Information processing device, information processing method, and program

Abstract

To reduce the amount of computation required for analyzing a process.SOLUTION: There is provided an information processing device including: an acquisition unit for acquiring sensor data indicating sensor values measured by at least one sensor provided in a substrate processing apparatus for executing a process including at least one step; a statistical processing unit configured to calculate statistical values of sensor values for each step on the basis of the sensor data; and an analysis unit configured to analyze the process on the basis of processed data including the statistical values for each step.SELECTED DRAWING: Figure 4

Description

[Technical Field]

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

[0002] There are known techniques for analyzing sensor data output from a substrate processing apparatus. For example, Patent Document 1 discloses an information processing apparatus that analyzes the similarity of multiple pieces of time-series data output from a substrate processing apparatus that is executing processing according to the same sequence using a dynamic time warping method, and outputs an analysis result according to the difference between the time-series data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2024-002710 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides techniques for reducing the amount of calculation required to analyze a process. [Means for solving the problem]

[0005] According to one aspect of the present disclosure, there is provided an information processing apparatus including: an acquisition unit that acquires sensor data indicating sensor values ​​measured by one or more sensors provided in a substrate processing apparatus that executes a process including one or more steps; a statistical processing unit that calculates statistical values ​​of the sensor values ​​for each step based on the sensor data; and an analysis unit that analyzes the process based on processed data including the statistical values ​​for each step. [Effects of the Invention]

[0006] In one aspect, the amount of calculation required to analyze a process can be reduced. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a block diagram showing an example of an overall configuration of a substrate processing system; [Figure 2] 1 is a schematic cross-sectional view showing an example of a substrate processing apparatus. [Figure 3] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer. [Figure 4] FIG. 2 is a block diagram illustrating an example of a functional configuration of an analysis device. [Figure 5] FIG. 10 is a diagram illustrating an example of processed data. [Figure 6] 10 is a flowchart illustrating an example of an analysis method. [Figure 7] FIG. 10 is a diagram illustrating an example of a segment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the drawings, the same components are denoted by the same reference numerals, and redundant explanations may be omitted.

[0009] [Embodiment] One embodiment of the present disclosure is a substrate processing system including a substrate processing apparatus that processes a substrate, which is an example of a process target object. In this embodiment, the substrate processing apparatus heat-treats a semiconductor wafer, which is an example of a substrate, in a processing chamber. The substrate processing system also includes an analyzer that analyzes sensor data indicating a sensor value measured by a sensor provided in the substrate processing apparatus.

[0010] A substrate processing apparatus is provided with one or more sensors that measure the state of the substrate processing apparatus. When the substrate processing apparatus executes a process for processing a substrate, the sensors provided in the substrate processing apparatus measure predetermined sensor values ​​at predetermined time intervals. Time series data of the sensor values ​​measured by each sensor is stored in a storage device provided in the substrate processing apparatus or in a storage device connected to the substrate processing apparatus via a network. The time series data of the sensor values ​​is also called "trace data." Trace data is an example of sensor data.

[0011] Trace data is used to analyze processes executed by substrate processing apparatuses. Examples of types of analysis include anomaly detection, variation factor analysis, and machine difference analysis. Anomaly detection is a process for detecting anomalies occurring in a substrate processing apparatus. Variation factor analysis is a process for analyzing the factors when process results vary from process to process in a single substrate processing apparatus. Machine difference analysis is a process for analyzing differences in equipment status when the same process is executed on multiple substrate processing apparatuses.

[0012] When performing analytical processing, raw trace data must be preprocessed in various ways to convert it into an analyzable state. However, the type and order of preprocessing required differ for each analytical processing. Therefore, the preprocessing must be adjusted each time an analytical processing is performed. Furthermore, preprocessing performed for one analytical processing cannot be reused for other analytical processing. As a result, the frequently recorded trace data must be permanently stored, resulting in an increase in storage capacity. Furthermore, preprocessing of the trace data each time analytical processing is performed requires a significant amount of calculation. Furthermore, because the trace data generated by substrate processing equipment exhibits process-specific characteristics, commonly used data preprocessing methods cannot guarantee analytical accuracy.

[0013] The present embodiment aims to reduce the amount of calculation required for analyzing a process, and to achieve this, in the present embodiment, statistical values ​​of sensor values ​​for each step are calculated based on sensor data indicating sensor values ​​measured by sensors provided in a substrate processing apparatus, and the process is analyzed based on processed data including the statistical values ​​for each step.

[0014] In one aspect, this embodiment allows the processed data to be reused in various analytical processes, thereby reducing the amount of calculation required for process analysis. In another aspect, this embodiment determines parameters to be used in preprocessing based on domain knowledge, thereby improving the accuracy of process analysis.

[0015] <System configuration> The overall configuration of a substrate processing system according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the overall configuration of a substrate processing system.

[0016] 1, the substrate processing system 100 includes substrate processing apparatuses 120a1 to 120a3 and controllers 121a1 to 121a3 in a factory a. The substrate processing apparatuses 120a1 to 120a3 and the controllers 121a1 to 121a3 are connected to each other by wire or wirelessly.

[0017] The substrate processing system 100 also includes substrate processing apparatuses 120b1 and 120b2 and controllers 121b1 and 121b2 in a factory b. The substrate processing apparatuses 120b1 and 120b2 and the controllers 121b1 and 121b2 are connected to each other by wire or wirelessly.

[0018] The substrate processing system 100 also includes substrate processing apparatuses 120c1 and 120c2 and controllers 121c1 and 121c2 in a factory c. The substrate processing apparatuses 120c1 and 120c2 and the controllers 121c1 and 121c2 are connected to each other by wire or wirelessly.

[0019] Substrate processing apparatuses 120a1-120a3, substrate processing apparatuses 120b1-120b2, and substrate processing apparatuses 120c1-120c2 are connected to host apparatuses 110a, 110b, and 110c, respectively, via networks N1-N3. Each substrate processing apparatus performs substrate processing under the control of each control device based on instructions from host apparatuses 110a, 110b, and 110c. Host apparatuses 110a, 110b, and 110c are connected to server apparatus 150 via network N4, such as the Internet.

[0020] In the following description, the substrate processing apparatuses 120a1 to 120a3, 120b1, 120b2, 120c1, and 120c2 are also collectively referred to as substrate processing apparatus 120. The control apparatuses 121a1 to 121a3, 121b1, 121b2, 121c1, and 121c2 are also collectively referred to as control apparatus 121. The host apparatuses 110a, 110b, and 110c are also collectively referred to as host apparatus 110.

[0021] It is assumed that the substrate processing apparatuses 120a1 to 120a3, the substrate processing apparatuses 120b1 and 120b2, and the substrate processing apparatuses 120c1 and 120c2 store various data that they manage within themselves.

[0022] The analysis device 140 is connected to the substrate processing apparatuses 120 including the substrate processing apparatus 120a1, and thereby continuously acquires accumulated data accumulated in each of the substrate processing apparatuses 120. The example in Fig. 2 shows the analysis device 140 connected to the substrate processing apparatus 120a1, but this is not limiting. In the present embodiment, the case where the analysis device 140 is connected to the substrate processing apparatus 120a1 will be described below in detail.

[0023] The substrate processing system 100 shown in Fig. 1 is one example, and it goes without saying that there are various system configuration examples depending on the application and purpose. The classification of devices such as the host device 110, substrate processing device 120, control device 121, analysis device 140, and server device 150 shown in Fig. 1 is one example. For example, the number of factories, the number of host devices 110, the number of substrate processing device 120, the number of control devices 121, the number of analysis devices 140, etc. are one example and are not limited to these.

[0024] For example, the substrate processing system 100 can have various configurations, such as a configuration in which at least two of the host device 110, substrate processing apparatus 120, control device 121, analysis device 140, and server device 150 are integrated together, or a configuration in which they are further divided. For example, the control device 121 may be configured to collectively control multiple substrate processing apparatuses 120, or may be provided in a one-to-one correspondence with the substrate processing apparatus 120, or may be integrated with the substrate processing apparatus 120.

[0025] Analysis device 140 may be realized by host device 110 or by server device 150. In this case, analysis device 140 is not necessary. Analysis device 140 may also be realized by control device 121. Analysis device 140 may also be realized by a control device that collectively controls multiple control devices 121.

[0026] <Substrate processing equipment> An example of a substrate processing apparatus according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a schematic cross-sectional view showing a vertical heat treatment apparatus, which is an example of a substrate processing apparatus.

[0027] The vertical heat treatment apparatus 120 in this embodiment is a substrate treatment apparatus that accommodates a large number of semiconductor wafers W, which are an example of objects to be treated, at one time and performs heat treatment such as oxidation, diffusion, low-pressure CVD (Chemical Vapor Deposition), etc. As shown in Fig. 2, the vertical heat treatment apparatus 120 includes a treatment vessel 10, a gas supply unit 20, an exhaust unit 30, a heating unit 40, a cooling unit 50, a control device 121, etc.

[0028] The processing vessel 10 has a substantially cylindrical shape. The processing vessel 10 includes an inner tube 11, an outer tube 12, a manifold 13, an injector 14, a gas outlet 15, a lid 16, etc. The inner tube 11 has a substantially cylindrical shape. The outer tube 12 has a substantially cylindrical shape with a ceiling, and the inner tube 11 and the outer tube 12 form a double-tube structure. The inner tube 11 and the outer tube 12 are made of a heat-resistant material such as quartz.

[0029] The manifold 13 has a substantially cylindrical shape. The manifold 13 supports the lower ends of the inner tube 11 and the outer tube 12. The manifold 13 is made of, for example, stainless steel. The injector 14 passes through the manifold 13 and extends horizontally into the inner tube 11, and then bends in an L shape inside the inner tube 11 and extends upward. The base end of the injector 14 is connected to the gas introduction pipe 24, and the tip is open. The injector 14 discharges the process gas (hereinafter simply referred to as "gas") introduced via the gas introduction pipe 24 into the inner tube 11 from the opening at the tip. There may be multiple injectors 14.

[0030] The gas outlet 15 is formed in the manifold 13. The processing gas is exhausted by the exhaust unit 30 through the gas outlet 15. The lid 16 airtightly closes the opening at the lower end of the manifold 13. The lid 16 is made of, for example, stainless steel. A wafer boat (substrate holder) 18 is placed on the lid 16 via a heat-insulating tube 17. The heat-insulating tube 17 and the wafer boat 18 are made of, for example, a heat-resistant material such as quartz.

[0031] The wafer boat 18 holds a plurality of semiconductor wafers W in a substantially horizontal position at predetermined intervals in the vertical direction. The wafer boat 18 is loaded into the processing vessel 10 by the lifting mechanism 19 raising the lid 16, and is accommodated in the processing vessel 10. The wafer boat 18 is unloaded from the processing vessel 10 by the lifting mechanism 19 lowering the lid 16.

[0032] The gas supply unit 20 includes a gas source 21, an IGS (Integrated Gas System) 22, an external pipe 23, and a gas introduction pipe 24. The gas source 21 is a supply source of processing gas and includes, for example, a film formation gas source, a cleaning gas source, and a purge gas source. The IGS 22 is an integrated circuit of gas pipes, and integrates a group of pipes connected to the film formation gas source, cleaning gas source, purge gas source, etc. of the gas source 21. A flow rate control unit is installed within the IGS 22 and controls the flow rate of gas flowing through each pipe. The flow rate control unit includes, for example, a mass flow controller and an opening / closing valve.

[0033] The IGS 22 is connected to an external pipe 23. The external pipe 23 is connected to a gas introduction pipe 24. A heater (not shown) is wrapped around the outer periphery of the external pipe 23 to heat the external pipe 23. The gas introduction pipe 24 is connected to the processing vessel 10 and introduces gas into the processing vessel 10. That is, the flow rate of the processing gas from the gas source 21 is controlled by a flow rate control unit in the IGS 22, and the processing gas is heated as it flows through the external pipe 23 and flows into the gas introduction pipe 24, and is supplied from the gas introduction pipe 24 into the processing vessel 10 via the injector 14. The injector 14 functions as a gas inlet for the processing vessel 10.

[0034] A gas piping joint 82 connected to the gas introduction pipe 24 is provided near the gas inlet of the processing chamber 10. A temperature sensor 80 is configured to pass through the joint 82. The temperature sensor 80 is configured to measure the temperature of the gas in the gas introduction pipe 24. The temperature sensor 80 transmits the measured temperature to the control device 121. A second heater 81 is also provided in the gas introduction pipe 24, and the second heater 81 is configured to heat the gas in the gas introduction pipe 24.

[0035] The exhaust unit 30 includes an exhaust device 31, an exhaust pipe 32, and a pressure controller 33. The exhaust device 31 is, for example, a vacuum pump such as a dry pump or a turbo molecular pump. The pressure controller 33 is installed in the exhaust pipe 32 and controls the pressure inside the processing vessel 10 by adjusting the conductance of the exhaust pipe 32. The pressure controller 33 is, for example, an automatic pressure control valve.

[0036] The heating unit 40 includes a thermal insulator 41, a first heater 42, and an outer skin 43. The thermal insulator 41 has a substantially cylindrical shape and is disposed around the outer tube 12. The thermal insulator 41 is primarily composed of silica and alumina. The first heater 42 has a linear shape and is disposed in a spiral or serpentine shape around the inner periphery of the thermal insulator 41. The first heater 42 is configured to enable temperature control by dividing the processing vessel 10 into multiple zones in the height direction. The outer skin 43 is disposed to cover the outer periphery of the thermal insulator 41. The outer skin 43 maintains the shape of the thermal insulator 41 and reinforces it. The outer skin 43 is formed of a metal such as stainless steel. A water-cooled jacket may be disposed around the outer skin 43 to suppress thermal influence of the heating unit 40 on the outside. The heating unit 40 heats the inside of the processing vessel 10 by heat generated by the first heater 42.

[0037] The cooling unit 50 supplies a cooling fluid toward the processing vessel 10 to cool the semiconductor wafer W in the processing vessel 10. The cooling fluid may be, for example, air. The cooling unit 50 supplies the cooling fluid toward the processing vessel 10 when, for example, rapidly lowering the temperature of the semiconductor wafer W after heat treatment. The cooling unit 50 has a fluid flow path 51, an outlet 52, a distribution flow path 53, a flow rate adjustment unit 54, and a heat exhaust port 55.

[0038] A plurality of fluid flow paths 51 are formed in the height direction between the insulating material 41 and the outer skin 43. The fluid flow paths 51 are, for example, flow paths formed around the outside of the insulating material 41. Blowing holes 52 are formed from each fluid flow path 51, penetrating the insulating material 41, and blow out the cooling fluid into the space between the outer pipe 12 and the insulating material 41. The distribution flow paths 53 are provided outside the outer skin 43, and distribute and supply the cooling fluid to each fluid flow path 51. The flow rate adjustment unit 54 is interposed in the distribution flow paths 53, and adjusts the flow rate of the cooling fluid supplied to the fluid flow paths 51.

[0039] The heat exhaust port 55 is provided above the plurality of blow-out holes 52, and exhausts the cooling fluid supplied to the space between the outer pipe 12 and the heat insulating material 41 to the outside of the processing vessel 10. The cooling fluid exhausted to the outside of the processing vessel 10 is cooled, for example, by a heat exchanger and supplied again to the distribution flow path 53. However, the cooling fluid exhausted to the outside of the processing vessel 10 may be exhausted without being reused.

[0040] The temperature sensor 60 detects the temperature inside the processing vessel 10. The temperature sensor 60 is provided, for example, in the inner pipe 11. However, the temperature sensor 60 may be provided at any position where it can detect the temperature inside the processing vessel 10, for example, in the space between the inner pipe 11 and the outer pipe 12. The temperature sensor 60 has, for example, multiple temperature measuring units provided at different positions in the height direction corresponding to multiple zones. The multiple temperature measuring units may be, for example, thermocouples or resistance temperature detectors. The temperature sensor 60 transmits the temperatures detected by the multiple temperature measuring units to the control device 121.

[0041] The control device 121 controls the operation of the vertical heat treatment device 120, thereby controlling the semiconductor process performed in the vertical heat treatment device 120. The control device 121 may be, for example, a computer.

[0042] <Computer> The host device 110, the control device 121, the analysis device 140, and the server device 150 included in the substrate processing system 100 shown in Fig. 1 are realized by a computer having a hardware configuration such as that shown in Fig. 3. Fig. 3 is a block diagram showing an example of the hardware configuration of the computer.

[0043] 3, the computer 500 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.

[0044] The input device 501 is a keyboard, mouse, touch panel, etc., and is used by an operator or the like to input various 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 a network. The HDD 508 is an example of a non-volatile storage device that stores programs and data.

[0045] The external I / F 503 is an interface with an external device. The computer 500 can read and / or write data from and to a recording medium 503a such as an SD (Secure Digital) memory card via the external I / F 503. 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.

[0046] The CPU 506 is a computing device that controls the entire computer 500 and realizes its functions by reading programs and data from storage devices such as the ROM 505 and HDD 508 onto the RAM 504 and executing the processes.

[0047] <Functional configuration> The functional configuration of the analysis device in this embodiment will be described with reference to Fig. 4. Fig. 4 is a block diagram showing an example of the functional configuration of the analysis device.

[0048] 4, analysis device 140 includes acquisition unit 210, statistical processing unit 220, exclusion unit 230, data storage unit 240, normalization unit 250, grouping unit 260, and analysis unit 270. Analysis device 140 functions as acquisition unit 210, statistical processing unit 220, exclusion unit 230, data storage unit 240, normalization unit 250, grouping unit 260, and analysis unit 270 by executing a pre-installed analysis program.

[0049] The acquisition unit 210, statistical processing unit 220, exclusion unit 230, normalization unit 250, grouping unit 260, and analysis unit 270 are realized, for example, by the CPU 506 shown in Fig. 3 executing a program loaded onto the RAM 504. The data storage unit 240 is realized, for example, by the RAM 504 or HDD 508 shown in Fig. 3.

[0050] The acquiring unit 210 acquires the trace data generated by the substrate processing apparatus 120. The trace data is time-series data indicating sensor values ​​measured by one or more sensors provided in the substrate processing apparatus 120. The trace data may include multiple time-series data indicating sensor values ​​measured by each of the multiple sensors.

[0051] The trace data may include time-series data of sensor values ​​measured when the substrate processing apparatus 120 executes a process for processing a target object. The process may include at least one or more steps. The trace data may include time-series data of sensor values ​​measured when the substrate processing apparatus 120 executes a process multiple times. For example, the trace data may be one piece of time-series data recorded throughout multiple processes, or multiple pieces of time-series data recorded each time the process is executed.

[0052] The statistical processing unit 220 calculates statistical values ​​of the sensor values ​​for each step included in the process based on the trace data acquired by the acquisition unit 210. The statistical processing unit 220 generates processed data including the statistical values ​​of the sensor values. The statistical processing unit 220 may calculate the processing time for each step along with the statistical values ​​for each step and include the calculated time in the processed data.

[0053] The statistical processing unit 220 may exclude sensor values ​​relating to steps with short processing times from the trace data. For example, the statistical processing unit 220 may calculate the processing time of each step included in the trace data and exclude sensor values ​​relating to steps with processing times less than a predetermined threshold from the trace data.

[0054] The threshold value for the processing time may be specified by a user of the analysis device 140. The threshold value may be stored in a storage unit such as the HDD 508 of the analysis device 140. The threshold value may be determined based on domain knowledge. For example, the threshold value may be determined by an engineer who has knowledge about the behavior of the substrate processing apparatus 120. As an example, the threshold value may be about 0.1 seconds or 1 second.

[0055] The statistical processing unit 220 may calculate a plurality of predetermined statistical values. The type of statistical value may be determined based on domain knowledge. The type of statistical value may include, for example, at least one of a maximum value, a minimum value, an average value, a standard deviation (e.g., 3σ), a mode, or a slope (the gradient of a regression line). In this embodiment, as an example, a configuration will be described in which the statistical processing unit 220 calculates a maximum value, a minimum value, an average value, and a standard deviation.

[0056] The statistical processing unit 220 may calculate statistical values ​​for each step included in the trace data, regardless of the type of recipe or step included in the process. The type of recipe or step may include, for example, a main recipe, a sub-recipe, a main step, or a sub-step.

[0057] The exclusion unit 230 excludes statistical values ​​based on tracing data acquired by the acquisition unit 210, the fluctuation range of which does not satisfy a predetermined criterion, from the processed data generated by the statistical processing unit 220. For example, the exclusion unit 230 may exclude statistical values ​​calculated based on tracing data that does not satisfy a predetermined constant value criterion from the processed data. The exclusion unit 230 may exclude statistical values ​​calculated based on tracing data that does not satisfy a predetermined minute fluctuation criterion from the processed data.

[0058] The constant value criterion may indicate that the fluctuation range of the sensor value included in the trace data is equal to or greater than a predetermined threshold. The fluctuation range of the sensor value is the difference between the maximum and minimum sensor values ​​included in the trace data. The threshold of the constant value criterion may be an extremely small value, for example, approximately 10 to the power of -6.

[0059] The minute fluctuation criterion may indicate that the fluctuation range of the sensor value included in the trace data is equal to or greater than a predetermined threshold. The threshold of the minute fluctuation criterion may be a value obtained by multiplying the resolution of the sensor by a predetermined threshold adjustment coefficient. The resolution of the sensor is the minimum value that can be measured by the sensor. The threshold adjustment coefficient may be determined based on domain knowledge. The threshold adjustment coefficient may be determined according to the model of the substrate processing apparatus 120. In other words, the same threshold adjustment coefficient may be used for substrate processing apparatuses 120 of the same model.

[0060] The processed data is stored in the data storage unit 240. The processed data includes statistical values ​​for each step calculated by the statistical processing unit 220. If the stress data includes sensor values ​​measured by multiple sensors, the processed data may include statistical values ​​for each combination of sensor and step.

[0061] The data storage unit 240 may accumulate processed data on a process-by-process basis. In other words, the data storage unit 240 may store processed data on a process-by-process basis in chronological order. When processed data is generated by processing trace data related to a newly executed process, the data storage unit 240 may add the new processed data to the end of the stored processed data and store it.

[0062] Fig. 5 is a diagram showing an example of processed data. As shown in Fig. 5, the processed data has data items indicating four statistics for each combination of sensor and step. Note that in Fig. 5, Mean is the average value, Max is the maximum value, Min is the minimum value, and std is the standard deviation.

[0063] Fig. 5 shows an example of processed data when M sensors are provided in the substrate processing apparatus 120 and a process including K steps is executed. Fig. 5 also shows an example of processed data after the substrate processing apparatus 120 executes the process N times. Note that a "Run" is a unit of process execution, and means that the entire process specified in the process recipe is executed once.

[0064] The normalization unit 250 normalizes the processed data read from the data storage unit 240. The normalization unit 250 may normalize the processed data using a method according to the analysis processing specified by the user.

[0065] When the analysis process specified by the user is machine-difference analysis, the normalization unit 250 normalizes the processed data using a normalization method specialized for machine-difference analysis. The normalization method may be a method based on inter-process reproducibility. Specifically, the normalization method divides the processed data by a normalization coefficient obtained by converting the weighted average of the variance of the processed data into a standard deviation.

[0066] We will now explain in more detail the normalization method specialized for machine difference analysis. In this normalization method, different methods are used for normalization depending on the number of executions of processes included in the trace data.

[0067] If the number of process executions included in the trace data is less than the maximum of 2, divide each sensor value by the -nth power of 10, where n is the minimum number of decimal points for each sensor value.

[0068] If the number of process executions included in the trace data is two or more, first calculate the variance of each sensor value in the trace data where the number of process executions is two or more. Next, calculate the weighted average of the variances by the number of process executions. Specifically, calculate Equation (1).

[0069]

number

[0070] However, σ k 2 is the unbiased variance of the k-th trace data, and N k is the number of data included in the k-th tracing data, n is the number of tracing data, and N is the total number of data included in the tracing data.

[0071] The weighted average of the variances calculated by Eq. (1) σ 2 is converted into a standard deviation, and the standard deviation is set as the normalization coefficient σ. Then, each sensor value included in the trace data is divided by the normalization coefficient σ.

[0072] If the analysis process specified by the user is an analysis process other than machine-difference analysis, the normalization unit 250 normalizes the processed data. Standardization is a process of converting data so that the mean is 0 and the variance is 1. The normalization method for processes other than machine-difference analysis is not limited to standardization, and any known normalization method used in data analysis may be used.

[0073] The grouping unit 260 groups a plurality of sensors provided in the substrate processing apparatus 120. In other words, the grouping unit 260 classifies a plurality of sensors into a plurality of groups. The normalization unit 250 may group a plurality of sensors using a method according to an analysis process designated by a user. The grouping unit 260 is not an essential component, and does not have to group the sensors.

[0074] For example, the grouping unit 260 may group a plurality of sensors based on the correlation between the processed data. Specifically, the grouping unit 260 may calculate the correlation coefficient between the processed data for all combinations of two sensors among the plurality of sensors, and classify sensors whose correlation coefficient is equal to or greater than a predetermined threshold into the same group.

[0075] For example, the grouping unit 260 may classify multiple sensors into predetermined groups. The groups may be determined based on domain knowledge. For example, sensors that are likely to have a physicochemical correlation with each other may be classified into the same group based on design information of the substrate processing apparatus 120. The groups may be determined according to the model of the substrate processing apparatus 120. In other words, sensors in substrate processing apparatuses 120 of the same model may be classified into the same group.

[0076] The analysis unit 270 analyzes the process executed by the substrate processing apparatus 120 based on the processed data read from the data storage unit 240. The analysis unit 270 may receive an analysis request from a user. The analysis request may include information indicating the analysis process and information indicating the analysis target. The information indicating the analysis target may include at least one of a step or a sensor. The step to be analyzed may include multiple steps. The multiple steps may be specified on a process basis or a recipe basis. The sensor to be analyzed may include multiple sensors. The multiple sensors may be specified in groups.

[0077] When the sensors to be analyzed are designated by group, the analysis unit 270 may acquire statistical values ​​of the sensor values ​​measured by the sensors classified into groups from the processed data and analyze the process based on a representative value of the statistical values ​​for each group. The analysis unit 270 may calculate a representative value of the statistical values ​​for each group and analyze fluctuations within or between groups. The analysis unit 270 may construct a model to be used in the analysis process based on the representative value of the statistical values ​​for each group. Examples of representative values ​​include the average, mode, median, maximum, and minimum values.

[0078] The analysis unit 270 reads processed data related to the analysis target indicated in the analysis request from the data storage unit 240, and executes the analysis process specified by the user on the read processed data. The analysis unit 270 outputs an analysis result indicating the result of the analysis process. For example, the analysis unit 270 may display the analysis result on the output device 502 connected to the analysis device 140.

[0079] <Processing Procedure> The analysis method executed by the substrate processing system 100 in this embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the analysis method.

[0080] In step S1, the acquisition unit 210 of the analysis device 140 acquires trace data generated by the substrate processing apparatus 120. The acquisition unit 210 may acquire trace data indicating sensor values ​​measured during execution of a process every time the substrate processing apparatus 120 executes the process. The acquisition unit 210 sends the acquired trace data to the statistical processing unit 220 and the excluding unit 230.

[0081] In step S2, the statistical processing unit 220 of the analysis device 140 receives the trace data from the acquisition unit 210. The statistical processing unit 220 calculates the processing time for each step included in the trace data. The statistical processing unit 220 determines whether the processing time for each step is less than a predetermined threshold. If the processing time is less than the threshold, the statistical processing unit 220 excludes the sensor value for that step from the trace data.

[0082] In step S3, the statistical processing unit 220 of the analysis device 140 calculates statistical values ​​of the sensor values ​​for each step included in the trace data. In this embodiment, the statistical processing unit 220 calculates the maximum, minimum, average, and standard deviation of the sensor values ​​included in each step. The statistical processing unit 220 sends the processed data including the statistical values ​​for each step to the exclusion unit 230.

[0083] In step S4, the exclusion unit 230 of the analysis device 140 receives the traced data from the acquisition unit 210. The exclusion unit 230 also receives the processed data from the statistical processing unit 220. The exclusion unit 230 determines whether the fluctuation range of the sensor value included in the traced data satisfies a certain value criterion. If the fluctuation range of the sensor value does not satisfy the certain value criterion, the exclusion unit 230 excludes the statistical value calculated based on the traced data from the processed data.

[0084] In step S5, the excluding unit 230 of the analysis device 140 determines whether the fluctuation range of the sensor value included in the trace data satisfies the minute fluctuation criterion. If the fluctuation range of the sensor value does not satisfy the minute fluctuation criterion, the excluding unit 230 excludes the statistical value calculated based on the trace data from the processed data.

[0085] The elimination unit 230 stores the processed data in the data storage unit 240. Therefore, the processed data stored in the data storage unit 240 includes only statistical values ​​based on trace data that meet the constant value criterion and the small fluctuation criterion.

[0086] In step S6, the analysis unit 270 of the analysis device 140 receives an analysis request from the user. The analysis request includes information indicating the analysis process and information indicating the analysis target. The information indicating the analysis target includes information including the steps and sensors. The analysis unit 270 reads out processed data related to the steps and sensors to be analyzed from the data storage unit 240. The analysis unit 270 sends the read processed data and information indicating the analysis process to the normalization unit 250.

[0087] In step S7, the normalization unit 250 of the analysis device 140 receives the processed data and information indicating the analysis process from the analysis unit 270. If the analysis process is machine difference analysis, the normalization unit 250 normalizes the processed data using a normalization method specialized for machine difference analysis. If the analysis process is a process other than machine difference analysis, the normalization unit 250 standardizes the processed data. The normalization unit 250 sends the normalized processed data to the grouping unit 260.

[0088] In step S8, the grouping unit 260 of the analysis device 140 receives the normalized processed data from the normalization unit 250. The grouping unit 260 groups the sensors provided in the substrate processing device 120. The grouping unit 260 groups the normalized processed data based on the sensor groups. The grouping unit 260 sends the grouped processed data to the analysis unit 270.

[0089] In step S9, the analysis unit 270 of the analysis device 140 receives the processed data from the grouping unit 260. The analysis unit 270 analyzes the process executed by the substrate processing apparatus 120 based on the processed data by the analysis processing indicated in the analysis request received in step S6.

[0090] If the analysis target indicated in the analysis request is a group of sensors, the analysis unit 270 calculates a representative value of the statistical values ​​for each group and analyzes fluctuations within or between groups. The analysis unit 270 may construct a model to be used in the analysis process based on the representative value of the statistical values ​​for each group.

[0091] In step S10, analysis unit 270 of analysis device 140 outputs the analysis result of the process in step S9. Specifically, analysis unit 270 displays the analysis result on output device 502 connected to analysis device 140.

[0092] A user of the analytical device 140 can verify the process executed by the substrate processing apparatus 120 by referring to the analysis results output from the analytical device 140. The user of the analytical device 140 can use the process verification results to help design or operate the substrate processing apparatus 120 or a process recipe.

[0093] <Effects of the embodiment> The analysis device 140 in this embodiment calculates statistical values ​​of sensor values ​​for each step based on sensor data indicating sensor values ​​measured by a sensor provided in a substrate processing apparatus that executes a process, and analyzes the process based on processed data including the statistical values ​​for each step. In one aspect, this embodiment allows the processed data to be reused in various analysis processes, thereby reducing the amount of calculation required for process analysis. Furthermore, this embodiment also allows the storage capacity required for process analysis to be reduced because it is only necessary to store statistical values ​​for each step.

[0094] Analysis device 140 may exclude sensor data if the fluctuation range of the sensor value included in the sensor data does not satisfy a predetermined criterion. The criterion may indicate that the fluctuation range is equal to or greater than a threshold value obtained by multiplying the resolution of the sensor by a predetermined coefficient. According to this embodiment, sensor data that does not need to be analyzed can be excluded based on domain knowledge.

[0095] Analysis device 140 may analyze the processed data using an analysis process specified by a user. Analysis device 140 may normalize the processed data using a method appropriate for the analysis process. When the analysis process is an instrument difference analysis, analysis device 140 may divide the processed data by a normalization coefficient obtained by converting the weighted average of the variances of the processed data into a standard deviation. According to this embodiment, the processed data can be normalized using an appropriate method appropriate for the analysis process.

[0096] The analysis device 140 may classify a plurality of sensors provided in the substrate processing apparatus into a plurality of groups. The analysis device 140 may analyze the process based on a representative value of statistical values ​​of sensor values ​​measured by the sensors classified into groups. The analysis device 140 may classify the sensors into groups based on design information of the substrate processing apparatus or correlations between processed data. According to this embodiment, since a large number of sensors can be analyzed in groups, the process can be analyzed accurately with a small amount of calculation.

[0097] [Variations] Some process recipes executed by substrate processing apparatuses include many repetitive processes. As an example, an atomic layer deposition (ALD) apparatus may execute a process recipe that includes many repetitive processes. A process recipe that includes repetitive processes has a huge number of steps, which increases the amount of calculations required for process analysis. Therefore, if trace data obtained when a process including repetitive processes is executed is preprocessed with a small amount of calculations, the amount of calculations required for process analysis can be further reduced.

[0098] In this modification, trace data including repeated processing is divided into multiple segments, and statistics are calculated for each segment. Each segment contains sensor values ​​measured during one or more repeated processing. In this case, segments near the beginning of the trace data contain sensor values ​​measured during a small number of repeated processing (e.g., one time), while segments near the end of the trace data contain sensor values ​​measured during a large number of repeated processing (e.g., two or more times).

[0099] Specifically, the first N-th repetitions of the tracing data are divided into segments, with each segment containing one repetition, and the N+1th repetitions and beyond are divided into M segments. If there are any surpluses after dividing into M segments, they can be added to the last segment. This divides the tracing data into N+M segments.

[0100] Fig. 7 is a diagram showing an example of a segment. Fig. 7 shows an example in which ten processes (Run1 to Run10) with different numbers of repetitions (cycles) are each divided into segments. In Fig. 7, N is 8 and M is 2.

[0101] As shown in Figure 7, in processes (Run1 to Run3) with N or fewer iterations, each iteration is included in one segment 1 to N. In processes (Run4 to Run10) with N+1 or more iterations, segments 9 to 10 contain the number of iterations obtained by subtracting N from the iteration count and dividing the result by M. For example, in Run10, which has 15 iterations, segment 9 contains three iterations ((15-8) / 2) and segment 10 contains four iterations ((15-8) / 2+1).

[0102] The statistical processing unit 220 calculates statistical values ​​for each segment. In a segment that includes one repetitive process, the statistical processing unit 220 calculates statistical values ​​for each step, as in the embodiment. On the other hand, in a segment that includes multiple repetitive processes, the statistical processing unit 220 calculates statistical values ​​for all the same steps executed in the multiple repetitive processes.

[0103] According to this modification, even when a process including many repeated processes is executed, the statistical values ​​included in the processed data can be suppressed to a certain number (e.g., N+M) or less. As a result, even when a process including many repeated processes is executed, analysis can be performed based on processed data including a certain number or less of statistical values, further reducing the amount of calculation required for process analysis.

[0104] [Other embodiments] The substrate processing apparatus for performing processes including the substrate processing method of the present disclosure is not limited to thermal processing apparatuses, and any type of apparatus such as atomic layer deposition (ALD) apparatus, capacitively coupled plasma (CCP), inductively coupled plasma (ICP), radial line slot antenna (RLSA), electron cyclotron resonance plasma (ECR), or helicon wave plasma (HWP) can be used as the substrate processing apparatus.

[0105] Furthermore, the substrate processing apparatus of the present disclosure can be applied to both plasma-using and non-plasma-using apparatuses that perform predetermined processing on substrates (e.g., film formation processing, etching processing, etc.). The substrate processing apparatus of the present disclosure can be applied to any of single-wafer apparatuses that process substrates one by one, batch apparatuses that process multiple substrates at once, and semi-batch apparatuses that process a smaller number of substrates at once than the number processed at once by a batch apparatus.

[0106] The information processing apparatus and substrate processing apparatus according to the presently disclosed embodiments are illustrative in all respects and are not limiting. The embodiments may be modified and improved in various ways without departing from the spirit and scope of the appended claims. The matters described in the above-described embodiments may be configured differently within a consistent range, and may be combined within a consistent range. [Explanation of symbols]

[0107] 100: Substrate processing system 110: Host device 120: Substrate processing equipment 121: Control device 140:Analysis equipment 150: Server device 210: Acquisition Department 220: Statistical processing unit 230: Exclusion part 240: Data storage unit 250: Standardization Department 260: Grouping section 270:Analysis Department

Claims

1. an acquisition unit configured to acquire sensor data indicating sensor values ​​measured by one or more sensors provided in a substrate processing apparatus that executes a process including one or more steps; a statistical processing unit configured to calculate a statistical value of the sensor value for each step based on the sensor data; an analysis unit configured to analyze the process based on the processed data including the statistical values ​​for each step; An information processing device comprising:

2. 2. The information processing device according to claim 1, an exclusion unit configured to exclude the sensor data when a fluctuation range of the sensor value included in the sensor data does not satisfy a predetermined criterion; Information processing device.

3. 3. The information processing device according to claim 2, The criterion indicates that the fluctuation range is equal to or greater than a threshold value obtained by multiplying the resolution of the sensor by a predetermined coefficient. Information processing device.

4. 4. The information processing device according to claim 1, the analysis unit is configured to analyze the processed data by an analysis process designated by a user; Information processing device.

5. 5. The information processing device according to claim 4, a normalization unit configured to normalize the processed data by a method corresponding to the analysis process; Information processing device.

6. 6. The information processing device according to claim 5, the normalization unit is configured to divide the processed data by a normalization coefficient obtained by converting a weighted average of variances of the processed data into a standard deviation when the analysis processing is an instrument difference analysis. Information processing device.

7. 5. The information processing device according to claim 4, a grouping unit configured to classify a plurality of sensors provided in the substrate processing apparatus into a plurality of groups; the analysis unit is configured to analyze the process based on a representative value of the statistical values ​​of the sensor values ​​measured by the sensors classified into the groups. Information processing device.

8. 8. The information processing device according to claim 7, the grouping unit is configured to classify the sensors into the groups based on design information of the substrate processing apparatus or a correlation between the processed data. Information processing device.

9. 4. The information processing device according to claim 1, the statistical processing unit is configured to, when the process includes an iterative process, calculate statistics of the sensor values ​​measured in a plurality of steps included in the iterative process. Information processing device.

10. The computer acquiring sensor data indicating sensor values ​​measured by one or more sensors provided in a substrate processing apparatus that executes a process including one or more steps; a step of calculating a statistical value of the sensor value for each step based on the sensor data; analyzing the process based on the processed data including the statistical values ​​for each step; An information processing method that performs the above.

11. On the computer, acquiring sensor data indicating sensor values ​​measured by one or more sensors provided in a substrate processing apparatus that executes a process including one or more steps; a step of calculating a statistical value of the sensor value for each step based on the sensor data; analyzing the process based on the processed data including the statistical values ​​for each step; A program to execute.

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