the invention]

[0008] The embodiments for carrying out the present invention will be described below with reference to the drawings.

[0009] <System Configuration> Figure 1 is a configuration diagram of an example of a process result prediction system according to this embodiment. The process result prediction system 1 includes a substrate processing device 10, a measuring device 11, a device controller 12, a server device 14, a database device 15, and a worker terminal 16. The substrate processing device 10, the measuring device 11, and the device controller 12 are installed in the manufacturing plant 2. The server device 14, the database device 15, and the worker terminal 16 may be installed in the manufacturing plant 2 or elsewhere. The substrate processing device 10, the measuring device 11, the device controller 12, the server device 14, the database device 15, and the worker terminal 16 are connected to each other via networks 18 and 20 such as the Internet and LAN (Local Area Network).

[0010] The substrate processing apparatus 10 is a device that performs the manufacturing process of semiconductors and the like. The manufacturing process of semiconductors and the like is carried out by a process that involves repeatedly depositing films and etching on a substrate to be processed. The substrate processing apparatus 10 is an example of a device that executes a process according to a process recipe and outputs a process log. The process recipe shows the procedure of the process that the substrate processing apparatus 10 will execute. For example, the process recipe is set to divide the process into multiple steps (sections). The process log is information such as sensor values ​​that shows the status of the substrate processing apparatus 10 while it is executing the process according to the recipe, and is sometimes called process history information. The substrate processing apparatus 10 may also be a film deposition apparatus, an etching apparatus, a semiconductor manufacturing apparatus, or a heat treatment apparatus.

[0011] The substrate processing apparatus 10 receives control commands according to a process recipe from the apparatus controller 12 and executes a process. As shown in FIG. 1, the substrate processing apparatus 10 may be equipped with the apparatus controller 12, or it does not necessarily have to be equipped with the apparatus controller 12 as long as it is communicably connected. The apparatus controller 12 outputs control commands for controlling adjustment items (control components) of the substrate processing apparatus 10 according to a process recipe, thereby causing the substrate processing apparatus 10 to execute a process according to the process recipe. In addition, the apparatus controller 12 has a function of a man-machine interface that receives instructions for the substrate processing apparatus 10 from an operator and provides information about the substrate processing apparatus 10 to the operator.

[0012] The measurement apparatus 11 measures, as process result information of the substrate processing apparatus 10 that has executed a process according to a process recipe, the film thickness, resistance, impurity concentration, leakage current, etc. of a processed substrate (e.g., a semiconductor wafer) to be processed after the process.

[0013] The database apparatus 15 stores and manages various types of information necessary for the processing of the process result prediction system 1 according to the present embodiment. For example, the database apparatus 15 may store and manage the process recipe executed by the substrate processing apparatus 10, the process log of the substrate processing apparatus 10 that has executed a process according to the process recipe, and the process result information of the substrate processing apparatus 10 that has executed a process in the substrate processing apparatus 10 according to the process recipe.

[0014] In addition, the database apparatus 15 may store and manage a data set (hereinafter simply referred to as a data set) in which the process recipe executed by the substrate processing apparatus 10, the process log of the substrate processing apparatus 10 that has executed a process according to the process recipe, and the process result information of the substrate processing apparatus 10 that has executed a process according to the process recipe are associated with each other.

[0015] The server device 14 obtains a dataset from the database device 15. The server device 14 may also obtain from the database device 15 the process recipe executed by the substrate processing device 10, the process log of the substrate processing device 10 that executed the process according to that process recipe, and the process result information of the substrate processing device 10 that executed the process according to that process recipe, and associate them to create a dataset.

[0016] The correspondence between the process recipe executed by the substrate processing apparatus 10, the process log of the substrate processing apparatus 10 that executed the process according to that process recipe, and the process result information of the substrate processing apparatus 10 that executed the process according to that process recipe may be performed by an operator.

[0017] The server device 14 extracts multiple feature items from the process recipe of the dataset, as described below. Feature items are items used to define the process content in the process recipe. The server device 14 accepts from the operator the selection of feature items to be used for classifying the dataset from the extracted multiple feature items, as described below. The server device 14 creates a predictive model for each classification (category) using machine learning with the dataset that matches the feature items selected by the operator, as described below. By accepting from the operator the selection of feature items to be used for classifying the dataset, this embodiment makes it possible to create a predictive model that has been machine-learned on the dataset of the classification intended by the operator. Furthermore, the server device 14 uses the created predictive model for each classification to predict the process result information of the substrate processing apparatus 10 that executes the process according to the process recipe, as described below.

[0018] Furthermore, the worker terminal 16 is a PC (Personal Computer) or smartphone operated by a worker, such as the equipment operator or analysis operator, of the substrate processing device 10 installed in the manufacturing plant 2. The worker terminal 16 may transmit the operator's operations to the server device 14 and receive and display the processing results from the server device 14.

[0019] It should be noted that the process result prediction system 1 shown in Figure 1 is just one example, and there are various system configurations depending on the application and purpose. For example, at least part of the processing performed by the server device 14 described above may be performed by the device controller 12 or the worker terminal 16. The device controller 12, the server device 14, or the worker terminal 16 are examples of information processing devices that perform the process result prediction method of this embodiment. Furthermore, the substrate processing device 10 equipped with the device controller 12 that performs the processing of the server device 14 described above is an example of a substrate processing device 10 that performs the process result prediction method of this embodiment. The server device 14 may be implemented by multiple information processing devices, or it may be implemented as a cloud computing service.

[0020] The process result prediction system 1 shown in Figure 1 may be implemented using a standalone substrate processing apparatus 10 and apparatus controller 12. The process result prediction system 1 shown in Figure 1 may have an integrated configuration in which at least a portion of the server apparatus 14, database apparatus 15, and worker terminal 16 are further separated.

[0021] <Hardware Configuration> The device controller 12, server device 14, database device 15, and worker terminal 16 of the process result prediction system 1 in Figure 1 are realized by, for example, an information processing device 500 with the hardware configuration shown in Figure 2. Figure 2 is a hardware configuration diagram of an example of an information processing device.

[0022] The information processing device 500 in Figure 2 includes an input device 501, an output device 502, an external interface 503, a RAM (Random Access Memory) 504, a ROM (Read Only Memory) 505, a CPU (Central Processing Unit) 506, a communication interface 507, and an HDD (Hard Disk Drive) 508, all of which are interconnected via bus B. The input device 501 and output device 502 may be connected and used only when necessary.

[0023] The input device 501 is a keyboard, mouse, touch panel, etc., and is used by the operator to input various operation signals. The output device 502 is a display, etc., and displays the processing results from the information processing device 500.

[0024] Communication I / F 507 is an interface that connects the information processing device 500 to network 18 or 20. HDD 508 is an example of a non-volatile storage device that stores programs and data.

[0025] External I / F 503 is an interface to external devices. The information processing device 500 can read from and / or write to a recording medium 503a such as an SD (Secure Digital) memory card via the external I / F 503. ROM 505 is an example of a non-volatile semiconductor memory (storage device) in which programs and data are stored. RAM 504 is an example of a volatile semiconductor memory (storage device) for temporarily holding programs and data.

[0026] The CPU 506 is a processing unit that reads programs and data from storage devices such as the ROM 505 and HDD 508 onto the RAM 504 and executes processing, thereby realizing the overall control and functions of the information processing device 500.

[0027] The device controller 12, server device 14, database device 15, and worker terminal 16 in Figure 1 can implement various functions described later by executing a program on the information processing device 500 with the hardware configuration shown in Figure 2.

[0028] <Functional Configuration> In the following, an example is described in which the information processing device 500 that performs the process result prediction method according to this embodiment is a server device 14. The information processing device 500 that performs the process result prediction method according to this embodiment may be a device controller 12 or an operator terminal 16.

[0029] The server device 14 that performs the process result prediction method according to this embodiment is implemented, for example, by the functional blocks shown in Figure 3. Figure 3 is a functional block diagram of an example of the server device 14 that performs the process result prediction method according to this embodiment. Note that the functional block diagram in Figure 3 omits the illustration of configurations that are not necessary for the explanation of this embodiment.

[0030] The server device 14 in Figure 3 executes a program for the server device 14 and implements a dataset acquisition unit 50, a dataset storage unit 52, an extraction unit 54, a prediction model creation unit 56, a reception unit 58, a prediction unit 60, a display unit 62, a process recipe acquisition unit 64, a process result information acquisition unit 66, and a search unit 68.

[0031] The dataset acquisition unit 50 acquires a dataset and stores it in the dataset storage unit 52. The dataset may be acquired from the database device 15, or it may be created by associating the process recipe executed by the substrate processing device 10, the process log of the substrate processing device 10 that executed the process according to that process recipe, and the process result information of the substrate processing device 10 that executed the process according to that process recipe, all of which were acquired from the database device 15.

[0032] The extraction unit 54 extracts multiple feature items from the process recipe of the dataset stored in the dataset storage unit 52. For example, the extraction unit 54 recognizes a STEP in which gas is flowing to the substrate to be processed as a film deposition STEP from the process recipe, and extracts multiple feature items from the film deposition STEP. The feature items extracted from the process recipe include, for example, the type of gas, the order of gas release, the number of STEPs, and the plasma.

[0033] The display unit 62 displays the multiple feature items extracted by the extraction unit 54, for example, as shown in Figure 4. Figure 4 is an image diagram of an example screen that displays the multiple extracted feature items and accepts the selection of feature items to be used for classifying the dataset from the operator.

[0034] Figure 4(a) shows the screen image before the operator selects the feature items to be used for classifying the dataset. Figure 4(b) shows the screen image after the operator selects the gas type (gas A, gas B, gas C, and gas D) as the feature items to be used for classifying the dataset. Figure 4(c) shows the screen image after the operator selects the gas release order (A / C / B / C, A / C / A / C / B / C, and A / D / C / B / C) as the feature items to be used for classifying the dataset, in addition to the state shown in Figure 4(b).

[0035] The reception unit 58 receives, for example, the selection of feature items to be used for classifying the dataset from the operator on the screen shown in Figure 4. The feature items selected by the operator are notified from the reception unit 58 to the prediction model creation unit 56 via the extraction unit 54.

[0036] The prediction model creation unit 56 creates a model equation for the prediction model using key adjustment items (also called key process knobs) that affect the process results, and then uses machine learning to machine-train the model equation using a dataset that matches the feature items selected by the operator to create a prediction model for each classification. The prediction model creation by the prediction model creation unit 56 is performed each time the operator selects the feature items to be used for classifying the dataset.

[0037] The prediction unit 60 predicts the process result information of the substrate processing apparatus 10, which has executed a process according to the process recipe, using the classification-based prediction model created by the prediction model creation unit 56, and displays it on the display unit 62. The process recipe acquisition unit 64 acquires the process recipe that will cause the prediction unit 60 to predict the process result information, and notifies the prediction unit 60.

[0038] The search unit 68 uses the process result information predicted by the prediction unit 60 to search for a process recipe that satisfies the process result information targeted by the worker and displays it on the display unit 62. The search unit 68 searches for a process recipe that satisfies the process result information targeted by the worker by performing inverse problem analysis using prediction models for each classification. The process result information acquisition unit 66 acquires the process result information targeted by the worker and notifies the search unit 68.

[0039] In the process result prediction system 1 according to this embodiment, in order to improve the prediction accuracy of the prediction model that predicts process result information, it is necessary to classify datasets of similar process recipes (for example, identical gas type, identical gas release order, identical number of steps) as datasets to be used for machine learning.

[0040] Dataset classification can be performed mechanically using clustering methods such as the k-means method. However, clustering methods such as the k-means method may classify data sets in a way that contradicts the operator's intentions, and the prediction accuracy of predictive models trained on that dataset may be significantly reduced. Furthermore, the k-means method has problems such as accuracy varying depending on how the initial values ​​of the centroids are chosen, and the operator having to set the number of clusters.

[0041] Therefore, in the process result prediction system 1 according to this embodiment, by accepting the selection of feature items to be used for classifying the dataset from the operator, it is possible to suppress the classification of the dataset that goes against the operator's intentions. Furthermore, in the process result prediction system 1 according to this embodiment, by classifying the dataset in a way that reflects the operator's intentions, it is possible to create a prediction model of the classification intended by the operator with high prediction accuracy.

[0042] <Processing> Figure 5 is a flowchart showing an example of the processing of the process result prediction system according to this embodiment.

[0043] In step S10, the data set acquisition unit 50 of the server device 14 acquires a data set and stores it in the data set storage unit 52. The data set stored in the data set storage unit 52 in step S10 includes, for example, the process recipe shown in Figure 6.

[0044] Figure 6 is an explanatory diagram of an example of a process recipe included in the dataset. As shown in Figures 6(a) and 6(b), the process recipe has information such as temperature, pressure, gas, and number of repetitions set for each STEP.

[0045] Figure 6(a) shows an example of a process recipe with "4" steps and gas types "Gas A, Gas B, and Gas C". Figure 6(b) shows an example of a process recipe with "5" steps and gas types "Gas A, Gas B, Gas C, and Gas D".

[0046] Gas A is, for example, TiCl4. Gas B is, for example, NH3. Gas C is, for example, H2. Gas D is, for example, SiH4. For example, Gas A may be WCl5, WF6, TaCl5, or AlCl3. Gas B may be N2 gas, N2H4 gas, or N2H2 gas. Gas C may be diborane (B2H6) gas, phosphine (PH3) gas, dichlorosilane (SiH2Cl2) gas, N2 gas, or Ar gas. Gas D may be SiH4 gas, Si2H6 gas, BH3 gas, or B2H6 gas.

[0047] In step S12, the extraction unit 54 reads the dataset from the dataset storage unit 52 and extracts multiple feature items from the process recipe of the dataset. Figures 7 and 8 show examples of multiple feature items extracted from the process recipe of the dataset. The multiple feature items in Figures 7 and 8 have a hierarchical structure.

[0048] Note that the multiple feature items extracted from the process recipe in the dataset may have different content, as shown in Figures 7 and 8. Also, the names of the multiple feature items extracted from the process recipe in the dataset may be different, as shown in "Gas Type" in Figure 7 and "Gas / Raw Material Type" in Figure 8.

[0049] This section explains several feature items in Figure 7. Several feature items in Figure 7 indicate that the process recipe of the acquired dataset is set to gas A, gas B, gas C, or gas D. Several feature items in Figure 7 also indicate that the process recipe of the acquired dataset is set to gas emission order A / B / C, A / B / C / D, A / C / B / C, A / D / C / B / C, A / C / A / C / B / C, and A / D / A / C / B / C). Several feature items in Figure 7 also indicate that the process recipe of the acquired dataset is set to STEP number "4" or "5". Several feature items in Figure 7 indicate that the process recipe of the acquired dataset is set to plasma "DC Power" or "RF Power". Several feature items in Figure 7 indicate that the plasma "DC Power" or "RF Power" of the process recipe of the acquired dataset is set to "0[W]" or "100[W]".

[0050] In step S14, the display unit 62 displays a screen 1000, such as the one shown in Figure 9, which accepts the selection of feature items from the operator. Figure 9 is an illustrative image of an example screen that displays multiple feature items extracted in step S12 and accepts the selection of feature items to be used for classifying the dataset from the operator.

[0051] In Figure 9, screen 1000 displays the multiple feature items extracted in step S12 in display area 1002. The operator can select the feature items to be used for classifying the dataset by operating the checkboxes displayed in display area 1002.

[0052] In step S16, the reception unit 58 determines whether or not it has received a selection of feature items from the operator. If it has not received a selection of feature items from the operator, it proceeds from step S16 to step S24, where the reception unit 58 determines whether or not it has received an instruction from the operator to finish creating the prediction model. If it has not received an instruction from the operator to finish creating the prediction model, the reception unit 58 returns to the process in step S16. Once it receives an instruction from the operator to finish creating the prediction model, the process in the flowchart shown in Figure 5 is completed.

[0053] In step S16, if the reception unit 58 determines that it has received the selection of feature items from the worker, it proceeds to the process in step S18. In step S18, the prediction model creation unit 56 is notified of the feature items selected by the worker. The prediction model creation unit 56 creates a model equation for the prediction model using adjustment items that affect the process results.

[0054] Adjustment items that affect the process results may be pre-set or selected using machine learning or the like. The adjustment items that affect the process results may be items from the data included in the process recipe or process log (such as adjustment values ​​for the substrate processing device 10). The adjustment values ​​for the substrate processing device 10 include gas flow rate, temperature, pressure, STEP time, and the number of repetitions of a predetermined STEP.

[0055] In step S20, the prediction model creation unit 56 uses a dataset that matches the feature items selected by the operator to machine-learn the model equation of the prediction model created in step S18, thereby creating a prediction model for each classification. In this embodiment, the model equation of the prediction model can be machine-learned using a dataset classified to reflect the operator's intent. The machine learning in step S20 is a process of adjusting the coefficients of the model equation of the prediction model so that the process result information predicted by the prediction model based on the process recipe included in the dataset that matches the feature items selected by the operator approximates the process result information included in the dataset.

[0056] In step S22, the prediction unit 60 predicts process result information using the classification-specific prediction model created in step S20. The display unit 62 displays the process result information predicted by the prediction unit 60, for example, as shown in screen 1000 of Figure 10, and then returns to the process in step S14.

[0057] Figure 10 is an illustrative image of an example screen that displays process result information predicted using a prediction model for each classification. In the display area 1002 of screen 1000 in Figure 10, the checkboxes for the feature items selected by the worker in step S16 are checked. Furthermore, screen 1000 in Figure 10 shows an example of displaying the process result information predicted using a prediction model for each classification in a graph.

[0058] In Figure 10, screen 1000 shows an example of film thickness, illustrating process result information predicted using a classification-specific prediction model. The graph shows the relationship between the predicted film thickness value predicted using the classification-specific prediction model and the actual film thickness value measured by the measuring device 11. Below the graph, the feature items selected by the operator and the created classification-specific prediction models are displayed.

[0059] Note that in Figure 10, screen 1000 displays graphs corresponding to the prediction models "model1-1" and "model1-2" for each classification, but it is also acceptable to display only the graph corresponding to the prediction model "model1-1" for each classification. The graph corresponding to the prediction model "model1-2" for each classification may also be a graph corresponding to a new classification prediction model that was created using machine learning with outliers.

[0060] Figure 11 is a flowchart of an example of the process for creating a new classification prediction model using machine learning with outliers. The process in the flowchart of Figure 11 is performed, for example, in step S20 of Figure 5.

[0061] In step S30, the prediction model creation unit 56 acquires a dataset that matches the feature items selected by the operator. In step S32, the prediction model creation unit 56 uses the dataset acquired in step S32 to machine-learn the model formula of the prediction model and creates a prediction model for each classification.

[0062] In step S34, the prediction model creation unit 56 determines whether there are outliers in the process result information predicted using the prediction models created for each classification. If there are no outliers, the prediction model creation unit 56 terminates the process shown in Figure 11. If there are outliers, the prediction model creation unit 56 proceeds to step S36, uses the outlier dataset to machine-learn the model equation of the prediction model, and creates a new prediction model for a new classification.

[0063] In screen 1000 of Figure 10, the operator can further select feature items to be used for classifying the dataset by manipulating the checkboxes displayed in display area 1002.

[0064] In step S16, if the reception unit 58 determines that it has received the selection of feature items from the worker, it proceeds to the processing in steps S18 to S22. The prediction unit 60 predicts process result information using the prediction model for each classification created in step S20. The display unit 62 displays the process result information predicted by the prediction unit 60, for example, as shown in screen 1000 in Figure 12, and then returns to the processing in step S14.

[0065] Figure 12 is an illustrative image of an example screen displaying process result information predicted using prediction models for each further subdivided classification. Display field 1002 in Figure 12 shows the checkbox for "Gas release order," a feature item that was additionally selected by the operator, checked in display field 1002 in Figure 10. Also, screen 1000 in Figure 12 shows an example of displaying process result information predicted using the classification-specific prediction models "model1-1," "model1-2," and "model1-3" in a graph.

[0066] In Figure 12, screen 1000 shows an example of film thickness, illustrating process result information predicted using a classification-specific prediction model. The graph shows the relationship between the predicted film thickness value predicted using the classification-specific prediction model and the actual film thickness value measured by the measuring device 11. Below the graph, the feature items selected by the operator and the created classification-specific prediction models are displayed.

[0067] Furthermore, in Figure 12, screen 1000 displays a graph corresponding to "model1-1," a predictive model trained using a dataset containing a process recipe with a gas release sequence of "A / C / B / C." Also in Figure 12, screen 1000 displays a graph corresponding to "model1-2," a predictive model trained using a dataset containing a process recipe with a gas release sequence of "A / C / A / C / B / C." In addition, in Figure 12, screen 1000 displays a graph corresponding to "model1-3," a predictive model trained using a dataset containing a process recipe with a gas release sequence of "A / D / A / B / C."

[0068] Note that the graphs on screen 1000 shown in Figures 10 and 12 are examples. For example, the graph displayed on screen 1000 may be a graph showing the relationship between the predicted resistance value predicted using a prediction model for each classification and the actual measured film thickness value measured by the measuring device 11, as shown in Figure 13. Also, below the graph, the feature items selected by the operator and the prediction models for each classification that were created are displayed. In the graph of Figure 13, the relationship between film thickness and resistance is represented by a hyperbola.

[0069] The operator can create a highly accurate predictive model for the intended classification by repeatedly changing the selection of feature items in the display area 1002 of Figure 10, Figure 12, or Figure 13.

[0070] Furthermore, the operator may use a prediction model for the intended classification to predict the process result information of the substrate processing apparatus 10 that has executed the process according to the specified process recipe, and display it, for example, as shown in screen 1100 of Figure 14. Figure 14 is an illustrative image of an example screen that displays the process result information of the substrate processing apparatus predicted using a prediction model for each classification. Screen 1100 of Figure 14 displays the process result information 1102 of the substrate processing apparatus predicted using a prediction model for each classification, and a graph 1104 showing the trend of the process result information and the predicted value.

[0071] Alternatively, the operator may use a predictive model for the intended classification to search for process recipes that satisfy the target process result information, and display them, for example, as shown in screen 1200 of Figure 15. Figure 15 is an illustrative image of an example screen that searches for and displays process recipes that satisfy the target process result information set by the operator. The process result information acquisition unit 66 of the server device 14 acquires the process result information set by the operator.

[0072] The search unit 68 of the server device 14 searches for a process recipe that satisfies the process result information targeted by the operator, using the process result information predicted by the prediction unit 60. The search unit 68 can search for a process recipe that satisfies the process result information targeted by the operator, for example, by inverse problem analysis using a prediction model for each classification. The server's display unit 62 displays the process recipe 1202 found by the search unit 68, for example, as shown in screen 1200 of Figure 15. Alternatively, the search unit 68 may search for the optimal process recipe from a graph showing the relationship between the process result information predicted using a prediction model for each classification and the items included in the process recipe (number of repetitions, pressure, etc.).

[0073] While preferred embodiments of the present disclosure have been described in detail above, the present disclosure is not limited to the embodiments described above, and various modifications and substitutions can be made to the embodiments described above without departing from the scope of the present disclosure. [Explanation of Symbols]

[0074] 1. Process Result Prediction System 10 Substrate Processing Equipment 12. Device Controller 14 Server Devices 15 Database Devices 16. Worker terminal 18, 20 Network 50 Dataset Acquisition Section 54 Extraction part 56 Predictive Model Creation Department 58 Reception Department 60 Prediction Section 62 Display section 64 Process Recipe Acquisition Unit 66 Process Result Information Acquisition Unit 68 Search Department

Claims

1. A method for predicting process results performed by an information processing device, Extracting multiple feature items from a process recipe in a dataset that associates a process recipe with a process log of a substrate processing apparatus that performed a process according to the process recipe and process result information of a substrate processing apparatus that performed a process according to the process recipe, The operator is asked to select from the extracted feature items to be used for classifying the dataset, Using the predictive model for each classification, which has been machine-trained using the dataset that matches the characteristic items selected by the worker, the process result information is predicted. A method for predicting process results having the following characteristics.

2. Further selection of the aforementioned characteristic items will be accepted from the worker, Further predict the process result information using a more refined predictive model for each classification, which has been machine-trained using the dataset that matches the characteristic items further selected by the worker, A process result prediction method according to claim 1, comprising:

3. Using adjustment items that affect the process results, create a model equation for the predictive model for each classification, and use the dataset that matches the feature items selected by the operator to machine-learn the model equation and create a predictive model for each classification. A process result prediction method according to claim 1 or 2, further comprising:

4. Using outliers included in the process result information predicted by the aforementioned prediction model, the model equation is machine-trained to create a new classification prediction model. The process result prediction method according to claim 3, further comprising:

5. The process result information predicted by the prediction model based on the characteristic items selected by the operator is displayed graphically. A process result prediction method according to claim 1 or 2, further comprising:

6. The process result information predicted by the prediction model is graphically displayed using different types of graphs. The process result prediction method according to claim 5, further comprising:

7. The aforementioned feature items include the gas release sequence of the gases used in the process, A method for predicting process results according to claim 1 or 2.

8. Using the predictive model for each classification, search for the process recipe that satisfies the process result information targeted by the worker. A process result prediction method according to claim 1 or 2, further comprising:

9. The aforementioned process result information is the processing result for the substrate to be processed. A method for predicting process results according to claim 1 or 2.

10. An extraction unit extracts multiple feature items from a process recipe in a dataset that associates a process recipe with a process log of a substrate processing apparatus that has executed a process according to the process recipe and process result information of a substrate processing apparatus that has executed a process according to the process recipe. A reception unit that receives from an operator the selection of feature items to be used for classifying the dataset from the extracted multiple feature items, A prediction unit predicts the process result information using a prediction model for each classification that has been machine-trained using the dataset that matches the characteristic items selected by the operator, An information processing device having

11. In an information processing device, Extraction procedure for extracting multiple feature items from a process recipe in a dataset that associates a process recipe with a process log of a substrate processing apparatus that has executed a process according to the process recipe and process result information of a substrate processing apparatus that has executed a process according to the process recipe. A reception procedure for receiving from an operator the selection of feature items to be used for classifying the dataset from the extracted multiple feature items, A prediction procedure for predicting process result information using a prediction model for each classification that has been machine-trained using the dataset that matches the characteristic items selected by the operator, A program that executes the command.

Images