Information processing apparatus and performance evaluation method

Abstract

To provide a technique for improving easiness of performance evaluation of a recipe of a substrate processing apparatus.SOLUTION: The information processing apparatus includes a prediction unit configured to predict a film formation result of the substrate processing apparatus that performs a film formation process according to a recipe, a recipe performance evaluation unit configured to perform performance evaluation of the recipe for each evaluation item according to the predicted film formation result of the substrate processing apparatus, and a display control unit configured to display the performance evaluation of the recipe for each evaluation item.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and a performance evaluation method. [Background technology]

[0002] For example, digital twin technology is known that reproduces the temperature behavior of substrate processing equipment in cyberspace. Digital twin technology can reproduce changes in real (physical) space, such as the state of a substrate processing equipment during processing, in virtual (cyber) space. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-168572 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a technique for improving the ease of evaluating the performance of recipes for substrate processing apparatuses. [Means for solving the problem]

[0005] One aspect of the present disclosure is an information processing device that evaluates the performance of a recipe of a substrate processing device that performs film formation processing according to the recipe, and includes a prediction unit that predicts film formation results of the substrate processing device that performs film formation processing according to the recipe, a recipe performance evaluation unit that performs performance evaluation of the recipe for each evaluation item according to the predicted film formation results of the substrate processing device, and a display control unit that displays the performance evaluation of the recipe for each evaluation item. [Effects of the Invention]

[0006] According to the present disclosure, it is possible to provide a technique for improving the ease of evaluating the performance of a recipe for a substrate processing apparatus. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a configuration diagram of an example of a substrate processing system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of an example of a computer. [Figure 3] FIG. 2 is a functional block diagram illustrating an example of a server device according to the present embodiment. [Figure 4] FIG. 2 is a functional block diagram illustrating an example of a prediction unit according to the present embodiment. [Figure 5] FIG. 2 is a functional block diagram illustrating an example of a prediction unit according to the present embodiment. [Figure 6] FIG. 1 is an explanatory diagram illustrating an example of learning of a machine learning model according to an embodiment of the present invention. [Figure 7] 10 is a flowchart illustrating an example of a process in which the server apparatus according to the present embodiment performs a performance evaluation of a recipe through a virtual operation of the substrate processing apparatus. [Figure 8] FIG. 10 is an explanatory diagram showing an example of evaluation items for recipe performance evaluation. [Figure 9] 10 is an example screen image showing a recipe performance evaluation for each evaluation item. [Figure 10] FIG. 10 is a diagram illustrating an example of a film formation stability score. [Figure 11] FIG. 10 is a diagram illustrating an example of a film formation stability score. [Figure 12] 1 is a vertical cross-sectional view schematically showing a batch apparatus as an example of a substrate processing apparatus according to an embodiment of the present invention. [Figure 13] FIG. 2 is a cross-sectional view showing the outline of the configuration of a heat treatment furnace. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, the present embodiment will be described with reference to the drawings.

[0009] <System configuration> 1 is a configuration diagram of an example of a substrate processing system 1 according to this embodiment. The substrate processing system 1 shown in FIG. 1 includes a substrate processing apparatus 10, an apparatus controller 12, a measuring instrument 14, a server apparatus 16, and an operator terminal 18.

[0010] The substrate processing apparatus 10, the apparatus controller 12, and the measuring instrument 14 are installed in the manufacturing factory 2. The server apparatus 16 and the operator terminal 18 may be installed in the manufacturing factory 2 or may be installed outside the manufacturing factory 2. The operator terminal 18 is an information processing terminal such as a PC (Personal Computer) or a smartphone operated by an operator such as an operator in charge of the apparatus or an analyst of the substrate processing apparatus 10.

[0011] The substrate processing apparatus 10, the apparatus controller 12, the measuring device 14, the server apparatus 16, and the operator terminal 18 are communicably connected via networks N1 and N2 such as the Internet or a LAN (Local Area Network).

[0012] The substrate processing apparatus 10 is an apparatus that performs processes such as film formation, etching, or ashing, and processes substrates such as wafers W. The substrate processing apparatus 10 is, for example, a semiconductor manufacturing apparatus, a heat treatment apparatus, or a film formation apparatus.

[0013] The substrate processing apparatus 10 executes a process by receiving a control command (setting value) according to, for example, a recipe from the apparatus controller 12. The substrate processing apparatus 10 is equipped with a plurality of sensors, such as a temperature sensor for measuring temperature and a pressure sensor for measuring pressure.

[0014] The equipment controller 12 receives instructions for the substrate processing apparatus 10 from an operator. The equipment controller 12 has a man-machine interface function that provides information about the substrate processing apparatus 10 to the operator. The equipment controller 12 receives sensor data output from a plurality of sensors installed in the substrate processing apparatus 10. The equipment controller 12 may optimize the setting values ​​of the substrate processing apparatus 10, detect anomalies, or predict anomalies.

[0015] The equipment controller 12 may also store history information (process log) of processes such as film formation processes performed by the substrate processing apparatus 10. The equipment controller 12 may output the process log to the server device 16 or the operator terminal 18.

[0016] 1 is provided for each substrate processing apparatus 10, it may be provided for each of a plurality of substrate processing apparatuses 10. The equipment controller 12 may be provided inside or outside the housing of the substrate processing apparatus 10.

[0017] The measuring instrument 14 is a measuring instrument that measures process results, such as a film thickness measuring instrument, a sheet resistance measuring instrument, a particle measuring instrument, etc. The measuring instrument 14 measures the adhesion state (film thickness, etc.) of a film on a substrate such as a wafer W. Hereinafter, the film thickness included in the process results measured by the measuring instrument 14 will be referred to as the actual film thickness.

[0018] The server device 16 may receive and store information about a plurality of substrate processing apparatuses 10 in one or more manufacturing factories 2. For example, the server device 16 may store process logs and process results of a plurality of substrate processing apparatuses 10 in one or more manufacturing factories 2.

[0019] The server device 16 may have a man-machine interface function for providing information about the substrate processing apparatus 10 to an operator using a web application, etc. For example, the information about the substrate processing apparatus 10 displayed by the server device 16 using a web application, etc., includes a performance evaluation of a recipe based on a virtual operation of the substrate processing apparatus 10, as will be described later.

[0020] The operator terminal 18 may receive and store information related to the plurality of substrate processing apparatuses 10 in the one or more manufacturing factories 2. For example, the operator terminal 18 may store process logs and process results of the plurality of substrate processing apparatuses 10 in the one or more manufacturing factories 2.

[0021] The operator terminal 18 may have a man-machine interface function that provides the operator with information about the substrate processing apparatus 10 using a web application, etc. For example, the information about the substrate processing apparatus 10 that the operator terminal 18 displays using a web application, etc., includes a performance evaluation of a recipe based on a virtual operation of the substrate processing apparatus 10, which will be described later.

[0022] The equipment controller 12, server device 16, and worker terminal 18 shown in Fig. 1 are an example of an information processing apparatus according to this embodiment. It goes without saying that the substrate processing system 1 shown in Fig. 1 is just one example, and various system configurations are possible depending on the application and purpose. The division of the equipment into the equipment controller 12, server device 16, and worker terminal 18 shown in Fig. 1 is just one example. Various configurations are possible, such as a configuration in which at least two of the equipment controller 12, server device 16, and worker terminal 18 are integrated, or a configuration in which they are further separated.

[0023] <Hardware configuration> The equipment controller 12, server device 16, and operator terminal 18 shown in Fig. 1 may be realized by a computer having the hardware configuration shown in Fig. 2. Fig. 2 is a hardware configuration diagram of an example of a computer 500.

[0024] 2 includes an input device 501, an output device 502, an external I / F (interface) 503, a RAM (random access memory) 504, a ROM (read only memory) 505, a CPU (central processing unit) 506, a communication I / F 507, and an HDD (hard disk drive) 508, all of which are interconnected by a bus B. The input device 501 and the output device 502 may be connected and used when necessary.

[0025] The input device 501 is a keyboard, mouse, touch panel, etc., and is used by an operator or the like to input operation signals. The output device 502 is a display, etc., and displays the results of processing by the computer 500. The communication I / F 507 is an interface that connects the computer 500 to the networks N1 and N2 shown in Fig. 1. The HDD 508 is an example of a non-volatile storage device that stores programs and data.

[0026] The external I / F 503 is an interface with an external device. The computer 500 can read from a recording medium 503a such as an SD (Secure Digital) memory card via the external I / F 503. The external I / F 503 may also be able to write to the recording medium 503a such as an SD memory card via the external I / F 503.

[0027] The ROM 505 is an example of a non-volatile semiconductor memory (storage device) that stores programs and data. The RAM 504 is an example of a volatile semiconductor memory (storage device) that temporarily stores programs and data. The CPU 506 is an arithmetic unit that reads programs and data from a storage device such as the ROM 505 or HDD 508 onto the RAM 504 and executes processing to realize overall control and functions of the computer 500.

[0028] The equipment controller 12, the server device 16, and the operator terminal 18 of the substrate processing system 1 shown in FIG. 1 realize various functions by executing programs on the computer 500 shown in FIG.

[0029] <Functional configuration> In the following, an example will be described in which the information processing device that evaluates the performance of the recipe of the substrate processing apparatus 10 that performs film formation processing according to the recipe is the server apparatus 16. Note that the information processing device that evaluates the performance of the recipe of the substrate processing apparatus 10 that performs film formation processing according to the recipe may be the equipment controller 12 or the operator terminal 18.

[0030] The server device 16 of the substrate processing system 1 according to this embodiment is realized by, for example, functional blocks as shown in Fig. 3. Fig. 3 is a functional block diagram showing an example of the server device 16 according to this embodiment. Note that the functional block diagram of Fig. 3 omits illustration of components that are not necessary for explaining this embodiment.

[0031] The server device 16 in FIG. 3 realizes an acquisition unit 130, a data storage unit 132, a temperature control unit 134, a prediction unit 136, a recipe performance evaluation unit 138, an input receiving unit 140, and a display control unit 142 by executing a program for the server device 16.

[0032] The acquisition unit 130 acquires a recipe for the substrate processing apparatus 10. A recipe is information in which control commands (setting values) required for causing the substrate processing apparatus 10 to perform a film formation process are set. For example, the recipe sets parameters such as temperature, time, and gas flow rate. The acquisition unit 130 may receive an input of the recipe for the substrate processing apparatus 10 from an operator, may receive the recipe from an apparatus that stores the recipe for the substrate processing apparatus 10, or may receive the recipe from an apparatus in which the operator creates the recipe for the substrate processing apparatus 10.

[0033] The recipe of the substrate processing apparatus 10 acquired by the acquisition unit 130 may include, for example, the initial temperature of each component of the substrate processing apparatus 10. The components of the substrate processing apparatus 10 may include a thermal insulation tube and a boat, which will be described later. The acquisition unit 130 stores the acquired recipe of the substrate processing apparatus 10 in the data storage unit 132.

[0034] The input receiving unit 140 receives various operations from the worker. For example, the operations received from the worker include an operation to launch an app, various operations for the launched app, etc. The input receiving unit 140 notifies the temperature control unit 134, the prediction unit 136, the recipe performance evaluation unit 138, and the display control unit 142 of the contents of the various operations performed by the worker.

[0035] The temperature control unit 134 uses a control algorithm equivalent to that of the temperature control unit of the substrate processing apparatus 10. The temperature control unit 134 acquires a set temperature according to a recipe stored in the data storage unit 132. The temperature control unit 134 performs feedback control of a virtual heater power based on the acquired set temperature and the temperature of a temperature sensor predicted by a thermal model 150 of a prediction unit 136, as described below, so that the temperature of the wafer W in the processing chamber approaches the set temperature.

[0036] In this way, the server device 16 uses a temperature control unit 134 with a control algorithm equivalent to that of the actual substrate processing apparatus 10, and therefore, by linking the temperature control unit 134 with the prediction unit 136, the temperature behavior of the substrate processing apparatus 10 can be reproduced in a virtual space. By utilizing digital twin technology, the server device 16 according to this embodiment can reproduce in a virtual space the temperature behavior of the substrate processing apparatus 10 that performs film formation processing according to a recipe.

[0037] The prediction unit 136 predicts the film formation results of the substrate processing apparatus 10 that performs film formation processing according to a recipe stored in the data storage unit 132. The prediction unit 136 predicts the temperatures of the components that configure the substrate processing apparatus 10 by using a thermal model 150, which will be described later. The prediction unit 136 also predicts the film formation results of the substrate processing apparatus 10 that performs film formation processing according to a recipe by using a chemical reaction model 152 or a machine learning model 154, which will be described later. The prediction unit 136 stores the film formation results of a virtual operation of the substrate processing apparatus 10 that performs film formation processing according to a recipe in the data storage unit 132 as a predicted result.

[0038] The recipe performance evaluation unit 138 performs a performance evaluation of the recipe for each evaluation item described below, in accordance with the film formation results (predicted film thickness, etc.) of the virtual operation of the substrate processing apparatus 10, which are the prediction results from the prediction unit 136. Details of the processing by the recipe performance evaluation unit 138 will be described later. The display control unit 142 displays the performance evaluation of the recipe evaluated by the recipe performance evaluation unit 138 for each evaluation item on the output device 502. Details of the processing by the display control unit 142 will be described later.

[0039] The prediction unit 136 in Fig. 3 is realized by, for example, the functional block shown in Fig. 4. Fig. 4 is a functional block diagram showing an example of the prediction unit 136 according to this embodiment. Note that the functional block diagram in Fig. 4 omits illustration of components that are not necessary for explaining this embodiment.

[0040] The prediction unit 136 in Fig. 4 has a thermal model 150 and a chemical reaction model 152. The thermal model 150 is a thermal physics model that predicts the temperature of the substrate processing apparatus 10 that performs film formation processing according to a recipe. The thermal physics model connects the thermal relationships between components using physical equations and simulates the temperature of each component. The thermal model 150 can use a 1DCAE thermal simulation model or the like.

[0041] The thermal model 150 predicts the temperature of each component of the substrate processing apparatus 10 from the heat relationship between the components, such as the heat generation amount of the heater and the heat capacity of each component, in accordance with the virtual heater power output by the temperature control unit 134 and the initial temperature of each component of the substrate processing apparatus 10. The thermal model 150 outputs the predicted temperature of each component of the substrate processing apparatus 10 to the chemical reaction model 152.

[0042] The chemical reaction model 152 is a physical model of a chemical reaction (film formation reaction) that predicts the film formation results of the substrate processing apparatus 10 that performs film formation processing according to a recipe. The physical model of the chemical reaction calculates a film formation rate (X nm / sec, etc.) according to various parameters required for the chemical reaction, such as the temperature of each component constituting the substrate processing apparatus 10, the pressure and gas flow rate set in the recipe, etc. The prediction unit 136 can output a predicted film thickness as a prediction result by integrating the film formation rate for each period (1 sec, etc.). Note that the prediction unit 136 may also output predicted film thicknesses at multiple points on the wafer W (for example, two points at the center and edge).

[0043] In this way, the prediction unit 136 in FIG. 4 can combine the thermal model 150 and the chemical reaction model 152 to output a predicted film thickness, which is the result of the film formation process in the substrate processing apparatus 10 according to the recipe, as a prediction result.

[0044] The prediction unit 136 in Fig. 3 may be realized by the functional block shown in Fig. 5. Fig. 5 is a functional block diagram showing an example of the prediction unit 136 according to this embodiment. Note that the functional block diagram in Fig. 5 omits illustration of components that are not necessary for explaining this embodiment.

[0045] The prediction unit 136 in Fig. 5 has a thermal model 150 and a machine learning model 154. The thermal model 150 in Fig. 5 is similar to the thermal model 150 in Fig. 4, and therefore a description thereof will be omitted. The thermal model 150 outputs the predicted temperatures of the components constituting the substrate processing apparatus 10 to the machine learning model 154.

[0046] The machine learning model 154 is a virtual machine (VM) of chemical reactions that predicts the film formation results of the substrate processing apparatus 10 that performs film formation processing according to a recipe. The virtual machine of chemical reactions can output a predicted film thickness as a prediction result according to various parameters required for prediction, such as the temperature of each component constituting the substrate processing apparatus 10, the pressure and gas flow rate set in the recipe, etc. The predicted film thickness output by the machine learning model 154 may be multiple predicted film thicknesses on the wafer W.

[0047] The machine learning model 154 has already learned the relationship between the temperature and recipe of each component of the substrate processing apparatus 10 and the film formation results of the substrate processing apparatus 10, as shown in Fig. 6, for example. Fig. 6 is an explanatory diagram showing an example of learning of the machine learning model 154 according to this embodiment. The learning unit 200 in Fig. 6 trains the machine learning model 154 using an existing machine learning method.

[0048] The machine learning model 154 has already learned the relationship between the temperature and recipe of a reference substrate processing apparatus 10 (hereinafter referred to as the reference apparatus) and the film formation results of the reference apparatus using an existing machine learning method, using the process log and process results when a film formation process is performed according to a recipe in the reference apparatus.

[0049] The process log used for machine learning includes the temperature (measured temperature) measured by the temperature sensor of the reference device that performs the film formation process according to the recipe, and the pressure and gas flow rate set in the recipe. The process results used for machine learning include the film formation results of the actual operation of the reference device. The film formation results of the actual operation of the reference device include the actual device film thickness measured by the measuring instrument 14.

[0050] The parameters of the machine learning model 154 are adjusted by the adjustment unit 202 so that when the measured temperature of a reference apparatus that performs film formation processing according to a recipe and the pressure and gas flow rate set in the recipe are input, the film formation result of the reference apparatus (actual film thickness) is output.

[0051] The adjustment unit 202 adjusts the parameters of the machine learning model 154 so as to reduce the difference between the film formation results (predicted film thickness) of the reference device output by the machine learning model 154 and the film formation results (actual film thickness) of the actual operation of the reference device included in the process results.

[0052] The machine learning model 154 that has been trained using the process log and process results when a film formation process is performed according to a recipe in a reference apparatus may be used as is in another substrate processing apparatus 10 of the same model as the reference apparatus. The machine learning model 154 that has been trained may correct parameters of the machine learning model 154 using the process log and process results when a film formation process is performed according to a recipe in another substrate processing apparatus 10 of the same model, by the learning mechanism shown in FIG.

[0053] The learning unit 200 can realize the machine learning model 154 that corresponds to machine differences by correcting the parameters of the machine learning model 154 that has undergone machine learning using the process log and process results when a film formation process is performed according to a recipe in another substrate processing apparatus 10 of the same model. The process log and process results for correcting the parameters of the machine learning model 154 that has undergone machine learning may be for a small number of runs of film formation processes.

[0054] In this way, the prediction unit 136 in FIG. 5 can combine the thermal model 150 and the machine learning model 154 to output a predicted film thickness, which is the result of the film formation process in the substrate processing apparatus 10 according to the recipe, as a prediction result.

[0055] <Processing> The server device 16 performs a performance evaluation of a recipe by virtual operation of the substrate processing apparatus 10, for example, according to the processing procedure shown in Fig. 7. Fig. 7 is a flowchart of an example of a process in which the server device 16 according to the present embodiment performs a performance evaluation of a recipe by virtual operation of the substrate processing apparatus 10.

[0056] In step S10, the server device 16 receives a selection of a recipe for performance evaluation by virtual operation of the substrate processing apparatus 10 from the operator.

[0057] In step S12, the prediction unit 136 of the server device 16 predicts the temperature of each component constituting the substrate processing apparatus 10 by using the thermal model 150.

[0058] In step S14, the prediction unit 136 of the server device 16 uses the chemical reaction model 152 or the machine learning model 154 to predict the film formation result of the substrate processing apparatus 10 that performs the film formation process according to the recipe.

[0059] In the processing of steps S10 to S14, predicted results such as predicted film thickness can be output by virtual operation of the substrate processing apparatus 10 that performs film formation processing according to the recipe.

[0060] In step S16, the recipe performance evaluation unit 138 performs a recipe performance evaluation for each evaluation item shown in FIG. 8, for example, in accordance with the prediction result of the virtual operation of the substrate processing apparatus 10, which is the prediction result by the prediction unit 136.

[0061] Fig. 8 is an explanatory diagram showing an example of evaluation items for recipe performance evaluation, which includes a film thickness score, a film formation stability score, a productivity score, a particle generation score, an energy consumption score, a process gas consumption score, and a temperature control score.

[0062] 8 are merely examples. For example, the evaluation items for the recipe performance evaluation may include at least one of a film thickness score, a film formation stability score, and a productivity score. Furthermore, for example, the evaluation items for the recipe performance evaluation may include at least one of a particle generation score, an energy consumption score, a process gas consumption score, and a temperature control score in addition to at least one of a film thickness score, a film formation stability score, and a productivity score.

[0063] The film thickness score is an example of a recipe evaluation method that evaluates the difference between the predicted film thickness and the target film thickness by normalizing it with the target film thickness. For example, if the predicted film thickness predicted by the prediction unit 136 is "85 nm" and the target film thickness of the recipe is "80 nm," the recipe performance evaluation unit 138 calculates the difference between the predicted film thickness and the target film thickness using the following formula (1):

[0064] Predicted thickness "85nm" - Target thickness "80nm" = Difference "5nm"...(1) The recipe performance evaluation unit 138 normalizes the difference with the target film thickness using the following equation (2).

[0065] Difference “5nm” / Target film thickness “80nm” = 1 / 16…(2) The recipe performance evaluation unit 138 scores the normalized value using the following formula (3).

[0066] 100 x (1 - 1 / 16) = Film thickness score "93.75 points" ... (3) In this way, the recipe performance evaluation unit 138 can quantify the evaluation of the film thickness of the recipe of the substrate processing apparatus 10 using the calculated film thickness score, thereby visualizing the performance of the recipe.

[0067] The film formation stability score is an example of a recipe evaluation method for evaluating the variance of the predicted film thickness when conditions are changed. For example, the recipe performance evaluation unit 138 evaluates the variance of the predicted film thickness when the initial temperature of each component constituting the substrate processing apparatus 10 is changed. The recipe performance evaluation unit 138 may also evaluate the variance of the predicted film thickness when the gas flow rate is changed. Here, an example of evaluating the variance of the predicted film thickness when the initial temperature of each component constituting the substrate processing apparatus 10 is changed will be described.

[0068] For example, an operator sets upper and lower limit values ​​for the fluctuation range of the initial temperature of each component constituting the substrate processing apparatus 10. The recipe performance evaluation unit 138 calculates the variance of the predicted film thickness when the initial temperature of each component constituting the substrate processing apparatus 10 is varied within the range of the upper and lower limit values ​​for the fluctuation range of the initial temperature of each component constituting the substrate processing apparatus 10. Here, an example will be described in which the variance of the predicted film thickness is "0.5 nm."

[0069] The recipe performance evaluation unit 138 normalizes the variance of the predicted film thickness by the target film thickness using the following equation (4), and converts the normalized value into a score.

[0070] 100 x (1 - 0.5 / 80) = Film formation stability score "99.4 points" ... (4) In this way, the recipe performance evaluation unit 138 can visualize the performance of the recipe by quantifying the evaluation of the film formation stability (robustness) of the recipe of the substrate processing apparatus 10 using the calculated film formation stability score. Note that the evaluation of the film formation stability (robustness) of the recipe of the substrate processing apparatus 10 may be given a "score A" if the variance of the predicted film thickness is within ±0.5% of the target film thickness, or a "score B" if the variance of the predicted film thickness is within ±3% of the target film thickness.

[0071] The productivity score is an example of a recipe evaluation method for evaluating the productivity. For example, the recipe performance evaluation unit 138 evaluates the productivity of the substrate processing apparatus 10 that performs film formation processing according to the recipe from the perspective of the recipe. The evaluation of the productivity from the perspective of the recipe is higher when the time required for film formation is shorter.

[0072] For example, the recipe performance evaluation unit 138 scores the number of wafers W that can be formed per unit time, the time required for one run, or the time required for one wafer W. The evaluation of productivity from the recipe perspective may be performed for each substrate processing apparatus 10, or may be performed for each manufacturing factory 2 such as a FAB.

[0073] The particle generation score is an example of a recipe evaluation method for evaluating the degree of particle generation. For example, the prediction unit 136 predicts the degree of particle generation. The recipe performance evaluation unit 138 quantifies the probability of particle generation or the number of particles generated. For example, the prediction unit 136 may use the process log and the process results to predict the degree of particle generation using a particle generation model created from the correlation between the recipe, the cumulative film thickness, and the particle measurement results obtained by the measuring instrument 14.

[0074] For example, the recipe performance evaluation unit 138 scores the probability or number of particles generated predicted by the prediction unit 136 so that the higher the probability or number of particles generated, the lower the score. The recipe performance evaluation unit 138 may also score the number of particles generated in one run.

[0075] The energy consumption score is an example of a recipe evaluation method for evaluating the amount of power consumption. For example, the prediction unit 136 predicts the amount of power consumption of the substrate processing apparatus 10. For example, the prediction unit 136 may use the process log and the process result to predict the amount of power consumption of the substrate processing apparatus 10 that performs a film formation process according to the recipe, using a power consumption model created from the correlation between the recipe, the accumulated film thickness, and the amount of power consumption of the substrate processing apparatus 10.

[0076] For example, the recipe performance evaluation unit 138 may score the predicted power consumption amount. The recipe performance evaluation unit 138 may score the power consumption amount of one run of the substrate processing apparatus 10, for example.

[0077] The process gas consumption score is an example of a recipe evaluation method for evaluating the total amount of process gas consumed. For example, the prediction unit 136 predicts the total amount of process gas consumed by the substrate processing apparatus 10. For example, the prediction unit 136 uses the process log and the process result to predict the total amount of process gas consumed by the substrate processing apparatus 10 that performs film formation processing according to the recipe, using a process gas consumption model created from the correlation between the recipe, the cumulative film thickness, and the amount of process gas consumed by the substrate processing apparatus 10.

[0078] For example, the recipe performance evaluation unit 138 may score the predicted total consumption amount of the process gas. For example, the recipe performance evaluation unit 138 may score the total consumption amount of the process gas for one run of the substrate processing apparatus 10.

[0079] The temperature control score is an example of a recipe evaluation method for evaluating temperature control. For example, the prediction unit 136 predicts whether the temperature control of the substrate processing apparatus 10 is within a range. For example, the prediction unit 136 predicts whether the temperature control by the temperature control unit 134 of the substrate processing apparatus 10 that performs a film formation process according to a recipe is within a range.

[0080] For example, the recipe performance evaluation unit 138 may output a prediction as Safe / Out or the like as to whether the temperature control by the temperature control unit 134 of the substrate processing apparatus 10 performing the film formation process according to the recipe is within range, or may convert it into a score.

[0081] Returning to FIG. 7, in step S18, the display control unit 142 displays the performance evaluation of the recipe evaluated by the recipe performance evaluation unit 138 for each evaluation item on the output device 502, for example, as shown in FIG.

[0082] Fig. 9 is an example screen image that displays the performance evaluation of a recipe for each evaluation item. Screen 1000 in Fig. 9 shows an example of evaluation items for the performance evaluation of the recipe shown in Fig. 8. Screen 1000 in Fig. 9 displays (visualizes) the performance evaluation of a recipe for each evaluation item, making it easy for an operator to understand the performance of the recipe.

[0083] For example, the priority of the multiple evaluation items shown in Fig. 8 varies depending on the worker. A worker who wants to reduce the environmental load places importance on the energy consumption score and process gas consumption score in Fig. 8. A worker who wants to improve productivity places importance on the productivity score in Fig. 8. By displaying screen 1000 in Fig. 9, the worker can evaluate the performance of a recipe by focusing on the evaluation items that the worker wants to prioritize.

[0084] Fig. 10 is an explanatory diagram of an example of a film formation stability score. In the graph of Fig. 10, the horizontal axis represents time. The graph of Fig. 10 also shows the change in temperature of the wafer W over time and the change in predicted film thickness of the wafer W over time. The graph of Fig. 10 shows a state in which the initial temperatures of the components constituting the substrate processing apparatus 10 are varied, so there is variation in the change in temperature of the wafer W over time and the change in predicted film thickness of the wafer W over time.

[0085] 10, the server device 16 according to this embodiment can create a recipe for obtaining a desired target film thickness by calculating the average and variance of predicted film thicknesses when varying the initial temperatures of the components constituting the substrate processing apparatus 10. Furthermore, the server device 16 according to this embodiment can quantify an evaluation of the film formation stability (robustness) of the recipe of the substrate processing apparatus 10, thereby visualizing the performance of the recipe.

[0086] The server device 16 according to this embodiment may display the graph of Fig. 10 on the output device 502 for confirmation by an operator. By referring to the graph of Fig. 10, the operator can understand the influence of the apparatus state at the start of the recipe, such as the initial temperature of each component constituting the substrate processing apparatus 10, on the film formation process. Furthermore, by referring to the graph of Fig. 10, the operator can understand the time required to dissipate heat to a level that does not affect the next run. Since the operator can understand the standby time required between runs, the operator can shorten the interval between runs.

[0087] In the graph of FIG. 10, a recipe with a small difference between the target film thickness and the predicted film thickness and a small variance of the predicted film thickness is a recipe that is easy to obtain the desired film thickness and has high film formation stability.

[0088] Furthermore, the server device 16 according to this embodiment may display the predicted film thickness for each recipe of the substrate processing apparatus 10 in a graph, as shown in Fig. 11. Fig. 11 is an explanatory diagram of an example of a film formation stability score. In the graph of Fig. 11, the horizontal axis represents zone. A zone is a unit area that can be heated and controlled by a heater.

[0089] As shown in FIG. 11, the server device 16 according to this embodiment calculates the degree of variation (variance) of the film formation results of a recipe in which the initial temperatures of each component constituting the substrate processing apparatus 10 are varied, and quantifies an evaluation of the film formation stability (robustness) of the recipe of the substrate processing apparatus 10, thereby visualizing the performance of the recipe.

[0090] According to the server device 16 of this embodiment, the performance evaluation of the recipe is displayed (visualized) for each evaluation item, making it easy for the operator to understand the performance of the recipe, compare recipes with each other, and understand room for improvement of the recipe. According to the server device 16 of this embodiment, the performance evaluation of the recipe of the substrate processing apparatus 10 does not depend on the individual, and even an operator without advanced specialized knowledge can easily evaluate the performance of the recipe of the substrate processing apparatus 10.

[0091] Furthermore, the server device 16 according to this embodiment enables a cycle of operations including performance evaluation of the created recipe, visualization of the recipe performance, recipe modification according to the recipe performance evaluation, and performance evaluation of the modified recipe. Thus, this embodiment can provide a technique for improving the ease of recipe performance evaluation for the substrate processing apparatus 10.

[0092] 12 is a vertical cross-sectional view schematically showing a batch apparatus, which is an example of the substrate processing apparatus 10 according to this embodiment. The substrate processing apparatus 10 includes a vertical heat treatment furnace 60. The substrate processing apparatus 10 holds and accommodates wafers W in a boat at predetermined intervals along the vertical direction, and performs various heat treatments on the wafers W, such as oxidation, diffusion, and low-pressure CVD. The substrate processing apparatus 10 supplies a process gas into a processing chamber 65, thereby forming a film on the surface of the wafers W placed in the processing chamber 65.

[0093] 1 includes a mounting table (load port) 20, a housing 30, and an apparatus controller 100. The housing 30 includes a loading area (working area) 40 and a heat treatment furnace 60.

[0094] The loading area 40 is provided at the bottom inside the housing 30. The heat treatment furnace 60 is provided above the loading area 40 inside the housing 30. A base plate 31 is provided between the loading area 40 and the heat treatment furnace 60.

[0095] The loading stage (load port) 20 is used to load and unload wafers W into and from the housing 30. Storage containers 21 and 22 are placed on the loading stage (load port) 20. The storage containers 21 and 22 are airtight storage containers (FOUPs) that have a detachable lid (not shown) on the front and can store a plurality of wafers W (for example, about 25 wafers W) at predetermined intervals.

[0096] Further, below the mounting table 20, an alignment device (aligner) 23 may be provided to align a cutout portion (for example, a notch) provided on the outer periphery of the wafer W transferred by the transfer mechanism 47 in one direction.

[0097] The loading area (work area) 40 is used to transfer wafers W between the storage containers 21 and 22 and the boat 44, to load the boat 44 into the processing container 65, and to unload the boat 44 from the processing container 65. The loading area 40 is provided with a door mechanism 41, a shutter mechanism 42, a lid 43, the boat 44, a base 45a, a base 45b, a lifting mechanism 46 shown in FIG. 13, and a transfer mechanism 47.

[0098] The door mechanism 41 is used to remove the lids of the storage containers 21 and 22 and open the interiors of the storage containers 21 and 22 to the loading area 40. The shutter mechanism 42 is provided above the loading area 40. The shutter mechanism 42 is provided to cover (or block) the furnace port 68a in order to suppress or prevent high-temperature heat from inside the furnace from being released into the loading area 40 through the furnace port 68a when the lid 43 is open.

[0099] The lid 43 has a heat-retaining cylinder 48 and a rotation mechanism 49. The heat-retaining cylinder 48 is provided on the lid 43. The heat-retaining cylinder 48 prevents the boat 44 from being cooled by heat transfer to the lid 43 side and serves to keep the boat 44 warm. The rotation mechanism 49 is attached to the lower part of the lid 43. The rotation mechanism 49 serves to rotate the boat 44. The rotation shaft of the rotation mechanism 49 passes through the lid 43 airtightly and is provided to rotate a turntable arranged on the lid 43.

[0100] The lifting mechanism 46 drives the lid body 43 to move up and down when the boat 44 is loaded into and unloaded from the loading area 40 into the processing vessel 65. When the lid body 43 raised by the lifting mechanism 46 is loaded into the processing vessel 65, the lid body 43 abuts against the furnace opening 68a to seal the furnace opening 68a.

[0101] The boat 44 placed on the lid 43 can rotatably hold the wafer W in a horizontal plane within the processing vessel 65. The substrate processing apparatus 10 may have a plurality of boats 44. The loading area 40 is provided with boats 44a and 44b.

[0102] A base 45a, a base 45b, and a boat transfer mechanism are provided in the loading area 40. The bases 45a and 45b are mounting tables onto which the boats 44a and 44b, respectively, are transferred from the lid 43. The boat transfer mechanism is used to transfer the boat 44a or 44b from the lid 43 to the base 45a or 45b.

[0103] The boats 44a and 44b are made of, for example, quartz, and are configured to mount large-diameter wafers W, for example, 300 mm in diameter, in a horizontal position at a predetermined interval (pitch width) in the vertical direction. The boats 44a and 44b are provided with a plurality of support columns (for example, three) between the top plate and the bottom plate. The support columns are provided with claws for holding the wafers W. The boats 44a and 44b may also be provided with auxiliary columns as appropriate in addition to the support columns.

[0104] The transfer mechanism 47 is used to transfer wafers W between the storage container 21 or 22 and the boat 44a or 44b. The transfer mechanism 47 has a base 57, a lifting arm 58, and a plurality of forks (transfer plates) 59. The base 57 is provided so as to be movable up and down and rotatable. The lifting arm 58 is provided so as to be movable up and down (liftable) by a ball screw or the like. The base 57 is provided so as to be rotatable horizontally on the lifting arm 58.

[0105] Fig. 13 is a cross-sectional view showing the outline of the configuration of a heat treatment furnace. The heat treatment furnace 60 in Fig. 13 is an example of a vertical furnace for accommodating a plurality of thin, disk-shaped wafers W and subjecting them to a predetermined heat treatment. The heat treatment furnace 60 includes a jacket 62, a heater 63, a space 64, and a treatment container 65.

[0106] The processing vessel 65 is used to store and heat-treat the wafers W held in the boat 44. The processing vessel 65 is made of, for example, quartz and has a vertically elongated shape. The processing vessel 65 is supported on a base plate 66 via a manifold 68 at the bottom. Gas is supplied from the manifold 68 to the processing vessel 65 through an injector 71. The injector 71 supplies gas into the processing vessel 65 from a blowing portion (hole). The injector 71 is connected to a gas supply source 72. The gas supplied to the processing vessel 65 is exhausted through an exhaust port 73 from an exhaust system 74 equipped with a vacuum pump capable of reducing pressure.

[0107] The lid 43 closes the furnace port 68a at the bottom of the manifold 68 when the boat 44 is loaded into the processing vessel 65. The lid 43 is provided so as to be movable up and down by an elevating mechanism 46. A heat-retaining cylinder 48 is placed on the top of the lid 43. The boat 44, which carries a large number of wafers W at predetermined intervals in the vertical direction, is provided on the top of the heat-retaining cylinder 48.

[0108] Jacket 62 is provided to cover the periphery of processing vessel 65 and defines a space 64 around processing vessel 65. Jacket 62 has a cylindrical shape similar to processing vessel 65. Jacket 62 is supported by a base plate 66. A heat insulating material 62a made of, for example, glass wool may be provided inside jacket 62 and outside space 64.

[0109] The heater 63 is provided to cover the periphery of the processing vessel 65. For example, the heater 63 is provided inside the jacket 62 and outside the space 64. The heater 63 heats the processing vessel 65 and also heats the wafers W held in the boat 44, i.e., the wafers W in the processing vessel 65. The heater 63 functions as a heating unit that heats the wafers W.

[0110] The heater 63 includes a heating resistor such as a carbon wire, and can control the temperature of the gas flowing inside the space 64 and heat the inside of the processing vessel 65 to a predetermined temperature (for example, 50 to 1200° C.).

[0111] The space 64 and the space within the processing vessel 65 are divided vertically into a plurality of unit areas, for example, 10 unit areas A1, A2, A3, A4, A5, A6, A7, A8, A9, and A10. The heater 63 is divided vertically into 63-1, 63-2, 63-3, 63-4, 63-5, 63-6, 63-7, 63-8, 63-9, and 63-10 corresponding to one of the unit areas. Each of the heaters 63-1 to 63-10 is configured to independently control heating for each of the unit areas A1 to A10 by the output (heater power) of a heater output unit 86 including, for example, a thyristor. The heaters 63-1 to 63-10 are examples of heating elements.

[0112] Measurement signals from temperature sensors Ao1 to Ao10 are input to equipment controller 100 via line 81. Measurement signals from temperature sensors Ai1 to Ai10 are input to equipment controller 100 via line 82. Upon receiving the measurement signals, equipment controller 100 controls the heater power output by heater output unit 86 based on the set temperature. Heater output unit 86 supplies heater power to each of heaters 63-1 to 63-10 via heater output line 87 and heater terminal 88.

[0113] The heat treatment furnace 60 may also include a cooling mechanism 90 for cooling the treatment vessel 65. The cooling mechanism 90 includes, for example, a blower 91, an air duct 92, and an exhaust duct 94.

[0114] Although the preferred examples of this embodiment have been described in detail above, this embodiment is not limited to the above-described examples, and various modifications and substitutions can be made to the above-described examples without departing from the scope of this embodiment.

[0115] 1, there are various system configuration examples according to the application and purpose. The substrate processing system 10 of the present disclosure can be applied to any of a single-wafer processing apparatus that processes substrates one by one, a batch processing apparatus that processes multiple substrates at once, and a semi-batch processing apparatus. [Explanation of symbols]

[0116] 1. Substrate Processing System 10. Substrate processing equipment 12 Equipment Controller 16 Server equipment 18 Worker terminal 130 Acquisition Department 132 Data storage unit 134 Temperature control unit 136 Prediction Department 138 Recipe Performance Evaluation Section 140 Input reception section 142 Display control unit 150 Thermal Model 152 Chemical Reaction Model 154 Machine Learning Models

Claims

1. 1. An information processing apparatus that evaluates the performance of a recipe of a substrate processing apparatus that performs a film formation process according to the recipe, a prediction unit that predicts a film formation result of the substrate processing apparatus that performs a film formation process according to the recipe; a recipe performance evaluation unit that performs a performance evaluation of the recipe for each evaluation item according to the predicted film formation result of the substrate processing apparatus; a display control unit that displays a performance evaluation of the recipe for each evaluation item; An information processing device having the above.

2. the prediction unit includes a thermal physics model for predicting a temperature of the substrate processing apparatus that performs the film formation process according to the recipe, and a chemical reaction physics model for predicting a film formation result of the substrate processing apparatus that performs the film formation process according to the recipe; and the physical model of chemical reactions predicts a film formation result of the substrate processing apparatus according to the temperature of the substrate processing apparatus predicted by the physical model of heat and the recipe; 2. The information processing device according to claim 1.

3. the prediction unit includes a thermal physics model that predicts a temperature of the substrate processing apparatus that performs the film formation process according to the recipe, and a machine learning model of a chemical reaction that predicts a film formation result of the substrate processing apparatus that performs the film formation process according to the recipe; and the machine learning model of the chemical reaction has learned the relationship between the temperature and the recipe of the substrate processing apparatus and the film formation result of the substrate processing apparatus, and predicts the film formation result of the substrate processing apparatus according to the temperature and the recipe of the substrate processing apparatus predicted by the thermal physical model; 2. The information processing device according to claim 1.

4. the recipe performance evaluation unit evaluates at least one of the evaluation items, including film thickness, film formation stability, and productivity, based on the film formation result of the substrate processing apparatus predicted by the prediction unit. The information processing device according to claim 1 .

5. the recipe performance evaluation unit further evaluates at least one of particle generation, energy consumption, process gas consumption, and temperature control as the evaluation items based on the film formation result of the substrate processing apparatus predicted by the prediction unit.

5. The information processing device according to claim 4.

6. the recipe performance evaluation unit performs the film thickness performance evaluation using an evaluation method in which a difference between a predicted film thickness predicted by the prediction unit and a target film thickness of the recipe is normalized by the target film thickness.

5. The information processing device according to claim 4.

7. the recipe performance evaluation unit performs the performance evaluation of the film formation stability by an evaluation method of evaluating the variance of the predicted film thickness predicted by the prediction unit when conditions are changed.

5. The information processing device according to claim 4.

8. A performance evaluation method in which an information processing apparatus evaluates the performance of a recipe of a substrate processing apparatus that performs a film formation process according to the recipe, comprising: predicting a film formation result of the substrate processing apparatus performing a film formation process according to the recipe; performing a performance evaluation of the recipe for each evaluation item according to the predicted film formation results of the substrate processing apparatus; Displaying a performance evaluation of the recipe for each evaluation item; A performance evaluation method having the following.

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