SUBSTRATE PROCESSING SYSTEM, INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING PROGRAM

Abstract

To provide a substrate processing system, an information processing apparatus, an information processing method, and an information processing program for quantifying a state in a processing space where plasma processing is performed.SOLUTION: A substrate processing system includes: an acquisition unit that acquires a plurality of types of time-series data each time a substrate is subjected to plasma processing; a learning unit that generates a number of trained outlier detection models corresponding to the number of types of time-series data by calculating the data density of each of the multiple types of time-series data acquired in a first phase; and a quantification unit that quantifies the state in a processing space in a second phase by inputting a plurality of types of time-series data acquired in the second phase to the corresponding trained outlier detection models and calculating the degree of deviation from the plurality of types of time-series data acquired in the first phase.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Attempts are being made to quantify, for example, the state in the processing space (chamber) where plasma processing is performed, using multiple types of time-series data acquired during the substrate manufacturing process. If the state in the processing space can be quantified and the control knob can be adjusted according to each state, the substrate after plasma processing can be made uniform. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] U.S. Patent No. 6,633,391 [Patent Document 2] U.S. Patent No. 7,829,468 [Patent Document 3] U.S. Pat. No. 1,056,513 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a substrate processing system, an information processing apparatus, an information processing method, and an information processing program for quantifying a state in a processing space where plasma processing is performed. [Means for solving the problem]

[0005] A substrate processing system according to an aspect of the present disclosure has, for example, the following configuration. an acquisition unit that acquires a plurality of types of time series data each time a plasma process is performed on a substrate; Calculate the data density of each type of time series data acquired in the first phase and learns the range of outliers By doing so, A trained outlier detection model for detecting outliers based on the range, The number of types of time series data corresponds to the number of The above A learning unit that generates a trained outlier detection model; Inputting the multiple types of time-series data acquired in the second phase into the corresponding trained outlier detection models, By aggregating binary information indicating outliers detected by the corresponding trained outlier detection model based on the range, Calculate the deviation from the multiple types of time series data obtained in the first phase The calculated deviation is , in the processing space in the second phase Quantified Status Output as information and a quantification unit for quantification of the detected Effect of the Invention

[0006] According to the present disclosure, it is possible to provide a substrate processing system, an information processing apparatus, an information processing method, and an information processing program that quantify a state in a processing space where plasma processing is performed. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 illustrates an example of a system configuration of a substrate processing system. [Diagram 2] FIG. 2 illustrates an example of a hardware configuration of a learning device. [Diagram 3] 11A to 11C are diagrams showing an example of multiple types of time-series data acquired each time a plasma process is performed on an unprocessed substrate in each phase. [Figure 4] FIG. 2 illustrates an example of a functional configuration of a learning device. [Diagram 5] FIG. 2 is a diagram illustrating an example of a functional configuration of a quantification device. [Figure 6] FIG. 13 is a diagram showing a specific example of the degree of deviation of each of a plurality of types of time series data. [Figure 7] FIG. 2 illustrates an example of a functional configuration of an adjustment device. [Figure 8] 13 is a flowchart showing the flow of an optimization process. [Figure 9] FIG. 4 is a diagram showing an example of emission intensity data acquired by an emission spectrometer. [Figure 10]11 is a diagram showing the relationship between the transition of the etching rate when plasma processing is performed on a plurality of unprocessed substrates and the operation phase in this embodiment. FIG. [Figure 11] FIG. 13 is a diagram showing an example of the degree of deviation of emission intensity data for each wavelength in the quantification phase. [Figure 12] FIG. 13 is a diagram showing the relationship between the deviation and the etching rate at a specific wavelength. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, each embodiment will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0009] [First embodiment] <System configuration of substrate processing system> First, the system configuration of the substrate processing system will be described. Fig. 1 is a diagram showing an example of the system configuration of the substrate processing system. As shown in Fig. 1, the substrate processing system 100 includes a substrate processing apparatus 110, time-series data acquiring apparatuses 120_1 to 120_n, a learning apparatus 130, a quantification apparatus 140, and an adjustment apparatus 150.

[0010] The substrate processing apparatus 110 has a processing space (chamber) in which plasma processing is performed, and performs plasma processing on objects (unprocessed substrates 1, 2, ..., etc.) in the processing space to generate resultants (processed substrates 1, 2, ..., etc.). Note that the unprocessed substrates 1, 2, ..., etc. referred to here refer to substrates before plasma processing is performed in the processing space, and the processed substrates 1, 2, ..., etc. refer to substrates after plasma processing is performed in the processing space.

[0011] The time series data acquiring devices 120_1 to 120_n are an example of an acquiring unit, and each acquires time series data before or during plasma processing on the unprocessed substrates 1, 2, ..., etc. in the processing space. The time series data acquiring devices 120_1 to 120_n perform measurements for different types of measurement items, and the number of measurement items measured by each of the time series data acquiring devices 120_1 to 120_n may be one or more.

[0012] In FIG. 1, time series data 1_1 to n_1 are time series data acquired by time series data acquiring devices 120_1 to 120_n before or during plasma processing on the "unprocessed substrate 1."

[0013] Similarly, the time series data 1_2 to n_2 are time series data acquired by the time series data acquiring devices 120_1 to 120_n before or during the plasma processing of the "unprocessed substrate 2."

[0014] The example in Figure 1 further shows time series data acquired before or during plasma processing on unprocessed substrate x, unprocessed substrate x+1, unprocessed substrate y, and unprocessed substrate y+1.

[0015] As described later, the operation phases of the substrate processing system 100 are classified into a learning phase (an example of a first phase), a quantification phase (an example of a second phase), and an adjustment phase (an example of a third phase).

[0016] Among these, the multiple types of time series data acquired in the learning phase are transmitted to the learning device 130. On the other hand, the multiple types of time series data acquired in the quantification phase are transmitted to the quantification device 140.

[0017] The learning device 130 calculates the data density at each time for each of the multiple types of time-series data acquired in the learning phase, and learns the range of outliers. Each trained model in which the range of outliers has been learned is notified to the quantification device 140.

[0018] The quantification device 140 detects outliers by inputting the multiple types of time series data acquired in the quantification phase into the corresponding trained model, and calculating the data density for each time of the multiple types of time series data. Furthermore, the quantification device 140 calculates the degree of deviation of each of the multiple types of time series data acquired in the quantification phase from the corresponding multiple types of time series data acquired in the learning phase based on the detected outlier. This allows the quantification device 140 to quantify the state (chamber condition) in the processing space when the plasma processing is performed on the unprocessed substrate in the quantification phase. The degree of deviation, which is the quantified state in the processing space, is notified to the adjustment device 150.

[0019] The adjustment device 150 calculates the amount of adjustment of the control knob according to the state in the processing space notified by the quantification device 140, and notifies the substrate processing apparatus 110 of the control knob that needs to be adjusted and the calculated amount of adjustment. This allows the substrate processing apparatus 110 to perform plasma processing suited to the state in the processing space when performing plasma processing on the next unprocessed substrate.

[0020] Although the multiple types of time series data are not described in detail in the description of FIG. 1, the multiple types of time series data may include, for example, process data measured by various sensors included in the substrate processing apparatus 110. Furthermore, the process data referred to here may include, for example, RF / DC data: RF power, reflected power, voltage, current, magnetic force, Gas pressure data: pressure in the processing space, FCS pressure, piping pressure, gas supply pressure, valve opening, backside He pressure, Gas flow data: RCS flow rate, MFC flow rate, pump speed, pump current value, pump temperature, Drive data: motor current, motor torque, motor RPM, Temperature data: wall temperature, coolant temperature, electrode temperature, substrate temperature, Alternatively, the multiple types of time-series data may include, for example, the following data measured by an optical emission spectrometer instead of or in addition to the process data: Emission data: Emission intensity data for each wavelength, Alternatively, the multiple types of time-series data may include, for example, the following measured by a spectroscopic reflectometer instead of or in addition to the process data: Spectroscopic data: Spectroscopic measurement data for each wavelength of the substrate and wall surface, Alternatively, the multiple types of time series data may include, for example, the following measured by a mass spectrometer (e.g., a quadrupole mass spectrometer) instead of or in addition to the process data: Mass spectrometry data: detection intensity data in a number corresponding to the number of types of mass-related values ​​(m / z values); Alternatively, the multiple types of time series data may include, for example, the following data measured by a plasma measurement device instead of or in addition to the process data: Ion data: ion energy distribution, ion angle distribution, etc. may be included.

[0021] <Hardware configuration of learning device, etc.> Next, the hardware configurations of the learning device 130, the quantification device 140, and the adjustment device 150 will be described. Since the learning device 130, the quantification device 140, and the adjustment device 150 have similar hardware configurations, the hardware configuration of the learning device 130 will be described here. Fig. 2 is a diagram showing an example of the hardware configuration of the learning device.

[0022] 2, the learning device 130 includes a processor 201, a memory 202, an auxiliary storage device 203, a user interface device 204, a connection device 205, and a communication device 206. The hardware components of the learning device 130 are connected to each other via a bus 207.

[0023] The processor 201 has various arithmetic devices such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. The processor 201 reads various programs (for example, a learning program described later) onto the memory 202 and executes them.

[0024] The memory 202 has a main storage device such as a Read Only Memory (ROM) and a Random Access Memory (RAM). The processor 201 and the memory 202 form a so-called computer, and the processor 201 executes various programs read onto the memory 202, causing the computer to realize various functions.

[0025] The auxiliary storage device 203 stores various programs and various data used when the processor 201 executes the various programs.

[0026] The user interface device 204 includes, for example, a keyboard or touch panel through which the user of the study device 130 inputs various commands, a display that displays the processing contents of the study device 130, and the like.

[0027] The connection unit 205 is a connection device that connects to other units in the substrate processing system 100. The communication unit 206 is a communication device for communicating with an external unit (not shown) via a network.

[0028] <Operation phase of substrate processing system> Next, a description will be given of the relationship between the operation phases of the substrate processing system 100 and the time-series data. Fig. 3 is a diagram showing an example of multiple types of time-series data acquired each time a plasma process is performed on an unprocessed substrate in each phase.

[0029] The example of FIG. 3 shows a case where plasma processing is performed on unprocessed substrates 1 to x in the learning phase. In this case, the learning device 130 has: From multiple types of time series data (time series data 1_1 to time series data n_1) acquired when plasma processing is performed on the unprocessed substrate 1, Multiple types of time series data (time series data 1_x to time series data n_x) acquired when plasma processing was performed on unprocessed substrate x, will be notified.

[0030] As a result, the learning device 130 calculates the data density at each time for each of the notified types of time-series data, and learns the range of outliers. As a result, the learning device 130 generates trained models whose number (n) corresponds to the number of types of time-series data.

[0031] 3 shows a case where plasma processing is performed on an unprocessed substrate x+1 in the quantification phase. In this case, the quantification device 140 has the following: Multiple types of time series data (time series data 1_x+1 to time series data n_x+1) acquired when plasma processing was performed on unprocessed substrate x+1, will be notified.

[0032] Thus, the quantification device 140 inputs the notified multiple types of time series data into the corresponding n trained models, and calculates the degree of deviation from each of the multiple types of corresponding time series data acquired in the training phase. As a result, the quantification device 140 can quantify the state (chamber condition) in the processing space when the plasma processing is performed on the unprocessed substrate x+1.

[0033] Moreover, the example in Figure 3 shows how the substrate processing apparatus 110 performs plasma processing on unprocessed substrate y, unprocessed substrate y+1, ... during the adjustment phase in which the adjustment device 150 adjusts the control knob based on the deviation degree, which is the quantified state within the processing space.

[0034] By configuring the control knob to be adjusted based on the condition within the processing space quantified in the quantification phase, and then performing plasma processing in the adjustment phase, it is possible to perform plasma processing appropriate to the condition within the processing space in the adjustment phase.

[0035] The timing of switching the operation phase in the substrate processing system 100 is arbitrary. For example, the learning phase may be switched to the quantification phase when a predetermined number of unprocessed substrates have been subjected to plasma processing. The quantification phase may be switched to the adjustment phase when a predetermined number of unprocessed substrates have been subjected to plasma processing. In this case, the substrate processing system 100 may be configured to switch all phases within one lot, or to switch each phase across multiple lots.

[0036] Furthermore, the order of acquiring the multiple types of time series data in the learning phase and the quantification phase by the substrate processing system 100 is arbitrary, and for example, the multiple types of time series data may be acquired in the order of the learning phase → the quantification phase as shown in Fig. 3. Specifically, the multiple types of time series data acquired in the quantification phase may be time series data acquired when the plasma processing is performed on an unprocessed substrate after the unprocessed substrate that was subjected to the plasma processing in the learning phase.

[0037] Alternatively, the substrate processing system 100 may be configured to acquire multiple types of time series data in the order of the quantification phase → learning phase. Specifically, the multiple types of time series data acquired in the quantification phase may be time series data acquired when a plasma process was performed on an unprocessed substrate prior to the unprocessed substrate that was subjected to the plasma process in the learning phase.

[0038] Furthermore, the execution pattern of the operation phases in the substrate processing system 100 is arbitrary, and the substrate processing system 100 may be configured to execute, for example, the quantification phase and the adjustment phase in parallel. Specifically, the substrate processing system 100 may be configured to notify the quantification device 140 of a plurality of types of time series data (time series data 1_y to time series data n_y, etc.) acquired when plasma processing is performed on the unprocessed substrate y, the time series data y+1, ..., etc. in the adjustment phase. This allows the substrate processing system 100 to execute the quantification phase in parallel with the adjustment phase.

[0039] As a result, for example, during the adjustment phase, the adjustment device 150 adjusts the control knob based on the quantified conditions within the processing space each time plasma processing is performed on a certain number of unprocessed substrates, and plasma processing can be performed under the adjusted control knob.

[0040] However, even in this case, the adjustment amount of the control knob used when performing plasma processing in the adjustment phase is calculated based on the deviation calculated in the immediately preceding quantification phase.

[0041] Furthermore, in the substrate processing system 100, the learning phase and the quantification phase may be executed any number of times. For example, the substrate processing system 100 may be configured to execute the learning phase and the quantification phase multiple times within the same lot, or may be configured to execute once every time the lot is changed. Alternatively, in the case where plasma processing is performed on multiple lots using the same recipe, the learning phase and the quantification phase may be executed once every time the recipe is changed. Alternatively, the learning phase and the quantification phase may be executed once every time plasma processing is performed on a predetermined number of lots (or plasma processing is performed on a predetermined number of unprocessed substrates). Alternatively, the learning phase and the quantification phase may be executed once at a predetermined time.

[0042] <Functional configuration of the learning device> Next, a description will be given of the functional configuration of the learning device 130. Fig. 4 is a diagram showing an example of the functional configuration of the learning device 130. A learning program is installed in the learning device 130, and the learning device 130 functions as a learning unit 400 by executing the program.

[0043] As shown in FIG. 4, the learning unit 400 has outlier detection models (models 410_1 to 410_n) in a number corresponding to the number of types of time-series data.

[0044] Model 410_1 has: In the learning phase, the unprocessed substrate 1 is subjected to plasma processing, and from the time-series data 1_1 acquired by the time-series data acquisition device 120_1, In the learning phase, the plasma processing is performed on the unprocessed substrate x, and the time-series data 1_x is acquired by the time-series data acquisition device 120_1. is input. In this way, in the learning phase, the model 410_1 calculates the data density at each time for the time series data acquired by the time series data acquiring device 120_1 every time the plasma processing is performed. Furthermore, in the learning phase, the model 410_1 learns the range of outliers for the time series data acquired by the time series data acquiring device 120_1 every time the plasma processing is performed. The range of outliers learned by the model 410_1 in the learning phase is set in the model 510_1 described later as reference range information.

[0045] Similarly, model 410_2 has From the time-series data 2_1 acquired by the time-series data acquisition device 120_2 as a result of performing plasma processing on the unprocessed substrate 1 in the learning phase, In the learning phase, the plasma processing is performed on the unprocessed substrate x, and the time-series data 2_x is acquired by the time-series data acquisition device 120_2. is input. In this way, in the learning phase, the model 410_2 calculates the data density at each time for the time series data acquired by the time series data acquiring device 120_2 every time the plasma processing is performed. Furthermore, in the learning phase, the model 410_2 learns the range of outliers for the time series data acquired by the time series data acquiring device 120_1 every time the plasma processing is performed. The range of outliers learned by the model 410_2 in the learning phase is set in the model 510_2 described later as reference range information.

[0046] Hereinafter, since the models 410_3 to 410_n-1 are omitted in FIG. 4, a description of these outlier detection models will be omitted.

[0047] Model 410_n has: From the time-series data n_1 acquired by the time-series data acquisition device 120_n as a result of performing plasma processing on the unprocessed substrate 1 in the learning phase, In the learning phase, the plasma processing is performed on the unprocessed substrate x, and the time-series data n_x is acquired by the time-series data acquisition device 120_n. is input. In this way, in the learning phase, the model 410_n calculates the data density at each time for the time series data acquired by the time series data acquiring device 120_n every time a plasma process is performed. Furthermore, in the learning phase, the model 410_n learns the range of outliers for the time series data acquired by the time series data acquiring device 120_n every time a plasma process is performed. The range of outliers learned by the model 410_n in the learning phase is set in the model 510_n, which will be described later, as reference range information.

[0048] The reference range information set for each of the models 510_1 to 510_n is arbitrary. If the multiple types of time series data input to each of the models 410_1 to 410_n are, for example, time series data acquired when the plasma processing is performed under normal conditions in the processing space, the reference range information is information indicating a normal range. If the multiple types of time series data input to each of the models 410_1 to 410_n are, for example, time series data acquired when the plasma processing is performed under abnormal conditions in the processing space, the reference range information is information indicating an abnormal range.

[0049] Alternatively, among the multiple types of time series data input to each of the models 410_1 to 410_n, -Multiple types of time series data obtained when plasma processing is performed under normal conditions in the processing space; and -Multiple types of time series data obtained when plasma processing is performed under abnormal conditions in the processing space; and In this case, the learning unit 400 performs the process in the following procedure, for example. By inputting multiple types of time series data acquired when plasma processing is performed under normal conditions in the processing space into models 410_1 to 410_n, respectively, the range of outliers is learned, and information indicating the normal range is calculated as reference range information. A plurality of types of time-series data acquired when plasma processing is performed under abnormal conditions in the processing space are input to the trained models 410_1 to 410_n, respectively, and the detection accuracy of the outliers detected based on the calculated normal range is verified. Among the models 410_1 to 410_n, a model having high accuracy in detecting outliers is identified as a model to be used in the quantification phase.

[0050] <Functional configuration of the quantification device> Next, a functional configuration of the quantification device 140 will be described. Fig. 5 is a diagram showing an example of the functional configuration of the quantification device. A quantification program is installed in the quantification device 140, and the quantification device functions as a quantification unit 500 by executing the program.

[0051] 5, the quantification unit 500 has trained outlier detection models (models 510_1 to 510_n) whose number corresponds to the number of types of time-series data, and deviation calculation units (deviation calculation units 520_1 to 520_n). The quantification unit 500 also has an output unit 530.

[0052] Model 510_1 has: Time-series data 1_x+1 acquired by the time-series data acquisition device 120_1 as a result of performing plasma processing on the unprocessed substrate x+1 in the quantification phase; is input. As a result, the model 510_1 calculates the data density at each time for the time series data acquired by the time series data acquiring device 120_1 every time the plasma processing is performed. Furthermore, the model 510_1 detects an outlier of the time series data based on the set reference range information, and notifies the deviation calculation unit 520_1.

[0053] Similarly, model 510_2 has Time-series data 2_x+1 acquired by the time-series data acquisition device 120_2 as a result of performing plasma processing on the unprocessed substrate x+1 in the quantification phase; is input. As a result, the model 510_2 calculates the data density at each time for the time series data acquired by the time series data acquiring device 120_2 every time the plasma processing is performed. Furthermore, the model 510_2 detects an outlier of the time series data based on the set reference range information, and notifies the deviation calculation unit 520_2.

[0054] Hereinafter, since the models 510_3 to 510_n-1 are omitted in FIG. 5, a description of these outlier detection models will be omitted.

[0055] Model 510_n has: In the quantification phase, time-series data n_x+1 is acquired by the time-series data acquisition device 120_n as a result of performing plasma processing on the unprocessed substrate x+1; is input. As a result, the model 510_n calculates the data density at each time for the time series data acquired by the time series data acquiring device 120_n every time a plasma process is performed. Furthermore, the model 510_n detects an outlier of the time series data based on the set reference range information, and notifies the deviation calculation unit 520_n.

[0056] The deviation calculation unit 520_1 calculates the deviation of the entire time-series data 1_x+1 based on the binary information indicating the outlier detected by the model 510_1, and notifies the output unit 530 of the deviation.

[0057] Similarly, the deviation calculation unit 520_2 calculates the deviation of the entire time-series data 2_x+1 based on the binary information indicating the outlier detected by the model 510_2, and notifies the output unit 530 of the deviation.

[0058] Hereinafter, since deviation calculation section 520_3 to deviation calculation section 520_n-1 are omitted in FIG. 5, description of these deviation calculation sections will be omitted.

[0059] The deviation calculation unit 520_n calculates the deviation of the entire time-series data n_x+1 based on the binary information indicating the outlier detected by the model 510_n, and notifies the output unit 530 of the deviation.

[0060] The output unit 530 identifies a deviation calculation unit corresponding to a pre-specified model (or a model specified by the learning unit 400) among the models 510_1 to 510_n. In addition, the output unit 530 notifies the adjustment device 150 of the deviation notified by the identified deviation calculation unit as the state (chamber condition) in the processing space quantified in the quantification phase.

[0061] <Specific examples of deviations> Next, a specific example of the deviations output from the deviation calculation units 520_1 to 520_n will be described. Fig. 6 is a diagram showing a specific example of the deviations of multiple types of time series data. In Fig. 6, the horizontal axis represents the type of time series data. Also, the vertical axis represents the deviation of the entire time series data calculated for each of the multiple types of time series data acquired in the quantification phase.

[0062] As shown in the example of FIG. 6, depending on the type of time-series data, the time-series data acquired in the quantification phase may significantly deviate from the time-series data acquired in the learning phase.

[0063] Therefore, by focusing on the degree of deviation of a particular type of time series data, it is possible to grasp the change in state within the processing space in the quantification phase relative to the learning phase.

[0064] <Functional configuration of the adjustment device> Next, a description will be given of the functional configuration of the adjustment device 150. Fig. 7 is a diagram showing an example of the functional configuration of the adjustment device. An adjustment program is installed in the adjustment device 150, and the adjustment device 150 functions as an adjustment unit 710 by executing the program.

[0065] When the adjustment unit 710 acquires the deviation, which is the state in the processing space quantified by the quantification device 140, it refers to the relationship data storage unit 720. The relationship data storage unit 720 stores a table 730 in which the correspondence between the deviation output from the deviation calculation unit corresponding to a specific model and the adjustment amount of the corresponding control knob is experimentally obtained in advance.

[0066] Adjustment unit 710 calculates the adjustment amount of the corresponding control knob based on the deviation output from the deviation calculation unit corresponding to a specific model by referring to table 730. The example of FIG. 7 shows that the adjustment amount ("f(deviation 1)") of the corresponding control knob ("control knob A") is calculated based on the deviation ("deviation 1") output from deviation calculation unit 520_1 corresponding to model 510_1. Also, the example of FIG. 7 shows that the adjustment amount ("f(deviation 2)") of the corresponding control knob ("control knob B") is calculated based on the deviation ("deviation 2") output from deviation calculation unit 520_2 corresponding to model 510_2. In addition, the example of Figure 7 shows that the adjustment amount ("f(deviation 3)") of the corresponding control knob ("control knob C") is calculated based on the deviation ("deviation 3") output from deviation calculation unit 520_3 corresponding to model 510_3.

[0067] <Flow of optimization process by substrate processing system> Next, a flow of an optimization process for optimizing the plasma processing by quantifying the state in the processing space and adjusting the control knob according to each state in the plasma processing by the substrate processing system 100 will be described. Fig. 8 is a flowchart showing the flow of the optimization process. The flowchart in Fig. 8 shows the flow of the optimization process when the learning phase, the quantification phase, and the adjustment phase are executed once at a predetermined timing.

[0068] In step S801, the learning device 130 acquires a plurality of types of time-series data acquired by each time-series data acquisition device when the substrate processing apparatus 110 performs plasma processing in the learning phase.

[0069] In step S802, the learning device 130 calculates the data density for each time by inputting the multiple types of time-series data acquired in the learning phase into the corresponding models, and learns the range of outliers. In this way, the learning device 130 generates trained models in the number corresponding to the number of types of time-series data.

[0070] In step S803, the quantification device 140 acquires a plurality of types of time series data acquired by each time series data acquisition device when the plasma processing is performed by the substrate processing apparatus 110 in the quantification phase.

[0071] In step S804, the quantification device 140 inputs the multiple types of time series data acquired in the quantification phase into the corresponding trained models, thereby calculating the data density at each time and detecting outliers. The quantification device 140 also calculates the deviation of each of the multiple types of time series data acquired in the quantification phase from the corresponding multiple types of time series data acquired in the learning phase based on the detected outliers. The quantification device 140 also notifies the adjustment device 150 of the deviation corresponding to a specific trained model as a quantified state in the processing space.

[0072] In step S805, the adjustment device 150 calculates the adjustment amount of the corresponding control knob based on the deviation degree, which is the quantified state in the processing space, and notifies the substrate processing apparatus 110 of the control knob that needs to be adjusted and the calculated adjustment amount in the adjustment phase.

[0073] <Summary> As is apparent from the above description, the substrate processing system 100 according to the first embodiment has the following features: Each time plasma processing is performed on an unprocessed substrate, multiple types of time-series data are acquired. In the learning phase, the data density of each type of time series data obtained is calculated, and the range of outliers is learned. This generates a number of trained outlier detection models according to the number of types of time series data. Input each of the multiple types of time series data acquired in the quantification phase into the corresponding trained outlier detection model. This allows the degree of deviation from the multiple types of time series data acquired in the learning phase to be calculated, and the state in the processing space in the quantification phase to be quantified.

[0074] As a result, according to the first embodiment, the state within the processing space where the plasma processing is performed can be quantified.

[0075] <<Example>> Next, an example of the substrate processing system 100 according to the first embodiment will be described. Substrate processing equipment: Etching processing equipment, Time-series data acquisition devices 120_1 to 120_n: emission spectroscopic analyzers with wavelengths of 200 to 800 [nm] (n=601), Time series data 1_1 to time series data 1_n: emission intensity data for wavelengths from 200 to 800 [nm], Model: Outlier detection model, Timing of switching operation phase: Within one lot, - Unprocessed substrate in the learning phase: the 10th unprocessed substrate in one lot, Unprocessed substrates in the quantification phase: the first and ninth unprocessed substrates in one lot, Unprocessed substrates in the adjustment phase: Unprocessed substrates from the 11th substrate onward in one lot, Each operation phase was executed under the above configuration example.

[0076] (1) Specific examples of emission intensity data Fig. 9 is a diagram showing an example of emission intensity data acquired by an emission spectrometer. In Fig. 9, the horizontal axis represents wavelengths of 200 to 800 [nm], and the vertical axis represents the emission intensity of each wavelength at a predetermined time. In this example, emission intensity data of each wavelength during plasma processing (see Fig. 9) was acquired as multiple types of time-series data.

[0077] (2) Specific examples of operation phases Fig. 10 is a diagram showing the relationship between the transition of the etching rate when plasma processing is performed on a plurality of unprocessed substrates and the operation phase in this embodiment. In Fig. 10, the horizontal axis represents the number of unprocessed substrates that have been plasma-processed, and the vertical axis represents the etching rate when plasma processing is performed on each unprocessed substrate. Furthermore, the upper part shows the operation phase when plasma processing is performed on each unprocessed substrate.

[0078] In the example of Figure 10, when the number of unprocessed substrates that have been plasma-processed is small, the etching rate is low, and as the number of unprocessed substrates that have been plasma-processed increases, the etching rate increases and eventually stabilizes.

[0079] In this embodiment, with respect to such a change in the etching rate, the plasma treatment for the tenth unprocessed substrate was set as a learning phase, and the plasma treatments for the first and ninth unprocessed substrates were set as quantification phases. Furthermore, the plasma treatments for the eleventh and subsequent unprocessed substrates were set as adjustment phases.

[0080] (3) Specific examples of the deviation of each wavelength in the quantification phase 11A and 11B are diagrams showing an example of the deviation of the emission intensity data of each wavelength in the quantification phase. Among these, Fig. 11A shows the emission intensity data of each wavelength acquired when the first unprocessed substrate is subjected to plasma processing in the quantification phase, and the deviation calculated by inputting the emission intensity data of each wavelength into the trained model.

[0081] Moreover, Figure 11(b) shows the emission intensity data for each wavelength obtained when plasma processing was performed on the ninth unprocessed substrate in the quantification phase, and the deviation calculated by inputting the emission intensity data for each wavelength into the trained model.

[0082] 11(a) and 11(b), the emission intensity data for each wavelength is similar, but the deviations for specific wavelengths are significantly different. Here, we consider the relationship between the deviations and the etching rate.

[0083] As shown in Fig. 10, the tenth unprocessed substrate that was plasma-processed in the learning phase was a substrate that was plasma-processed after the etching rate had stabilized. In addition, among the unprocessed substrates that were plasma-processed in the quantification phase, the ninth unprocessed substrate was also a substrate that was plasma-processed after the etching rate had stabilized. In other words, the two substrates were plasma-processed under conditions in which the difference in etching rate was small. In addition, as shown in Fig. 11(b), the emission intensity data of each wavelength acquired when the ninth unprocessed substrate was plasma-processed in the quantification phase has a small deviation from the emission intensity data of each wavelength acquired in the learning phase.

[0084] On the other hand, as shown in Fig. 10, among the unprocessed substrates plasma-processed in the quantification phase, the first unprocessed substrate is a substrate plasma-processed before the etching rate becomes stable, and the difference from the etching rate in the learning phase is large. Also, as shown in Fig. 11(a), the emission intensity data of each wavelength acquired when the first unprocessed substrate is plasma-processed in the quantification phase is largely different from the emission intensity data of each wavelength acquired in the learning phase.

[0085] In this way, it can be said that the degree of deviation from the emission intensity data of each wavelength acquired in the learning phase generally represents a change in the etching rate, which is an example of a state within the processing space.

[0086] Specifically, if the plasma processing performed after the etching rate has stabilized is considered to be the learning phase, the deviation from the emission intensity data for each wavelength acquired in the learning phase can be said to represent the change from the etching rate after it has stabilized.

[0087] Furthermore, if the plasma processing performed before the etching rate stabilizes is considered to be the learning phase, the deviation from the emission intensity data for each wavelength acquired in the learning phase can be said to represent the change from the etching rate before it stabilized.

[0088] (4) Identifying wavelength Fig. 12 is a diagram showing the relationship between the deviation and the etching rate at a specific wavelength. Of these, Fig. 12(a) is the same graph as Fig. 11(a) with a specific wavelength region 1200 added. Meanwhile, Fig. 12(b) is a diagram showing the relationship between the deviation included in the specific wavelength region 1200 shown in Fig. 12(a) and the etching rate.

[0089] As shown in FIG. 12(b), the deviation at a specific wavelength and the etching rate have a roughly linear relationship.

[0090] Therefore, according to this embodiment, by specifying the wavelength deviation included in region 1200 among the wavelength deviations calculated in the quantification phase and adjusting the control knob, it is possible to perform plasma processing suitable for changing the etching rate. Note that the control knob at this time may be adjusted by adding an adjustment amount according to the specified wavelength deviation to the control amount of the control knob used in the learning phase (i.e., the plasma processing for the 10th unprocessed substrate), for example.

[0091] [Other embodiments] In the above first embodiment, a case where an outlier detection model is used is described, but the type of model is not limited to this, and other models may be used as long as they can detect outliers in data based on the data density of time-series data.

[0092] In the first embodiment, the learning device 130, the quantification device 140, and the adjustment device 150 are provided separately from the substrate processing apparatus 110. However, some or all of the learning device 130, the quantification device 140, and the adjustment device 150 may be provided integrally with the substrate processing apparatus 110.

[0093] In the above first embodiment, the learning device 130, the quantification device 140, and the adjustment device 150 are described as being configured as separate entities, but some or all of these devices may be configured as an integrated information processing device. In this case, an information processing program including some or all of the learning program executed in the learning device 130, the quantification program executed by the quantification device 140, and the adjustment program executed in the adjustment device 150 is executed in the information processing device.

[0094] The present invention is not limited to the configurations shown in the above embodiments, combinations with other elements, etc. These points can be changed without departing from the spirit of the present invention, and can be appropriately determined according to the application form. [Explanation of symbols]

[0095] 100: Substrate processing system 110: Substrate processing apparatus 120_1~120_n: Time series data acquisition device 130: Learning device 140:Quantification device 150: Adjustment device 400: Learning Department 410_1~410_n : Model 500: Quantification section 510_1~510_n : Model 520_1~520_n: Deviation calculation section 530: Output section 710: Adjustment section

Claims

1. an acquisition unit that acquires a plurality of types of time series data each time a plasma process is performed on a substrate; a learning unit that generates a number of trained outlier detection models corresponding to the number of types of time series data, the number of trained outlier detection models being determined by calculating a data density for each of the multiple types of time series data acquired in the first phase and learning an outlier range to detect an outlier based on the range; a quantification unit that inputs the multiple types of time-series data acquired in the second phase into the corresponding trained outlier detection models, calculates a deviation from the multiple types of time-series data acquired in the first phase by aggregating binary information indicating outliers detected by the corresponding trained outlier detection models based on the range, and outputs the calculated deviation as information indicating a quantified state in the processing space in the second phase; A substrate processing system comprising:

2. 2. The substrate processing system of claim 1, wherein the learning unit learns the outlier ranges of each of the multiple types of time series data acquired in the first phase based on the calculated data density, and generates the trained outlier detection model for detecting outliers based on the ranges.

3. 3. The substrate processing system of claim 2, wherein the quantification unit detects outliers based on the corresponding range in each of the multiple types of time series data by inputting each of the multiple types of time series data acquired in the second phase into a corresponding one of the trained outlier detection models, and calculates the deviation for each of the multiple types of time series data by aggregating binary information indicating the detected outliers.

4. an adjustment unit that adjusts a corresponding control knob based on a specific deviation degree among the deviation degrees calculated by the quantification unit; The substrate processing system according to claim 2 , wherein in the third phase, when the plasma processing is performed on the substrate, the plasma processing is performed using the control knob after being adjusted by the adjustment unit.

5. 5. The substrate processing system of claim 4, wherein the multiple types of time series data acquired in the second phase are time series data acquired when plasma processing is performed on a substrate subsequent to the substrate on which plasma processing is performed in the first phase.

6. 5. The substrate processing system of claim 4, wherein the multiple types of time series data acquired in the second phase are time series data acquired when plasma processing was performed on a substrate prior to the substrate on which plasma processing was performed in the first phase.

7. an acquisition unit that acquires a plurality of types of time series data each time a plasma process is performed on a substrate; a learning unit that generates a number of trained outlier detection models corresponding to the number of types of time series data, the number of trained outlier detection models being determined by calculating a data density for each of the multiple types of time series data acquired in the first phase and learning an outlier range to detect an outlier based on the range; a quantification unit that inputs the multiple types of time-series data acquired in the second phase into the corresponding trained outlier detection models, calculates a deviation from the multiple types of time-series data acquired in the first phase by aggregating binary information indicating outliers detected by the corresponding trained outlier detection models based on the range, and outputs the calculated deviation as information indicating a quantified state in the processing space in the second phase; An information processing device having the above configuration.

8. an acquiring step of acquiring a plurality of types of time series data each time a plasma processing is performed on a substrate; a learning process for generating a trained outlier detection model for detecting outliers based on an outlier range by calculating a data density for each of the multiple types of time series data acquired in the first phase and learning the range of the outliers, the number of the trained outlier detection models corresponding to the number of types of the time series data; a quantification step of inputting the multiple types of time-series data acquired in the second phase into the corresponding trained outlier detection models, aggregating binary information indicating outliers detected by the corresponding trained outlier detection models based on the ranges, thereby calculating a deviation from the multiple types of time-series data acquired in the first phase, and outputting the calculated deviation as information indicating a quantified state in the processing space in the second phase; An information processing method implemented by a computer.

9. an acquiring step of acquiring a plurality of types of time series data each time a plasma processing is performed on a substrate; a learning process for generating a trained outlier detection model for detecting outliers based on an outlier range by calculating a data density for each of the multiple types of time series data acquired in the first phase and learning the range of the outliers, the number of the trained outlier detection models corresponding to the number of types of the time series data; a quantification step of inputting the multiple types of time-series data acquired in the second phase into the corresponding trained outlier detection models, aggregating binary information indicating outliers detected by the corresponding trained outlier detection models based on the ranges, thereby calculating a deviation from the multiple types of time-series data acquired in the first phase, and outputting the calculated deviation as information indicating a quantified state in the processing space in the second phase; An information processing program for causing a computer to execute the above.

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