Information processing device, program, and process condition search method

Abstract

To provide a technology for retrieving processing conditions that can achieve a targeted processing result by using a machine learning model of a semiconductor manufacturing device.SOLUTION: An information processing device for creating a machine learning model of a semiconductor manufacturing device for executing processing according to a process condition, and retrieving processing conditions that can achieve a targeted processing result by using the machine learning model, includes: means for selecting a machine learning device suitable for a data set among a plurality of machine learning models created by a plurality of regression methods; means for performing an optimization calculation by using the selected machine learning model to calculate a plurality of process conditions, prediction values of process results, and reliabilities of the prediction values; means for selecting one or more process conditions according to the prediction values of the process results and the reliabilities of the prediction values; and means for displaying the selected process conditions, the prediction values of the process results and the reliabilities of the prediction values.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] For example, a technique for estimating physical quantities when a simulation of an etching process is repeatedly performed while changing parameters has been conventionally known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2016 / 132759 Brochure Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a technique for searching for process conditions that can achieve a target process result using a machine learning model of a semiconductor manufacturing equipment. [Means for solving the problem]

[0005] One aspect of the present disclosure is an information processing device that creates a machine learning model of a semiconductor manufacturing device that executes processing according to process conditions, and searches for the process conditions that can achieve a target process result using the machine learning model, A machine learning model creation means for creating a plurality of machine learning models trained using a plurality of regression methods by using learning data included in the dataset stored in the dataset storage unit; Among the multiple machine learning models created, Using evaluation data included in the dataset stored in the dataset storage unit, The above The data set storage unit storesThe system includes a machine learning model selection means for selecting the machine learning model suitable for a data set, a calculation means for performing an optimization calculation using the selected machine learning model and calculating a plurality of process conditions for achieving the target process result, a predicted value of the process result corresponding to the process conditions, and a reliability of the predicted value, a process condition selection means for selecting one or more process conditions from the plurality of process conditions for achieving the target process result in accordance with the predicted value of the process result and the reliability of the predicted value, and a display control means for displaying the selected process condition, the predicted value of the process result corresponding to the selected process condition, and the reliability of the predicted value. Effect of the Invention

[0006] According to the present disclosure, a machine learning model of a semiconductor manufacturing equipment can be used to search for process conditions that can achieve a target process result. [Brief description of the drawings]

[0007] [Figure 1] 1 is a configuration diagram of an example of an information processing system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating a hardware configuration of an example of a computer. [Diagram 3] FIG. 2 is a functional block diagram of an example of an analysis server according to the present embodiment. [Figure 4] 10 is a flowchart of an example of processing of the information processing system according to the embodiment. [Diagram 5] FIG. 13 is an image diagram of an example of an input screen. [Figure 6] FIG. 13 is an image diagram of an example of an output screen. [Figure 7] 13 is a display example of an example of a result of a multi-objective optimization calculation. [Figure 8] 13 is a display example of an example of the reliability of a prediction. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0009] <System configuration> 1 is a configuration diagram of an example of an information processing system according to the present embodiment. The information processing system 1 shown in FIG. 1 includes a semiconductor manufacturing apparatus 10, an apparatus controller 20, a host computer 22, an external measuring instrument 24, and an analysis server 26.

[0010] The semiconductor manufacturing equipment 10, the equipment controller 20, the host computer 22, the external measuring device 24, and the analysis server 26 are communicatively connected via a network 40 such as a LAN (Local Area Network).

[0011] The semiconductor manufacturing equipment 10 is, for example, a heat treatment film forming equipment, and executes processing corresponding to each step of the semiconductor manufacturing process (e.g., film forming, etching, ashing, cleaning, etc.) according to control commands (process conditions) output from the equipment control controller 20. The process conditions are conditions of the semiconductor manufacturing process, and are a combination of parameters that control (adjust) the control parts (control knobs) of the semiconductor manufacturing equipment 10. There are a wide variety of combinations of parameters that adjust the control knobs.

[0012] The equipment controller 20 is a controller having a computer configuration for controlling the semiconductor manufacturing equipment 10. The equipment controller 20 outputs the selected process conditions, which are calculated as described below, to the semiconductor manufacturing equipment 10 as parameters for adjusting the control knob of the semiconductor manufacturing equipment 10.

[0013] The host computer 22 is an example of a man-machine interface (MMI) that receives instructions for the semiconductor manufacturing equipment 10 from an operator and provides information about the semiconductor manufacturing equipment 10 to the operator by displaying the information, etc.

[0014] The external measuring instrument 24 is a measuring instrument, such as a film thickness measuring instrument, a sheet resistance measuring instrument, or a particle measuring instrument, that measures the results (process results) after a process is executed according to process conditions. The external measuring instrument 24 measures the actual values ​​of indices that judge the validity of the process results. There are a wide variety of indices that judge the validity of the process results. For example, the external measuring instrument 24 measures the adhesion of a film on a wafer such as a monitor wafer (film formation result) as an example of the actual value of the process result.

[0015] The analysis server 26 creates multiple machine learning models using multiple regression methods, and calculates process conditions that can achieve a target process result using the created machine learning models, as described below. In addition, the analysis server 26 selects process conditions to be proposed to an operator, etc., from multiple process conditions that can achieve a target process result, according to a predicted value of the process result corresponding to each process condition and the reliability of the predicted value, etc., as described below.

[0016] The operator causes semiconductor manufacturing equipment 10 to execute processing according to the process conditions selected by analysis server 26. After the processing is executed according to the process conditions, the operator measures the actual values ​​of the process results using external measuring device 24. The process conditions and the process results when semiconductor manufacturing equipment 10 executes processing according to those process conditions (process results corresponding to the process conditions) are fed back to analysis server 26, thereby improving the accuracy of the process conditions calculated next time and thereafter.

[0017] If the actual measured value of the process result achieves the target value of the process result, the operator ends the search for process conditions capable of achieving the target process result, and if the actual measured value of the process result does not achieve the target value of the process result, the operator continues the search for process conditions capable of achieving the target process result.

[0018] It is needless to say that the information processing system 1 in Fig. 1 is one example, and there are various system configuration examples according to the use and purpose. The division of the equipment such as the semiconductor manufacturing equipment 10, the equipment controller 20, the host computer 22, the external measuring device 24, and the analysis server 26 in Fig. 1 is one example.

[0019] For example, the information processing system 1 can have various configurations, such as a configuration in which two or more of the semiconductor manufacturing equipment 10, the equipment controller 20, the host computer 22, the external measuring device 24, and the analysis server 26 are integrated together, or a configuration in which they are further divided.

[0020] <Hardware configuration> The device controller 20, the host computer 22, and the analysis server 26 of the information processing system 1 are realized by a computer having the hardware configuration shown in Fig. 2. Fig. 2 is a diagram showing the hardware configuration of an example of a computer.

[0021] 2 includes an input device 501, an output device 502, an external I / F (interface) 503, a RAM (Random Access Memory) 504, a ROM (Read Only Memory) 505, a CPU (Central Processing Unit) 506, a communication I / F 507, and a HDD (Hard Disk Drive) 508, all of which are connected to each other via a bus B. The input device 501 and the output device 502 may be connected and used when necessary.

[0022] The input device 501 is a keyboard, mouse, touch panel, etc., and is used by an operator 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 the network 40. The HDD 508 is an example of a non-volatile storage device that stores programs and data.

[0023] 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 a Secure Digital (SD) memory card via the external I / F 503. The ROM 505 is an example of a non-volatile semiconductor memory (storage device) in which programs and data are stored. The RAM 504 is an example of a volatile semiconductor memory (storage device) that temporarily holds programs and data.

[0024] The CPU 506 is a calculation device that reads out programs and data from storage devices such as the ROM 505 and the HDD 508 onto the RAM 504 and executes processes, thereby realizing overall control and functions of the computer 500 .

[0025] The device controller 20, host computer 22, and analysis server 26 shown in FIG. 1 can realize various functions described below by executing programs on a computer 500 having the hardware configuration shown in FIG.

[0026] <Functional configuration> The analysis server 26 of the information processing system 1 according to this embodiment is realized by, for example, a functional block as shown in Fig. 3. Fig. 3 is a functional block diagram of an example of the analysis server according to this embodiment. Note that the functional block diagram of Fig. 3 omits illustration of configurations that are not necessary for the description of this embodiment.

[0027] By executing a program for the analysis server 26, the analysis server 26 realizes a machine learning model creation unit 50, a machine learning model selection unit 52, a calculation unit 54, a process condition selection unit 56, a display control unit 58, a process result reception unit 60, a judgment unit 62, a feedback unit 64, and a dataset storage unit 66.

[0028] The dataset storage unit 66 stores a dataset used by the machine learning model creation unit 50 and the machine learning model selection unit 52. The dataset stored in the dataset storage unit 66 associates a combination of parameters (process conditions) for adjusting the control knobs with actual measured values ​​of the process results corresponding to the process conditions. For example, the dataset stored in the dataset storage unit 66 is prepared by having the semiconductor manufacturing equipment 10 execute processes under a plurality of process conditions and collecting data of actual measured values ​​of the process results when the processes are executed according to each process condition.

[0029] The dataset stored in the dataset storage unit 66 includes learning data used by the machine learning model creation unit 50 and evaluation data used by the machine learning model selection unit 52. For example, in a statistical method for evaluating generalization performance called cross-validation, data is divided into k pieces, one of which is used as evaluation data, and the remaining k-1 pieces are used as learning data.

[0030] The machine learning model creation unit 50 uses the learning data of the dataset stored in the dataset storage unit 66 to create machine learning models for a number of regression methods such as linear regression and nonlinear regression.

[0031] The machine learning model selection unit 52 selects one or more machine learning models suitable for the prepared dataset from among the multiple machine learning models created by the machine learning model creation unit 50, by using the evaluation data of the dataset stored in the dataset storage unit 66.

[0032] The calculation unit 54 acquires a target process result set by, for example, an operator. The calculation unit 54 may acquire a target process result stored in the data set storage unit 66 or the like, or may acquire a target process result input by the operator on an input screen or the like.

[0033] The calculation unit 54 performs a multi-objective optimization calculation using one or more machine learning models selected by the machine learning model selection unit 52, and calculates a plurality of process conditions for achieving a target process result. Since there are a plurality of indicators of the target process result, the calculation unit 54 performs multi-objective optimization, and calculates a plurality of process conditions that achieve all the indicators of the target process result, or that are close to the target process result.

[0034] Some machine learning models can calculate the reliability of a predicted value. The calculation unit 54 calculates the calculated process conditions, the predicted value of the process result corresponding to the process conditions, and the reliability of the predicted value. The reliability of a predicted value of a machine learning model that cannot calculate the reliability of a predicted value is calculated using another machine learning model that can calculate the reliability of a predicted value. The reliability of a predicted value is one of the factors for determining which process conditions the semiconductor manufacturing equipment should follow, in other words, for the program to select process conditions.

[0035] The process condition selection unit 56 selects one or more process conditions from the multiple process conditions calculated by the calculation unit 54. For example, the process condition selection unit 56 selects a process condition with good predicted process performance and high reliability from the predicted values ​​of the process results of each of the multiple process conditions calculated by the calculation unit 54 and the reliability of the predicted values. The predicted process performance can be determined from the achievement degree of the predicted value of the process result relative to the target. The reliability of the process condition can be determined from the reliability of the predicted value of the process result.

[0036] The display control unit 58 displays, on an output screen or the like, one or more process conditions selected by the process condition selection unit 56, the achievement level of the predicted value of the process result corresponding to the process condition, and the reliability of the predicted value. Therefore, the operator can confirm one or more process conditions predicted by the machine learning model as being capable of achieving the target process result. The display control unit 58 also displays an input screen or the like for receiving input of the target process result from the operator, and displays the result of determining whether to continue or end the search for process conditions as described below.

[0037] Thereafter, the operator causes semiconductor manufacturing equipment 10 to execute processing in accordance with one or more process conditions selected by process condition selection unit 56. The operator also uses external measuring device 24 to measure actual values ​​of the process results after processing in accordance with the process conditions is executed. The operator provides analysis server 26 with the actual values ​​of the process results measured using external measuring device 24. The actual values ​​of the process results may be provided to analysis server 26 from external measuring device 24 via network 40, or via a recording medium such as a USB (Universal Serial Bus) memory.

[0038] The process result receiving unit 60 receives the actual measured values ​​of the process results corresponding to one or more process conditions. The determination unit 62 compares the target values ​​of the process results for each of the one or more process conditions selected by the process condition selecting unit 56 with the actual measured values ​​of the process results received by the process result receiving unit 60, and determines whether to continue or end the search for process conditions based on the achievement degree of the actual measured values ​​relative to the target values ​​of the process results. The determination unit 62 causes the display control unit 58, for example, to display the result of the determination as to whether to continue or end the search for process conditions.

[0039] The feedback unit 64 adds the process conditions and the actual measured values ​​of the process results corresponding to the process conditions to the dataset stored in the dataset storage unit 66, and feeds back the process results. In this way, the analysis server 26 according to this embodiment feeds back the actual measured values ​​of the process results corresponding to the process conditions, so that the learning function works, and the calculation accuracy from the next time onwards is improved.

[0040] <Processing> In the following, an example will be described in which an operator searches for process conditions that can achieve a target film formation result using a machine learning model. The film formation result is an example of a process result.

[0041] FIG. 4 is a flowchart of an example of processing of the information processing system according to this embodiment. The information processing system 1 according to this embodiment needs to prepare a dataset to be used for machine learning. In step S10, for example, an operator causes the semiconductor manufacturing apparatus 10 to execute a film formation process while changing process conditions, and prepares a dataset by collecting data of actual measured values ​​of the film formation results when the film formation process is executed according to each process condition. In addition, the dataset does not necessarily need to be prepared manually by an operator. For example, the dataset may be prepared by automatically executing a design of experiments. The prepared dataset is stored in the dataset storage unit 66.

[0042] An operator who wishes to start searching for process conditions that can achieve a target film formation result can start the search for process conditions by entering the necessary information on an input screen 1000 such as that shown in FIG. 5 and pressing a calculation execution button 1008.

[0043] Fig. 5 is an image diagram of an example of an input screen. An input screen 1000 in Fig. 5 includes a target value input field 1002, a control knob amplitude limit input field 1004, a display field 1006 displaying a list of models to be used, a calculation execution button 1008, and a film formation result input field 1010.

[0044] The target value input field 1002 is a field for setting an index (target value) for judging the suitability of the target film formation result. The target value of the film formation result may be a plurality of indexes, such as film thickness, in-plane uniformity, refractive index, wet etching rate (WER), etc. The target value input field 1002 may also be configured to set a priority for each target value.

[0045] The control knob amplitude limit input field 1004 is a field for setting the upper and lower amplitude limits of the parameters for adjusting the control knobs (temperature, pressure, gas, etc.) for each control knob. The control knob amplitude limit input field 1004 may also be configured to set the usage priority for each control knob. For example, when N2 gas is used, the upper and lower limits of the flow rate of N2 gas are set.

[0046] A display field 1006 for displaying a list of models to be used displays one or more machine learning models suitable for the prepared data set as a list of machine learning models to be used. The display field 1006 may be configured to accept changes to the list of machine learning models to be used from the operator.

[0047] The execute calculation button 1008 is an example of a start instruction receiving unit that receives an instruction to start searching for process conditions from an operator. When the execute calculation button 1008 is pressed by the operator, the analysis server 26 starts a multi-objective optimization calculation using, for example, the machine learning model displayed in the display field 1006 as the model to be used.

[0048] The film formation result input field 1010 is a field for inputting the actual values ​​of the film formation results measured using the external measuring device 24 after the semiconductor manufacturing equipment 10 performs a film formation process according to one or more process conditions selected by the process condition selection unit 56.

[0049] 4, the machine learning model creation unit 50 of the analysis server 26 creates machine learning models of multiple regression methods such as linear regression and nonlinear regression, using the learning data of the dataset stored in the dataset storage unit 66. In addition, the machine learning model selection unit 52 selects one or more machine learning models suitable for the prepared dataset from the multiple machine learning models created by the machine learning model creation unit 50, by using the evaluation data of the dataset stored in the dataset storage unit 66.

[0050] In step S14, the calculation unit 54 acquires, for example, a target value of the film formation result set in the target value input field 1002. The calculation unit 54 also performs a multi-objective optimization calculation using one or more machine learning models selected by the machine learning model selection unit 52, and calculates, for each process condition, a process condition that achieves the target value of the film formation result, a predicted value of the film formation result corresponding to the process condition, and the reliability of the predicted value.

[0051] In step S16, the process condition selection unit 56 selects one or more process conditions from the plurality of process conditions calculated by the calculation unit 54. The process condition selection unit 56 selects a process condition having good predicted process performance and high reliability based on the predicted values ​​of the film formation results for each of the plurality of process conditions calculated by the calculation unit 54 and the reliability of the predicted values. The process condition selection unit 56 may receive an operation for selecting a process condition from an operator.

[0052] In step S18, the display control unit 58 displays one or more process conditions selected by the process condition selection unit 56, the degree of achievement of the predicted value of the film formation result corresponding to the process condition, and the reliability of the predicted value on, for example, an output screen 1100 as shown in FIG. 6 .

[0053] Fig. 6 is an image diagram of an example of an output screen. An output screen 1100 in Fig. 6 includes a display field 1102 for the results of multi-objective optimization, a display field 1104 for the reliability of prediction, a display field 1106 for the predicted film formation results, and a display field 1108 for a list of proposed process conditions.

[0054] A multi-objective optimization result display field 1102 displays the results of the multi-objective optimization calculation performed by the calculation unit 54 using one or more machine learning models. For example, the multi-objective optimization result display field 1102 displays the results of the multi-objective optimization calculation in the form of a graph as shown in FIG.

[0055] Fig. 7 is a display example of an example of the result of the multi-objective optimization calculation. The display field 1102 of the result of the multi-objective optimization in Fig. 7 is a graph with the horizontal axis representing the degree of achievement of the predicted value for the first target value of the film formation result and the vertical axis representing the degree of achievement of the predicted value for the second target value of the film formation result, and is an example of the process conditions that are the result of the multi-objective optimization calculation. In the graph in Fig. 7, each plot represents a plurality of process conditions that are the result of the multi-objective optimization calculation.

[0056] The prediction reliability display field 1104 displays the reliability of the prediction based on the results of multi-objective optimization calculations performed using one or more machine learning models. For example, the prediction reliability display field 1104 displays the prediction reliability in the form of a graph as shown in FIG.

[0057] Fig. 8 is a display example of the reliability of prediction. A display field 1104 of the reliability of prediction in Fig. 8 is a graph with the horizontal axis representing the achievement level of the predicted value with respect to the first target value of the film formation result and the vertical axis representing the reliability of the predicted value, and is an example showing the achievement level of the predicted value with respect to the target value for each process condition and the reliability of the predicted value. In the graph in Fig. 8, each plot represents a plurality of process conditions that are the results of multi-objective optimization calculation.

[0058] List of suggested process conditions A display field 1108 displays a list of process conditions that are selected from a plurality of process conditions that are the results of the multi-objective optimization calculation and that are proposed for the execution of the film formation process in the semiconductor manufacturing equipment 10. List of suggested process conditions The display field 1108 may receive from the operator a change to the list of process conditions for performing the film formation process in the semiconductor manufacturing equipment 10. List of suggested process conditions A display field 1108 displays predicted values ​​of the film formation results for each of the proposed process conditions.

[0059] Note that the first target value and the second target value displayed in the display field 1102 of the multi-objective optimization result and the display field 1104 of the prediction reliability on the output screen 1100 of Fig. 6 are merely examples, and it is desirable that the operator can select them. The operator refers to the output screen 1100 of Fig. 6 and determines one or more process conditions for the film formation process to be executed by the semiconductor manufacturing equipment 10.

[0060] The operator causes the semiconductor manufacturing equipment 10 to perform a film formation process under one or more determined process conditions. When the film formation process is completed, the operator uses the external measuring device 24 to measure actual values ​​of the film formation results according to each process condition. The operator inputs the actual values ​​of the film formation results measured using the external measuring device 24, for example, into the film formation result input field 1010 in FIG. 5, and provides them to the analysis server 26. The process result receiving unit 60 receives the actual values ​​of the film formation results corresponding to each of the one or more process conditions.

[0061] When the process result receiving unit 60 receives the actual measurement values ​​of the film formation results corresponding to one or more process conditions, the determining unit 62 of the analysis server 26 advances from the process of step S20 to the process of step S22.

[0062] In step S22, the judgment unit 62 compares the target values ​​of the film formation results for each process condition under which the film formation process was performed in the semiconductor manufacturing equipment 10 with the actual measured values ​​of the film formation results, and judges whether to continue or terminate the search for process conditions based on the degree of achievement of the actual measured values ​​of the film formation results relative to the target values.

[0063] If it is determined that the search for process conditions is to be continued, the process proceeds from step S24 to step S26, where the feedback unit 64 adds the process conditions under which the film formation process was performed by the semiconductor manufacturing equipment 10 and the actual measured values ​​of the process results corresponding to those process conditions to the data set stored in the data set storage unit 66, and feeds back the process results. The analysis server 26 returns from the process of step S26 to the process of step S12. If it is determined that the search for process conditions is to be ended, the analysis server 26 ends the process of the flowchart in FIG.

[0064] In the above description, in the process of step S16, an example has been described in which the process conditions are selected based on the predicted values ​​of the film formation results for each of the multiple process conditions calculated by the calculation unit 54 and the reliability of the predicted values, but the present invention is not limited to this example. In the process of step S16, the process conditions may be selected according to the priority of the target values ​​of the film formation results, the priority of use for each control knob, the degree of similarity of the process conditions calculated by each machine learning model, and the like.

[0065] In the process of step S16, the process condition selection unit 56 selects one or more process conditions, for example, as follows, from the plurality of process conditions calculated by the calculation unit 54 through the multi-objective optimization calculation.

[0066] For example, in the process of step S16, consider selecting the process condition that has the largest sum Ji of two objective functions fj(xi) as shown below. The process condition calculated by performing a multi-objective optimization calculation is xi. The objective function is fj(xi). In the example below, if J1>J2>J3 and there is one process condition to be proposed, the process condition selection unit 56 proposes process condition x1 to the operator.

[0067] J1 = f1(x1) + f2(x1) J2 = f1(x2) + f2(x2) J3 = f1(x3) + f2(x3) As described above, when priorities are set for the target values ​​of the film formation results or when priorities for using the control knobs are set, this can be addressed by adding weights to the above formula according to the priorities.

[0068] In addition, in the process of step S12, a plurality of regression methods may be used depending on the purpose. For example, the plurality of regression methods may be used to select a machine learning model according to the linearity or nonlinearity of each control knob. A linear regression model is adopted for the process conditions of the control knob that can be adequately represented by a linear model. A nonlinear model is adopted for the process conditions of the control knob that are highly nonlinear. The machine learning model is selected by the machine learning model selection unit 52, but may also be selected by an operator, for example.

[0069] In addition, in the process of step S12, the nonlinear model may be switched to a local linear regression model. In a region where the amount of data has increased sufficiently, the nonlinear model may be switched to a local linear regression model, thereby enabling optimization using a more accurate machine learning model.

[0070] In this embodiment, an example has been described in which the film formation result includes multiple target values ​​and the process conditions include multiple parameters that adjust multiple control knobs, but the present invention is also applicable to a case in which the film formation result includes a single target value and the process conditions include a parameter that adjusts a single control knob.

[0071] According to this embodiment, since there are a wide variety of objective values ​​for judging whether the process result is good or bad, and the target values ​​of the process results differ for each customer of the semiconductor manufacturing equipment 10, the problem of it taking time to search for process conditions for achieving the target values ​​of the customer's process results can be solved.

[0072] Although the preferred embodiment of the present invention has been described above in detail, the present invention is not limited to the above embodiment, and various modifications and substitutions can be made to the above embodiment without departing from the scope of the present invention. For example, in this embodiment, the heat treatment film forming apparatus has been described as an example, but the present invention can also be applied to batch film forming apparatuses such as CVD (chemical vapor deposition), thermal oxidation, and ALD (atomic layer deposition). [Explanation of symbols]

[0073] 1. Information Processing Systems 10. Semiconductor manufacturing equipment 20 Equipment Controller 22 Host Computer 24 External measuring device 26 Analysis Server 40 Network 50 Machine Learning Model Creation Department 52 Machine Learning Model Selection Section 54 Calculation section 56 Process condition selection section 58 Display control section 60 Process result reception section 62 Judgment section 64 Feedback section 66 Data Set Storage

Claims

1. An information processing device that creates a machine learning model of a semiconductor manufacturing device that executes processing according to process conditions, and searches for the process conditions that can achieve a target process result using the machine learning model, A machine learning model creation means for creating a plurality of machine learning models trained using a plurality of regression methods by using the learning data included in the dataset stored in the dataset storage unit; a machine learning model selection means for selecting, from among the plurality of machine learning models created by the machine learning model creation means, the machine learning model suitable for the dataset stored in the dataset storage unit, using evaluation data included in the dataset stored in the dataset storage unit; and a calculation means for performing an optimization calculation using the selected machine learning model, and calculating a plurality of process conditions for achieving the target process result, a predicted value of the process result corresponding to the process conditions, and a reliability of the predicted value; a process condition selection means for selecting one or more process conditions from a plurality of process conditions that achieve the target process result according to a predicted value of the process result and a reliability of the predicted value; a display control means for displaying the selected process condition, a predicted value of the process result corresponding to the selected process condition, and a reliability of the predicted value; An information processing device having the above configuration.

2. a determination means for determining whether to continue or terminate the search for the process condition based on a comparison result between an actual measurement value of a process result obtained by executing a process by the semiconductor manufacturing equipment according to the selected process condition and a target value of the process result; The information processing apparatus according to claim 1 , further comprising:

3. a feedback means for feeding back the selected process conditions and actual measured values ​​of a process result obtained by executing a process by the semiconductor manufacturing equipment according to the process conditions to the machine learning model selection means; 3. The information processing apparatus according to claim 1, further comprising:

4. The process condition selection means further selects one or more process conditions from the plurality of process conditions that achieve the target process result, based on priorities assigned to a plurality of target values ​​included in the target process result, priorities assigned to a plurality of adjustment targets that are included in the process conditions and are adjusted for achieving the target process result, and a degree of achievement of a predicted value of the process result with respect to the target value.

4. The information processing device according to claim 1, wherein:

5. The display control means displays an input field for inputting priorities to be assigned to the plurality of target values, an input field for inputting priorities to be assigned to the plurality of adjustment targets, and the machine learning model to be used, and displays a screen for accepting a change of the machine learning model to be used.

5. The information processing apparatus according to claim 4,

6. An information processing device that creates a machine learning model of a semiconductor manufacturing device that executes processing according to process conditions, and searches for the process conditions that can achieve a target process result using the machine learning model; A machine learning model creation step of creating a plurality of machine learning models trained using a plurality of regression methods using the learning data included in the dataset stored in the dataset storage unit; a machine learning model selection step of selecting, from among the plurality of machine learning models created in the machine learning model creation step, the machine learning model suitable for the dataset stored in the dataset storage unit, by using evaluation data included in the dataset stored in the dataset storage unit; a calculation step of performing an optimization calculation using the selected machine learning model to calculate a plurality of process conditions for achieving the target process result, a predicted value of the process result corresponding to the process conditions, and a reliability of the predicted value; a process condition selection step of selecting one or more process conditions from the plurality of process conditions that achieve the target process result according to a predicted value of the process result and a confidence level of the predicted value; a display control step of displaying the selected process condition, a predicted value of the process result corresponding to the selected process condition, and a reliability of the predicted value; A program for executing the above.

7. A process condition search method for an information processing device, which creates a machine learning model of a semiconductor manufacturing device that executes processing according to process conditions, and searches for the process conditions that can achieve a target process result using the machine learning model, A machine learning model creation process that uses the learning data included in the dataset stored in the dataset storage unit to create a plurality of machine learning models trained using a plurality of regression methods; a machine learning model selection step of selecting, from among the plurality of machine learning models created in the machine learning model creation step, the machine learning model suitable for the dataset stored in the dataset storage unit by using evaluation data included in the dataset stored in the dataset storage unit; a calculation step of performing an optimization calculation using the selected machine learning model to calculate a plurality of process conditions for achieving the target process result, a predicted value of the process result corresponding to the process conditions, and a reliability of the predicted value; a process condition selection step of selecting one or more process conditions from the plurality of process conditions that achieve the target process result according to a predicted value of the process result and a reliability of the predicted value; a display control step of displaying the selected process condition, a predicted value of the process result corresponding to the selected process condition, and a reliability of the predicted value; The process condition searching method includes:

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