Evaluation device, evaluation method, and evaluation program

Abstract

To evaluate a prediction result of prediction by a shape simulator.SOLUTION: There is provided an evaluation device that has: a storage part which associatively stores a simulation parameter of a shape simulator so calculated that an output when shape information on an object before processing is input to a shape simulator approximates shape information on an object after the processing when the object before the processing is processed under a predetermined condition and first range information representing the same shape range as an object before the processing or second range information representing the same shape range as an object after the processing; a prediction part which inputs the shape information on a new object before processing and the simulation parameter to the shape simulator to predict shape information on the new object before the processing; and an evaluation part which outputs a result of a comparison of the shape information on the new object before the processing or shape information on the new object after the processing with the first or second range information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] In general, in the field of substrate processing equipment, a shape simulator is used to predict the shape of a substrate after processing. A shape simulator is a device that predicts the shape of a substrate after processing when the substrate is processed under predetermined processing conditions.

[0003] When using the shape simulator, a user derives simulation parameters in advance according to predetermined processing conditions from the relationship between the pre-processing shapes of multiple substrates and the post-processing shapes of the multiple substrates when each is processed under the predetermined processing conditions, and then operates the shape simulator using the pre-derived simulation parameters to obtain a predicted result of the post-processing shape of a new substrate when processed under the predetermined processing conditions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-135365 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the case of the above-mentioned shape simulator, if a user uses a new substrate whose shape is significantly different from that of the substrate used to derive the simulation parameters, the accuracy of the prediction results will decrease.

[0006] For this reason, when using the shape simulator, it is necessary to objectively evaluate the prediction results.

[0007] The present disclosure provides an evaluation device, an evaluation method, and an evaluation program for evaluating prediction results predicted by a shape simulator. [Means for solving the problem]

[0008] An evaluation device according to one aspect of the present disclosure has, for example, the following configuration: Simulation parameters of the shape simulator calculated so that an output when shape information of an object before processing is input to the shape simulator approaches shape information of an object after processing when the object before processing is processed under predetermined processing conditions, and first range information indicating a shape range that is considered to have the same shape as the object before processing. and a storage unit that stores the processed object and second range information that indicates a shape range that is considered to have the same shape as the processed object, in association with each other; a prediction unit that predicts shape information of a new object after processing when the new object before processing is processed under the predetermined processing conditions by inputting shape information of the new object before processing and the simulation parameters into the shape simulator; As a result of comparing the shape information of the new object before the processing with the first range information, and and an evaluation unit that outputs a result of comparing the shape information of the new object after the processing with the second range information. [Effects of the Invention]

[0009] It is possible to provide an evaluation device, an evaluation method, and an evaluation program for evaluating the prediction results predicted by a shape simulator. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration in an evaluation data generation phase of a shape simulation system. [Figure 2] FIG. 2 illustrates an example of a hardware configuration of an evaluation data generating device. [Figure 3]FIG. 2 is a diagram illustrating an example of collected data stored in a collected data storage unit. [Figure 4] FIG. 2 illustrates an example of a functional configuration of an evaluation data generating device. [Figure 5] FIG. 10 is a diagram illustrating a specific example of processing by a simulation data generating unit. [Figure 6] FIG. 10 is a diagram showing a specific example of simulation data stored in a simulation data storage unit. [Figure 7] FIG. 10 is a diagram illustrating a specific example of processing by a simulation parameter calculation unit. [Figure 8] 10 is a flowchart showing the flow of an evaluation data generation process. [Figure 9] FIG. 1 is a diagram illustrating an example of a system configuration in an evaluation phase of a shape simulation system. [Figure 10] 10A and 10B are diagrams illustrating specific examples of processing by a simulator control unit and an evaluation unit. [Figure 11] 10 is a flowchart showing the flow of an evaluation process performed by the evaluation device. [Figure 12] FIG. 10 is a diagram illustrating a specific example of evaluation processing. [Figure 13] FIG. 1 is a diagram illustrating an example of a system configuration in a search phase of a shape simulation system. [Figure 14] FIG. 10 is a diagram showing a specific example of evaluation and search processing. [Figure 15] 10 is a flowchart showing the flow of evaluation and search processing by the evaluation device. DETAILED DESCRIPTION OF THE INVENTION

[0011] Each embodiment will be described below with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted. In addition, while the present disclosure uses a substrate processing process for explanation, this is merely an example and is not intended to be limiting.

[0012] [First embodiment] First, an outline of a shape simulation system to which an evaluation device according to the first embodiment is applied will be described. The shape simulation system is a system having a shape simulator, and in the first embodiment, executes processing in an evaluation data generation phase and processing in an evaluation phase.

[0013] The process performed by the shape simulation system in the evaluation data generation phase is as follows: Simulation parameters used when running the shape simulator, Range information indicating a predetermined shape range used when evaluating the prediction results predicted by the shape simulator; This refers to the process of generating evaluation data associated with the

[0014] In addition, the process in the evaluation phase of the shape simulation system is as follows: - Operate the shape simulator for a new board using the simulation parameters included in the generated evaluation data, The prediction results obtained by the shape simulator are evaluated using the range information included in the generated evaluation data. Refers to processing.

[0015] The details of the shape simulation system will be explained below, dividing it into an evaluation data generation phase and an evaluation phase.

[0016] <Configuration of the shape simulation system (evaluation data generation phase)> First, the overall system configuration of the shape simulation system in the evaluation data generation phase will be described. Fig. 1 is a diagram showing an example of the system configuration of the shape simulation system in the evaluation data generation phase.

[0017] As shown in FIG. 1, in the evaluation data generation phase, the shape simulation system 100 includes a substrate processing apparatus 110, measuring apparatuses 111 and 112, an evaluation data generation apparatus 120, and a shape simulator .

[0018] In FIG. 1, a substrate processing apparatus 110 carries out various substrate processing processes (for example, dry etching and deposition) by transporting a plurality of unprocessed substrates (objects).

[0019] Some of the pre-processed substrates among the plurality of pre-processed substrates are transported to a measuring device 111, cut in the cross-sectional direction at various positions, and then the cross-sectional shape is measured by the measuring device 111. As a result, the measuring device 111 generates a pre-processed cross-sectional image showing the cross-sectional shape of the pre-processed substrate. The measuring device 111 includes a scanning electron microscope (SEM), a transmission electron microscope (TEM), an atomic force microscope (AFM), etc.

[0020] The example in FIG. 1 shows how the measuring device 111 generates unprocessed cross-sectional images with file names such as "cross-sectional image LD001," "cross-sectional image LD002," "cross-sectional image LD003," . . .

[0021] On the other hand, after various substrate processing processes are performed, the processed substrate is unloaded from the substrate processing apparatus 110. At this time, the substrate processing apparatus 110 holds processing conditions (process data acquired during the execution of various substrate processing processes, recipe parameters used when executing various substrate processing processes, etc.).

[0022] Some of the processed substrates out of the plurality of processed substrates carried out from the substrate processing apparatus 110 are transported to the measuring apparatus 112, cut in the cross-sectional direction at various positions, and then the cross-sectional shapes are measured by the measuring apparatus 112. As a result, the measuring apparatus 112 generates a post-processing cross-sectional image showing the cross-sectional shape of the processed substrate. Note that, like the measuring apparatus 111, the measuring apparatus 112 includes a scanning electron microscope (SEM), a transmission electron microscope (TEM), an atomic force microscope (AFM), etc.

[0023] The example in FIG. 1 shows how the measuring device 112 generates processed cross-sectional images with file names="cross-sectional image LD001'", "cross-sectional image LD002'", "cross-sectional image LD003'", . . . .

[0024] The pre-processing cross-sectional image generated by the measuring device 111, the process data, recipe parameters, etc. held by the substrate processing apparatus 110, and the post-processing cross-sectional image generated by the measuring device 112 are transmitted as collected data to the evaluation data generating device 120. As a result, the collected data is stored in the collected data storage unit 122 of the evaluation data generating device 120.

[0025] An evaluation data generation program is installed in the evaluation data generation device 120, and the evaluation data generation device 120 functions as an evaluation data generation unit 121 by executing the program.

[0026] The evaluation data generation unit 121 reads out the collected data stored in the collected data storage unit 122, and generates simulation data to be used when operating the shape simulator 130. The evaluation data generation unit 121 also stores the generated simulation data in the simulation data storage unit 123.

[0027] The simulation data includes multiple pairs of pre-processing cross-sectional images and post-processing cross-sectional images included in the collected data, and is managed by classifying them into groups of processing conditions (process data, recipe parameters, etc.) that produce the same effect in the change in cross-sectional shape before and after processing.

[0028] In this embodiment, a group of processing conditions (process data, recipe parameters, etc.) that produce the same effect in the change in cross-sectional shape before and after processing is referred to as a "Proxel," as a concept that represents the smallest data unit in microfabrication in a substrate processing. However, the "same effect" here does not necessarily mean that the change in cross-sectional shape is exactly the same, but refers to a change in cross-sectional shape that is of the same order (within a predetermined range).

[0029] The evaluation data generating unit 121 reads out a plurality of pairs of pre-processing cross-sectional images and post-processing cross-sectional images included in the simulation data of a specific Proxel from the simulation data classified by Proxel.

[0030] Furthermore, the evaluation data generating unit 121 inputs each pre-processing cross-sectional image included in the read-out pairs to the shape simulator 130, thereby acquiring each post-processing predicted cross-sectional image from the shape simulator 130.

[0031] Here, when the evaluation data generating unit 121 operates the shape simulator 130, it repeatedly inputs each pre-processing cross-sectional image to the shape simulator 130 while changing the values of the simulation parameters.

[0032] Then, the evaluation data generating unit 121 changes the values of the simulation parameters so that each post-processing predicted cross-sectional image repeatedly output from the shape simulator 130 approaches each corresponding post-processing cross-sectional image.

[0033] As a result, the evaluation data generating unit 121 can derive optimal simulation parameter values that minimize the sum of the difference values between each post-processing predicted cross-sectional image and each corresponding post-processing cross-sectional image.

[0034] Furthermore, the evaluation data generation unit 121 generates evaluation data and stores it in an evaluation data storage unit 124, which is an example of a storage unit. Specifically, the evaluation data generation unit 121 A simulation parameter set consisting of the derived optimal simulation parameter values; the particular Proxel associated with the simulation data used to derive the optimal simulation parameter values; First range information indicating the range of shape data of the pre-processed cross-sectional image included in the simulation data of the specific Proxel (information indicating the shape range considered to be the same shape), Second range information indicating the range of shape data of the processed cross-sectional image included in the simulation data of the specific Proxel (information indicating the shape range considered to be the same shape), The evaluation data is generated by associating the two data and stored in the evaluation data storage unit 124. Here, the range information may be set using one or more pieces of shape data. For example, the range information may be expressed as a predetermined range based on one or more pieces of shape data.

[0035] The shape simulator 130 operates when the pre-processing cross-sectional image and the values of the simulation parameters are input by the evaluation data generating unit 121, and outputs a post-processing predicted cross-sectional image.

[0036] <Hardware configuration of the evaluation data generation device> Next, a description will be given of the hardware configuration of the evaluation data generation device 120. Fig. 2 is a diagram showing an example of the hardware configuration of the evaluation data generation device.

[0037] 2, the evaluation data generation device 120 includes a processor 201, a memory 202, an auxiliary storage device 203, an I / F (Interface) device 204, a communication device 205, and a drive device 206. The hardware components of the evaluation data generation device 120 are connected to each other via a bus 207.

[0038] 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, an evaluation data generation program, etc.) into the memory 202 and executes them.

[0039] The memory 202 has a main storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), etc. 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.

[0040] The auxiliary storage device 203 stores various programs and various data used when the various programs are executed by the processor 201. The collected data storage unit 122, the simulation data storage unit 123, and the evaluation data storage unit 124 described above are realized in the auxiliary storage device 203.

[0041] The I / F device 204 is a connection device that connects the shape simulator 130, which is an example of an external device, with the evaluation data generation device 120.

[0042] The communication device 205 is a communication device for communicating with the substrate processing apparatus 110, the measuring apparatuses 111 and 112, etc. via a network.

[0043] The drive device 206 is a device for loading a recording medium 210. The recording medium 210 here includes media that record information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, a magneto-optical disk, etc. The recording medium 210 may also include semiconductor memory that records information electrically, such as a ROM, a flash memory, etc.

[0044] The various programs to be installed in the auxiliary storage device 203 are installed, for example, by setting the distributed recording medium 210 in the drive device 206 and reading the various programs recorded on the recording medium 210 by the drive device 206. Alternatively, the various programs to be installed in the auxiliary storage device 203 may be installed by being downloaded from a network via the communication device 205.

[0045] <Examples of collected data> Next, a description will be given of a specific example of collected data stored in the collected data storage unit 122. Fig. 3 is a diagram showing an example of collected data stored in the collected data storage unit.

[0046] As shown in FIG. 3, the collected data 300 includes information items such as "process," "job ID," "cross-sectional image before processing," "process data, recipe parameters, etc.", "Proxel," and "cross-sectional image after processing."

[0047] The "step" field stores a name indicating a substrate treatment process. The example in FIG. 3 shows that "dry etching" is stored as the "step."

[0048] In the "job ID", an identifier for identifying a job executed by the substrate processing apparatus 110 is stored.

[0049] The example in FIG. 3 shows that "PJ001," "PJ002," and "PJ003" are stored as "job IDs" for dry etching.

[0050] "Unprocessed cross-sectional image" stores the file name of the unprocessed cross-sectional image generated by the measuring device 111. The example in Fig. 3 shows that when the job ID is "PJ001", the measuring device 111 generated a unprocessed cross-sectional image with the file name "cross-sectional image LD001" for one unprocessed substrate in the lot (substrate group) of the job.

[0051] 3 shows that, when job ID = "PJ002", a pre-processed cross-sectional image with file name = "cross-sectional image LD002" was generated by measuring device 111 for one pre-processed substrate in the lot (substrate group) of the job. Furthermore, the example of Fig. 3 shows that, when job ID = "PJ003", a pre-processed cross-sectional image with file name = "cross-sectional image LD003" was generated by measuring device 111 for one pre-processed substrate in the lot (substrate group) of the job.

[0052] "Process data, recipe parameters, etc." stores processing conditions (process data, recipe parameters, etc.) held in the substrate processing apparatus 110. In the example of FIG. 3, "process data set 001_1" stores, for example, Data output from the substrate processing apparatus 110 during processing, such as Vpp (potential difference), Vdc (direct current self-bias voltage), OES (light emission intensity by optical emission spectroscopy), Reflect (reflected wave power), Top DCS current (detection value of Doppler current meter), etc. Data measured during processing, such as plasma density, ion energy, ion flux, etc. The process data includes:

[0053] In the example of FIG. 3, for example, "recipe parameter set 001_1" contains Data set as set values in the substrate processing apparatus 110, such as pressure (pressure inside the chamber), power (power of the high frequency power source), gas (gas flow rate), temperature (temperature inside the chamber or temperature on the surface of the substrate), etc. In addition, "Recipe Parameter Set 001_1" and the like contain the following parameters other than recipe parameters: Data set as target values in the substrate processing apparatus 110, such as CD (critical dimension), depth, taper angle, tilt angle, bowing, etc. The target value parameters may be included.

[0054] "Proxel" stores a Proxel name indicating a group into which the process data (included in the process data set) and recipe parameters (included in the recipe parameter set) stored in "process data, recipe parameters, etc." The example in Figure 3 shows that the process data, recipe parameters, etc. corresponding to job IDs = "PJ001" to "PJ003", respectively, are classified into "Proxel_A," "Proxel_B," and "Proxel_C."

[0055] "Post-processing cross-sectional image" stores the file name of the post-processing cross-sectional image generated by measuring device 112. The example in Fig. 3 shows that when job ID = "PJ001", a post-processing cross-sectional image with file name = "cross-sectional image LD001'" was generated by measuring device 112 for one post-processing substrate in the lot (substrate group) of the job.

[0056] 3 shows that, when job ID="PJ002", a post-processing cross-sectional image with file name="cross-sectional image LD002'" is generated by measuring device 112 for one processed substrate in the lot (substrate group) of the job. Furthermore, the example of Fig. 3 shows that, when job ID="PJ003", a post-processing cross-sectional image with file name="cross-sectional image LD003'" is generated by measuring device 111 for one processed substrate in the lot (substrate group) of the job.

[0057] <Functional configuration of the evaluation data generation device> Next, the functional configuration of the evaluation data generation device 120 will be described in detail. Fig. 4 is a diagram showing an example of the functional configuration of the evaluation data generation device. As shown in Fig. 4, the evaluation data generation unit 121 of the evaluation data generation device 120 A simulation data generation unit 410, ·Acquisition unit 420, A simulation parameter calculation unit 430, It has.

[0058] The simulation data generation unit 410 reads out the collected data stored in the collected data storage unit 122, generates simulation data, and then stores the generated simulation data in the simulation data storage unit 123. The simulation data generation unit 410 generates simulation data for each Proxel.

[0059] The acquisition unit 420 reads out, from the simulation data storage unit 123, a plurality of pre-processing cross-sectional images from among a plurality of pairs of pre-processing cross-sectional images and post-processing cross-sectional images included in the simulation data of a specific Proxel.

[0060] The acquisition unit 420 also inputs the read out pre-processing cross-sectional images to the shape simulator 130, thereby operating the shape simulator 130.

[0061] The simulation parameter calculation unit 430 calculates the values of the simulation parameters to be input to the shape simulator 130. The simulation parameter calculation unit 430 first inputs the values of the default simulation parameters to the shape simulator 130.

[0062] Next, the simulation parameter calculation unit 430 receives the pre-processing cross-sectional images from the acquisition unit 420, and acquires the post-processing predicted images output from the shape simulator 130. The simulation parameter calculation unit 430 also reads out the corresponding post-processing cross-sectional images, calculates the difference values between the acquired post-processing predicted cross-sectional images, and changes the values of the simulation parameters so that the sum of the calculated difference values is minimized. The simulation parameter calculation unit 430 then inputs the changed simulation parameter values to the shape simulator 130.

[0063] The simulation parameter calculation unit 430 repeats these processes until the sum of the difference values becomes the smallest, thereby operating the shape simulator 130 multiple times.

[0064] Furthermore, the simulation parameter calculation unit 430 stores the simulation parameter set consisting of the simulation parameter values when the sum of the difference values is minimum in the evaluation data storage unit 124 as the optimal simulation parameter set.

[0065] Furthermore, the simulation parameter calculation unit 430 reads out Proxels associated with the simulation data used to derive the optimal simulation parameter set from the simulation data storage unit 123. Furthermore, the simulation parameter calculation unit 430 stores the read Proxels in the evaluation data storage unit 124 in association with the optimal simulation parameter set.

[0066] The simulation parameter calculation unit 430 also calculates each shape data of the plurality of pre-processing cross-sectional images included in the simulation data used to derive the optimal simulation parameter set. Then, the simulation parameter calculation unit 430 stores first range information indicating the range of each shape data in the evaluation data storage unit 124 in association with the optimal simulation parameter set.

[0067] Furthermore, the simulation parameter calculation unit 430 calculates each shape data of the multiple post-processing cross-sectional images included in the simulation data used to derive the optimal simulation parameter set. Then, the simulation parameter calculation unit 430 stores second range information indicating the range of each shape data in the evaluation data storage unit 124 in association with the optimal simulation parameter set.

[0068] <Specific examples of processing by each part of the evaluation data generation unit> Next, a specific example of the processing of each unit (here, the simulation data generating unit 410 and the simulation parameter calculating unit 430) of the evaluation data generating unit 121 will be described.

[0069] (1) Specific example of the processing of the simulation data generation unit First, a specific example of the processing of the simulation data generating unit 410 will be described. Fig. 5 is a diagram showing a specific example of the processing of the simulation data generating unit.

[0070] As shown in FIG. 5, the simulation data generating unit 410 reads the collected data 300 from the collected data storage unit 122 and classifies the data for each Proxel to generate simulation data.

[0071] In the example of FIG. 5, the simulation data generating unit 410 calculates the following based on the collected data 300: Simulation data 510 (data name = "Simulation data A"), Simulation data 520 (data name = "Simulation data B"), Simulation data 530 (data name = "Simulation data C"), This shows how the above was generated.

[0072] In the example of FIG. 5, the simulation data 510 is simulation data consisting of pairs of pre-processing cross-sectional images and post-processing cross-sectional images included in the collected data 300, which are associated with the Proxel name "Proxel_A".

[0073] Similarly, in the example of FIG. 5, the simulation data 520 is simulation data made up of pairs associated with the Proxel name="Proxel_B" among the multiple pairs included in the collected data 300.

[0074] Similarly, in the example of FIG. 5, the simulation data 530 is simulation data made up of pairs associated with the Proxel name="Proxel_C" among the multiple pairs included in the collected data 300.

[0075] As described above, the evaluation data generation unit 121 derives an optimal simulation parameter set using the same simulation data for each Proxel. The simulation data 510 is used to derive an optimal simulation parameter set with the set name = "parameter set A"; The simulation data 520 is used to derive an optimal simulation parameter set with the set name = "parameter set B"; The simulation data 530 is used to derive an optimal simulation parameter set with set name = "parameter set C"; This shows:

[0076] Next, a specific example of the simulation data will be described. Fig. 6 is a diagram showing a specific example of the simulation data stored in the simulation data storage unit.

[0077] 6, the pre-processing cross-sectional images shown on the left side of the page are pre-processing cross-sectional images with the file names "cross-sectional image LD001," "cross-sectional image LD005," and "cross-sectional image LD006." On the other hand, the post-processing cross-sectional images shown on the right side of the page are post-processing cross-sectional images with the file names "cross-sectional image LD001'," "cross-sectional image LD005'," and "cross-sectional image LD006'."

[0078] (2) Specific example of the processing of the simulation parameter calculation unit Next, a specific example of the processing of the simulation parameter calculation unit 430 will be described. Fig. 7 is a diagram showing a specific example of the processing of the simulation parameter calculation unit. As described above, the processing executed by the simulation parameter calculation unit 430 includes processing for calculating an optimal simulation parameter set and processing for generating evaluation data, but here, a specific example of the processing for generating evaluation data will be described.

[0079] The example in Fig. 7 shows how the simulation parameter calculation unit 430 generates evaluation data 700 (file name = "Evaluation Data A"). As shown in Fig. 7, the evaluation data 700 includes the following information items: "Proxel", "simulation parameter set", "first range information", and "second range information".

[0080] Among these, in "Proxel", for example, Proxel name="Proxel_A" associated with a plurality of pairs included in the simulation data 510 (data name="simulation data A") is stored.

[0081] In addition, in the "simulation parameter set", a simulation parameter set with the set name="parameter set A" derived based on the simulation data 510 is stored.

[0082] Furthermore, the "first range information" stores first range information indicating the range of shape data (e.g., opening width, aspect ratio, surface material, etc.) of each pre-processing cross-sectional image stored in the "pre-processing cross-sectional image" of the simulation data 510. The example of FIG. 7 shows how each shape data is calculated based on each pre-processing cross-sectional image stored in the "pre-processing cross-sectional image" of the simulation data 510. The example of FIG. 7 also shows how first range information (data name="first range information S00A") is calculated based on each calculated shape data.

[0083] Furthermore, the "second range information" stores second range information indicating the range of shape data (e.g., opening width, aspect ratio, surface material, etc.) of each post-processing cross-sectional image stored in the "post-processing cross-sectional image" of the simulation data 510. The example of FIG. 7 shows how each shape data is calculated based on each post-processing cross-sectional image stored in the "post-processing cross-sectional image" of the simulation data 510. The example of FIG. 7 also shows how second range information (data name="second range information S00A'") is calculated based on each calculated shape data.

[0084] <Evaluation data generation process flow> Next, a description will be given of the flow of the evaluation data generation process performed by the evaluation data generation device 120. Fig. 8 is a flowchart showing the flow of the evaluation data generation process.

[0085] In step S801, the evaluation data generating device 120 reads the collected data and generates simulation data.

[0086] In step S802, the evaluation data generating device 120 acquires a plurality of pairs of pre-processing cross-sectional images and post-processing cross-sectional images included in the simulation data of a specific Proxel.

[0087] In step S803, the evaluation data generating device 120 inputs the default simulation parameter values to the shape simulator .

[0088] In step S804, the evaluation data generating device 120 inputs each of the pre-processing cross-sectional images included in the plurality of pairs to the shape simulator .

[0089] In step S805, the evaluation data generating device 120 operates the shape simulator .

[0090] In step S806, the evaluation data generating device 120 acquires each post-processing predicted cross-sectional image from the shape simulator .

[0091] In step S807, the evaluation data generating device 120 calculates each difference value between each post-processing cross-sectional image included in the plurality of pairs and each post-processing predicted cross-sectional image.

[0092] In step S808, the evaluation data generating device 120 determines whether the sum of the difference values has become the smallest.

[0093] In step S808, if the evaluation data generation device 120 determines that the sum of the difference values is not the smallest (No in step S808), the process proceeds to step S809.

[0094] In step S809, the evaluation data generating device 120 changes the values of the simulation parameters, inputs the changed values of the simulation parameters to the shape simulator 130, and then returns to step S804.

[0095] On the other hand, if it is determined in step S808 that the sum of the difference values has become the smallest (Yes in step S808), the process proceeds to step S810.

[0096] In step S810, the evaluation data generating device 120 stores in the evaluation data storage unit 124 a simulation parameter set consisting of optimal simulation parameter values that minimize the sum of the difference values.

[0097] In step S811, the evaluation data generation device 120 calculates each shape data of the pre-processed substrate based on each pre-processed cross-sectional image included in the multiple pairs, and stores first range information indicating the range of each shape data in the evaluation data storage unit 124. In addition, the evaluation data generation device 120 calculates each shape data of the post-processed substrate based on each post-processed cross-sectional image included in the multiple pairs, and stores second range information indicating the range of each shape data in the evaluation data storage unit 124.

[0098] In step S812, the evaluation data generation device 120 generates evaluation data by associating a specific Proxel, a simulation parameter set, the first range information, and the second range information.

[0099] <Configuration of shape simulation system (evaluation phase)> Next, the overall system configuration of the shape simulation system in the evaluation phase will be described below. Fig. 9 is a diagram showing an example of the system configuration of the shape simulation system in the evaluation phase.

[0100] As shown in FIG. 9, in the evaluation phase, a shape simulation system 900 includes a substrate processing apparatus 110, a measuring apparatus 111, an evaluation apparatus 910, and a shape simulator .

[0101] Of these, the substrate processing apparatus 110, the measuring apparatus 111, and the shape simulator 130 have already been explained using FIG. 1, and therefore explanations thereof will be omitted here.

[0102] The example in FIG. 9 shows how the measurement device 111 generates a pre-processing cross-sectional image with the file name "cross-sectional image X."

[0103] An evaluation program is installed in the evaluation device 910, and by executing this program, the evaluation device 910 functions as a data acquisition unit 911, a Proxel input unit 912, a simulator control unit 913, and an evaluation unit 914.

[0104] The data acquisition unit 911 acquires the pre-processing cross-sectional image generated by the measuring device 111 and the processing conditions (process data, recipe parameters, etc.) held by the substrate processing apparatus 110. The data acquisition unit 911 notifies the simulator control unit 913 and the evaluation unit 914 of the acquired pre-processing cross-sectional image. The data acquisition unit 911 also notifies the Proxel input unit 912 of the acquired processing conditions (process data, recipe parameters, etc.).

[0105] The Proxel input unit 912 determines which Proxel the processing conditions (process data, recipe parameters, etc.) notified by the data acquisition unit 911 are classified into, and notifies the simulator control unit 913 of the determined Proxel.

[0106] The simulator control unit 913 is an example of a prediction unit. Based on the Proxel notified by the Proxel input unit 912, the simulator control unit 913 refers to the evaluation data storage unit 124 and reads out a simulation parameter set associated with the notified Proxel. The simulator control unit 913 also inputs the pre-processing cross-sectional image notified by the data acquisition unit 911 and the read-out simulation parameter set into the shape simulator 130, thereby operating the shape simulator 130.

[0107] Furthermore, the simulator control unit 913 operates the shape simulator 130 to acquire the post-processing predicted cross-sectional image output from the shape simulator 130. Furthermore, the simulator control unit 913 notifies the evaluation unit 914 of the acquired post-processing predicted cross-sectional image.

[0108] The evaluation unit 914 calculates shape data of the unprocessed substrate based on the pre-processing cross-sectional image notified by the data acquisition unit 911. The evaluation unit 914 also calculates shape data of the post-processed substrate based on the post-processing predicted cross-sectional image notified by the simulator control unit 913.

[0109] The evaluation unit 914 also references the evaluation data storage unit 124 and determines whether the shape data of the pre-processed substrate calculated based on the pre-processing cross-sectional image is included in the range specified by the first range information. Similarly, the evaluation unit 914 references the evaluation data storage unit 124 and determines whether the shape data of the post-processed substrate calculated based on the post-processing predicted cross-sectional image is included in the range specified by the second range information.

[0110] Furthermore, the evaluation unit 914 determines as a result of the determination that The shape data of the unprocessed substrate calculated based on the unprocessed cross-sectional image is included in the range specified by the first range information, and The shape data of the processed substrate calculated based on the predicted cross-sectional image after processing is included in the range specified by the second range information. In this case, the processed predicted cross-sectional image output from the shape simulator 130 is evaluated as a prediction result with high prediction accuracy.

[0111] On the other hand, the evaluation unit 914 determines as a result of the determination that The shape data of the unprocessed substrate calculated based on the unprocessed cross-sectional image is outside the range specified by the first range information, or When the shape data of the processed substrate calculated based on the predicted cross-sectional image after processing is outside the range specified by the second range information, The post-processing predicted cross-sectional image output from the shape simulator 130 is evaluated as a prediction result with low prediction accuracy.

[0112] If the evaluation unit 914 evaluates that the prediction result has low prediction accuracy, the evaluation device 910, for example, A process for updating the simulation parameter set of the corresponding evaluation data stored in the evaluation data storage unit 124 and a process for updating the first or second range information may be performed. After defining a new Proxel, processing to generate new evaluation data may be performed.

[0113] However, if the evaluation unit 914 evaluates that the prediction result has high prediction accuracy, the evaluation device 910 does not perform these processes.

[0114] <Hardware configuration of evaluation device> Next, we will explain the hardware configuration of the evaluation device 910. Note that the hardware configuration of the evaluation device 910 is similar to the hardware configuration of the evaluation data generation device 120 (see FIG. 2), so here we will explain the differences from the evaluation data generation device 120.

[0115] In the case of the evaluation device 910, the processor 201 reads out and executes the evaluation program on the memory 202. In the case of the evaluation device 910, the auxiliary storage device 203 realizes the evaluation data storage unit .

[0116] In the case of the evaluation device 910, a display device that displays the results of evaluation by the evaluation unit 914 is connected to the I / F device 204. In the case of the evaluation device 910, the communication device 205 communicates with the substrate processing device 110 and the measuring device 112.

[0117] <Specific examples of processing by the simulation control unit and evaluation unit> Next, a specific example of the processing of the simulator control unit 913 and the evaluation unit 914 among the units of the evaluation device 910 will be described. Fig. 10 is a diagram showing a specific example of the processing of the simulator control unit and the evaluation unit.

[0118] In the example of FIG. 10, the simulator control unit 913 The unprocessed substrate on which the unprocessed cross-sectional image with file name = "cross-sectional image X" was generated, When processed under the processing conditions (process data, recipe parameters, etc.) corresponding to Proxel_A, 10 shows a process of predicting a post-processing predicted cross-sectional image.

[0119] In the example of FIG. 10, the evaluation unit 914 The prediction accuracy of the predicted cross-sectional image after processing (file name = "cross-sectional image X'") When evaluation is performed using the evaluation data 700, This shows the processing.

[0120] Specifically, the simulator control unit 913 inputs an unprocessed cross-sectional image with the file name "cross-sectional image X" and a simulation parameter set with the set name "parameter set A" associated with Proxel_A to the shape simulator 130. As a result, the shape simulator 130 outputs a post-processing predicted cross-sectional image with the file name "cross-sectional image X'".

[0121] At this time, the evaluation unit 914 calculates shape data (data name="shape data XS") of the unprocessed substrate based on the pre-processing cross-sectional image (file name="cross-sectional image X"), and calculates shape data (data name="shape data XS'") of the processed substrate based on the predicted post-processing cross-sectional image (file name="cross-sectional image X'").

[0122] The evaluation unit 914 also determines whether or not the shape data of the unprocessed substrate (data name="shape data XS") is included in the range specified by the first range information with the data name="first range information S00A" stored in the evaluation data 700. Furthermore, the evaluation unit 914 determines whether or not the shape data of the processed substrate (data name="shape data XS'") is included in the range specified by the second range information with the data name="second range information S00A'" stored in the evaluation data 700. The example in FIG. 10 is The shape data of the unprocessed substrate (data name = "shape data XS") is included in the range specified by the first range information of data name = "first range information S00A", and The shape data of the processed substrate (data name = "shape data XS'") is included in the range specified by the second range information of data name = "second range information S00A'", As a result, the processed predicted cross-sectional image (file name="cross-sectional image X'") is evaluated as a prediction result with high prediction accuracy.

[0123] <Evaluation process flow> Next, a description will be given of the flow of the evaluation process performed by the evaluation device 910. Fig. 11 is a flowchart showing the flow of the evaluation process.

[0124] In step S1101, the evaluation device 910 determines a Proxel corresponding to the processing conditions, and inputs the simulation parameters associated with the determined Proxel to the shape simulator .

[0125] In step S1102, the evaluation device 910 inputs the pre-processing cross-sectional image to the shape simulator .

[0126] In step S1103, the evaluation device 910 operates the shape simulator 130. As a result, the shape simulator 130 outputs a post-processing predicted cross-sectional image.

[0127] In step S1104, the evaluation unit 910 acquires the post-processing predicted cross-sectional image output by the shape simulator .

[0128] In step S1105, the evaluation device 910 calculates shape data of the unprocessed substrate based on the unprocessed cross-sectional image input to the shape simulator .

[0129] In step S1106, the evaluation device 910 refers to the evaluation data and determines whether the calculated shape data of the unprocessed substrate is within the range specified by the first range information. If it is determined in step S1106 that the shape data is outside the range specified by the first range information (No in step S1106), the process proceeds to step S1110.

[0130] On the other hand, if it is determined in step S1106 that the value is included in the range specified by the first range information (if Yes in step S1106), the process proceeds to step S1107.

[0131] In step S1107, the evaluation device 910 calculates shape data of the processed substrate based on the predicted post-processing cross-sectional image.

[0132] In step S1108, the evaluation device 910 refers to the evaluation data and determines whether the calculated shape data of the processed substrate is included in the range specified by the second range information. If it is determined in step S1108 that the shape data is included in the range specified by the second range information (Yes in step S1108), the process proceeds to step S1109.

[0133] In step S1109, the evaluation unit 910 evaluates that the post-processing predicted cross-sectional image is a prediction result with high prediction accuracy.

[0134] On the other hand, if it is determined in step S1108 that the range specified by the second range information has been exceeded (No in step S1108), the process proceeds to step S1110.

[0135] In step S1110, the evaluation unit 910 evaluates that the post-processing predicted cross-sectional image is a prediction result with low prediction accuracy.

[0136] <Summary> As is clear from the above description, the evaluation device according to the first embodiment The simulation parameters of the shape simulator are calculated so that the output when a pre-processing cross-sectional image of the pre-processing substrate is input to the shape simulator approaches a post-processing cross-sectional image of the processed substrate when the pre-processing substrate is processed under specified processing conditions. First range information is calculated, which indicates a shape range that is considered to be the same shape data as the shape data of the pre-processing cross-sectional image used to calculate the simulation parameters. Second range information is calculated, which indicates a shape range that is considered to be the same shape data as the shape data of the processed cross-sectional image used to calculate the simulation parameters. The calculated simulation parameters, the first range information, and the second range information are associated with each other to generate evaluation data, which is then stored in the evaluation data storage unit. By inputting the pre-processing cross-sectional image of the new pre-processing substrate and the calculated simulation parameters into a shape simulator, a predicted post-processing cross-sectional image of the post-processing substrate when the new pre-processing substrate is processed under the above-mentioned predetermined processing conditions is predicted. The results of comparing the shape data of the pre-processed substrate calculated based on the pre-processing cross-sectional image with the first range information, and the results of comparing the shape data of the post-processed substrate calculated based on the predicted post-processing cross-sectional image with the second range information are output.

[0137] As a result, the evaluation device according to the first embodiment can evaluate whether the post-processing predicted cross-sectional image output by the shape simulator is a prediction result with high prediction accuracy or a prediction result with low prediction accuracy.

[0138] That is, according to the first embodiment, it is possible to evaluate the prediction results obtained by the shape simulator.

[0139] [Second embodiment] In the first embodiment, a case has been described in which the shape simulator 130 is operated once using one simulation parameter set to evaluate the prediction result obtained by the shape simulator. In contrast, in the second embodiment, a case will be described in which the shape simulator 130 is operated multiple times using multiple simulation parameter sets.

[0140] Fig. 12 is a diagram showing a specific example of evaluation processing by the evaluation device. In Fig. 12, reference numeral 1210 indicates how the simulator control unit 913 of the evaluation device 910 operates the shape simulator 130 multiple times.

[0141] Specifically, the diagram shows how the shape simulator 130 is operated three times using a plurality of simulation parameter sets with set names "parameter set A" to "parameter set C."

[0142] 12, a pre-processed cross-sectional image with a file name of "cross-sectional image X" and a simulation parameter set with a set name of "parameter set A" are input. As a result, a post-processed predicted cross-sectional image with a file name of "cross-sectional image X'" is output from the shape simulator 130.

[0143] 12, a post-processing predicted cross-sectional image with the file name "cross-sectional image X'" is input as a pre-processing cross-sectional image together with a simulation parameter set with the set name "parameter set B." As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X''" is output from the shape simulator 130.

[0144] 12, a post-processing predicted cross-sectional image with the file name "cross-sectional image X''" is input as a pre-processing cross-sectional image together with a simulation parameter set with the set name "parameter set C". As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X'''" is output from the shape simulator 130.

[0145] On the other hand, in FIG. 12, reference numeral 1220 indicates how the evaluation unit 914 of the evaluation device 910 evaluates the prediction result.

[0146] Specifically, in the example of reference numeral 1220, when the shape simulator 130 is operated for the first time, the evaluation unit 914 The first pre-processed substrate shape data (data name = "Shape Data XS") calculated based on the pre-processed cross-sectional image with file name = "Cross-sectional Image X", A range specified by the first range information (data name = "first range information S00A") of the evaluation data 700, Contrast this with The shape data (data name = "Shape Data XS'") of the substrate after the first processing calculated based on the predicted cross-sectional image after processing of file name = "Cross-sectional Image X'", A range specified by the second range information (data name = "second range information S00A'") of the evaluation data 700, This shows a comparison of the two.

[0147] In addition, in the example of reference numeral 1220 in FIG. 12, when the shape simulator 130 is operated for the second time, the evaluation unit 914 The second pre-processing substrate shape data (data name = "Shape Data XS'") calculated based on the post-processing predicted cross-sectional image of file name = "Cross-sectional Image X'", A range specified by the first range information (data name = "first range information S00B") of the evaluation data 1230, Contrast this with - The shape data of the substrate after the second processing (data name = "Shape Data XS'') calculated based on the predicted cross-sectional image after processing of file name = "Cross-sectional Image X''); A range specified by the second range information (data name = "second range information S00B'") of the evaluation data 1230, This shows a comparison of the two.

[0148] In addition, in the example of reference numeral 1220 in FIG. 12, when the shape simulator is operated for the third time, the evaluation unit 914 The third pre-processing substrate shape data (data name = "Shape Data XS'') calculated based on the post-processing predicted cross-sectional image of file name = "Cross-sectional Image X''), A range specified by the first range information (data name = "first range information S00C") of the evaluation data 1240, Contrast this with - The shape data of the substrate after the third processing (data name = "Shape Data XS''") calculated based on the predicted cross-sectional image after processing of file name = "Cross-sectional Image X''", and A range specified by the second range information (data name = "second range information S00C'") of the evaluation data 1240, This shows a comparison of the two.

[0149] In this way, when the shape simulator 130 is operated multiple times using multiple simulation parameter sets to obtain a final prediction result, the shape data of the processed substrate calculated based on the nth (n is an integer of 1 or more) predicted cross-sectional image after processing is second range information associated with the simulation parameter set used when the shape simulator was operated the nth time; First range information associated with a simulation parameter set used when the shape simulator is operated for the (n+1)th time; Comparisons will be made using

[0150] As a result, the evaluation unit 914 compares the final post-processing predicted cross-sectional image with the first and second range information each time, and if the final post-processing predicted cross-sectional image is included in the range specified by the first and second range information in each time, the evaluation unit 914 evaluates the final post-processing predicted cross-sectional image as a prediction result with high prediction accuracy.

[0151] Furthermore, the evaluation unit 914 compares the first and second range information each time, and if the result exceeds the range specified by the first or second range information in any of the times, evaluates that the final post-processing predicted cross-sectional image is a prediction result with low prediction accuracy.

[0152] That is, according to the second embodiment, even if the shape simulator is operated multiple times, the prediction results obtained by the shape simulator can be evaluated.

[0153] [Third embodiment] In the second embodiment, a case has been described in which the shape simulator 130 is operated multiple times using a combination of multiple predetermined simulation parameter sets. In contrast, in the third embodiment, a case will be described in which the shape simulator 130 is operated multiple times to ultimately obtain target shape data and to search for a combination of simulation parameter sets that will yield prediction results with high prediction accuracy. The third embodiment will be described below, focusing on the differences from the first and second embodiments.

[0154] <Configuration of shape simulation system (exploration phase)> First, the overall system configuration of the shape simulation system in the search phase will be explained. - Operate the shape simulator multiple times using combinations of simulation parameters included in the generated evaluation data, The prediction result is evaluated using the first range information, the second range information, and the final target shape data, Search for a combination of simulation parameters that will ultimately yield the desired shape data and provide highly accurate prediction results. Refers to the phase.

[0155] 13 is a diagram showing an example of a system configuration of a shape simulation system in the search phase. As shown in FIG. 13, in the search phase, a shape simulation system 1300 includes a substrate processing apparatus 110, a measuring apparatus 111, an evaluation apparatus 1310, and a shape simulator 130.

[0156] Of these, the substrate processing apparatus 110, the measuring apparatus 111, and the shape simulator 130 have already been explained using FIG. 1, and therefore explanations thereof will be omitted here.

[0157] An evaluation program is installed in the evaluation device 1310, and by executing this program, the evaluation device 1310 functions as a data acquisition unit 1311, a combination search unit 1312, a simulator control unit 1313, and an evaluation unit 1314.

[0158] The data acquisition unit 1311 acquires the pre-processing cross-sectional image generated by the measurement device 111 and notifies the simulator control unit 1313 and the evaluation unit 1314 of the image.

[0159] The combination search unit 1312 reads out a plurality of simulation parameter sets from the evaluation data stored in the evaluation data storage unit 124, and generates combinations of the plurality of simulation parameter sets. The combination search unit 1312 also notifies the simulator control unit 1313 in sequence of the plurality of simulation parameter sets included in the generated combinations.

[0160] Furthermore, the combination search unit 1312 sequentially notifies the simulator control unit 1313 of a plurality of simulation parameter sets, and in response, acquires an evaluation result of the prediction result from the evaluation unit 1314. The evaluation results acquired by the combination search unit 1312 include: A first evaluation result indicating whether the prediction result has high prediction accuracy or low prediction accuracy; a second evaluation result indicating whether or not the error between the final shape data of the processed substrate corresponding to the predicted result and the final target shape data is equal to or less than a predetermined threshold value; Includes:

[0161] Furthermore, the combination search unit 1312 generates a new combination of simulation parameters based on the evaluation result obtained by the evaluation unit 1314. Furthermore, the combination search unit 1312 notifies the simulator control unit 1313 in sequence of the multiple simulation parameter sets included in the generated new combination.

[0162] Furthermore, the combination search unit 1312 The first evaluation result was that the prediction results were highly accurate, and A second evaluation result is obtained that the error between the final shape data of the processed substrate corresponding to the predicted result and the final target shape data is equal to or less than a predetermined threshold value. The combination of simulation parameter sets in this case is output as the search result.

[0163] The simulator control unit 1313 is another example of a prediction unit. The simulator control unit 1313 inputs the pre-processing cross-sectional image notified by the data acquisition unit 1311 and the simulation parameter set notified initially by the combination search unit 1312 to the shape simulator 130, thereby operating the shape simulator 130.

[0164] The simulator control unit 1313 also notifies the evaluation unit 1314 of the post-processing predicted cross-sectional image output from the shape simulator 130. The simulator control unit 1313 then inputs the post-processing predicted cross-sectional image as a pre-processing cross-sectional image to the shape simulator 130 together with the simulation parameter set notified by the combination search unit 1312, thereby operating the shape simulator 130. The simulator control unit 1313 repeats the same process for all simulation parameter sets notified by the combination search unit 1312.

[0165] The evaluation unit 1314 calculates shape data of the unprocessed substrate based on the unprocessed cross-sectional image notified by the data acquisition unit 1311. - The calculated shape data of the substrate before processing, a range specified by first range information associated with the first simulation parameter set included in the combination generated by the combination search unit 1312; Contrast with.

[0166] The evaluation unit 1314 also second range information associated with the simulation parameter set used when the shape simulator 130 was operated the nth time; First range information associated with the simulation parameter set used when the shape simulator 130 was operated the (n+1)th time; and Comparisons are made using

[0167] As a result, the evaluation unit 1314 notifies the combination search unit 1312 of the first evaluation result.

[0168] Furthermore, the evaluation unit 1314 determines whether or not the error between the final shape data of the processed substrate corresponding to the predicted result and the final target shape data is equal to or less than a predetermined threshold, and notifies the combination search unit 1312 of the second evaluation result. Note that the final target shape data is assumed to have been input to the evaluation unit 1314 in advance.

[0169] <Specific examples of evaluation and search processes> Next, a specific example of the evaluation and search processing by the evaluation device 1310 will be described. Fig. 14 is a diagram showing a specific example of the evaluation and search processing. In Fig. 14, reference numeral 1410 indicates how the simulator control unit 1313 operates the shape simulator 130 multiple times.

[0170] Specifically, first, the combination search unit 1312 generates a combination (combination 1) of "parameter set A" and "parameter set B" as a combination of multiple simulation parameter sets. Then, the simulator control unit 1313 operates the shape simulator 130 twice using combination 1.

[0171] 14, a pre-processing cross-sectional image with the file name "cross-sectional image X1" and a simulation parameter set with the set name "parameter set A" are input. As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X1'" is output from the shape simulator 130.

[0172] 14, a post-processing predicted cross-sectional image with the file name "cross-sectional image X1'" is input as a pre-processing cross-sectional image together with simulation parameters with the set name "parameter set B." As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X1''" is output from the shape simulator 130.

[0173] On the other hand, in FIG. 14, reference numeral 1420 indicates how the evaluation unit 1314 evaluates the prediction result.

[0174] In the example of the reference numeral 1420 in FIG. 14, when the shape simulator 130 is operated for the first time, the evaluation unit 1314 The shape data of the unprocessed substrate (data name = "shape data XS1") is compared with the range specified by the first range information (data name = "first range information S00A") of the evaluation data 700, The shape data of the processed substrate (data name = "shape data XS1'") is compared with the range specified by the second range information (data name = "second range information S00A'") of the evaluation data 700, This shows the situation.

[0175] In addition, in the example of reference numeral 1420 in FIG. 14, when the shape simulator 130 is operated for the second time, the evaluation unit 1314 The shape data of the processed substrate (data name = "shape data XS1'") is compared with the range specified by the first range information (data name = "first range information S00B") of the evaluation data 1230, The shape data of the processed substrate (data name = "shape data XS1'') is compared with the range specified by the second range information (data name = "second range information S00B'") of the evaluation data 1230, This shows the situation.

[0176] In addition, in the example of reference numeral 1420 in FIG. 14, when the shape simulator 130 is operated for the second time, the evaluation unit 1314 Final processed substrate shape data (data name = "Shape data XS1" and The final target shape data and The figure shows how the error was calculated.

[0177] At this time, it is assumed that the shape data of the processed substrate (data name="shape data XS1'') exceeds the range specified by the second range information (data name="second range information S00B'") of the evaluation data 1230. In this case, when generating a new combination, the combination search unit 1312 imposes a penalty on the simulation parameter set with the set name="parameter set B". As a result, the simulation parameter set with the set name="parameter set B" becomes less likely to be included in the new combination.

[0178] In this way, the combination search unit 1312 The shape data of the substrate after the nth processing exceeds the range specified by the first range information associated with the simulation parameter set used for the (n+1)th operation, which is included in the mth combination (m is an integer equal to or greater than 1), and When the shape data of the substrate after the nth processing is included in the range specified by the second range information associated with the simulation parameter set used for the nth operation included in the mth combination (m is an integer of 1 or more), When generating the (m+1)th combination, the simulation parameter set used in the (n+1)th operation is less likely to be included.

[0179] Alternatively, the combination search unit 1312 performs the following: When the shape data of the substrate after the nth processing exceeds the range specified by the second range information associated with the simulation parameter set used for the nth operation included in the mth combination (m is an integer equal to or greater than 1), When generating the (m+1)th combination, the simulation parameters used in the nth operation are less likely to be included.

[0180] In the example of reference numeral 1430 in FIG. 14, the following new combination is obtained: Set name = "Parameter Set A", Set name = "Parameter Set C", Set name = "Parameter Set D", The example of reference numeral 1430 shows how the shape simulator 130 is operated three times using the new combination ("combination 2") of the simulation parameter sets.

[0181] 14, a pre-processing cross-sectional image with the file name "cross-sectional image X1" and a simulation parameter set with the set name "parameter set A" are input. As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X1'" is output from the shape simulator 130.

[0182] 14, a post-processing predicted cross-sectional image with the file name "cross-sectional image X1'" is input as a pre-processing cross-sectional image together with simulation parameters with the set name "parameter set C." As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X2''" is output from the shape simulator 130.

[0183] 14, a post-processing predicted cross-sectional image with the file name "cross-sectional image X2''" is input as a pre-processing cross-sectional image together with a simulation parameter set with the set name "parameter set D". As a result, a post-processing predicted cross-sectional image with the file name "cross-sectional image X2'''" is output from the shape simulator 130.

[0184] On the other hand, in FIG. 14, reference numeral 1440 indicates how the evaluation unit 1314 evaluates the prediction result.

[0185] In the example of the reference numeral 1440 in FIG. 14, when the shape simulator 130 is operated for the first time, the evaluation unit 1314 The shape data of the unprocessed substrate (data name = "shape data XS1") is compared with the range specified by the first range information (data name = "first range information S00A") of the evaluation data 700, The shape data of the processed substrate (data name = "shape data XS1'") is compared with the range specified by the second range information (data name = "second range information S00A'") of the evaluation data 700, This shows the situation.

[0186] In addition, in the example of the reference numeral 1440 in FIG. 14, when the shape simulator 130 is operated for the second time, the evaluation unit 1314 The shape data of the second unprocessed substrate (data name = "shape data XS1'") is compared with the range specified by the first range information (data name = "first range information S00C") of the evaluation data 1240, The shape data of the substrate after the second processing (data name = "shape data XS2''") is compared with the range specified by the second range information (data name = "second range information S00C'") of the evaluation data 1240, This shows the situation.

[0187] In addition, in the example of reference numeral 1440 in FIG. 14, when the shape simulator 130 is operated for the third time, the evaluation unit 1314 The shape data of the third unprocessed substrate (data name = "shape data XS2'') is compared with the range specified by the first range information (data name = "first range information S00D") of the evaluation data 1450, The shape data of the substrate after the third processing (data name = "shape data XS2'''") is compared with the range specified by the second range information (data name = "second range information S00D'") of the evaluation data 1450, This shows the situation.

[0188] In addition, in the example of reference numeral 1440 in FIG. 14, when the shape simulator 130 is operated for the third time, the evaluation unit 1314 The final processed substrate shape data (data name = "Shape data XS2'''"), The final target shape data and The figure shows how the error was calculated.

[0189] In this way, by repeating the process of generating a new combination based on the evaluation result based on the previous combination and outputting the evaluation result based on the new combination, the combination search unit 1312 can search for the optimal combination.

[0190] <Evaluation and search process flow> Next, we will explain the flow of evaluation and search processing by the evaluation device 1310. Fig. 15 is a flowchart showing the flow of evaluation and search processing by the evaluation device.

[0191] In step S1501, the evaluation device 1310 generates a default combination as a combination of simulation parameter sets.

[0192] In step S1502, the evaluation device 1310 inputs the pre-processing cross-sectional image together with one of the simulation parameter sets included in the current combination to the shape simulator 130. Alternatively, the evaluation device 1310 inputs the post-processing predicted cross-sectional image previously output from the shape simulator 130 as the current pre-processing cross-sectional image together with one of the simulation parameter sets included in the current combination to the shape simulator 130.

[0193] In step S1503, the evaluation device 1310 operates the shape simulator 130. As a result, the shape simulator 130 outputs the current post-processing predicted cross-sectional image.

[0194] In step S1504, the evaluation unit 1310 acquires the post-processing predicted cross-sectional image currently output from the shape simulator .

[0195] In step S1505, it is determined whether all simulation parameter sets included in the current combination have been input to the shape simulator 130. In step S1505, if it is determined that there are simulation parameter sets that have not been input to the shape simulator 130 (No in step S1505), the process returns to step S1502.

[0196] On the other hand, if it is determined in step S1505 that all simulation parameter sets included in the current combination have been input to the shape simulator 130 (Yes in step S1505), the process proceeds to step S1506.

[0197] In step S1506, the evaluation device 1310 calculates shape data of the pre-processed substrate based on the pre-processing cross-sectional image input to the shape simulator 130. The evaluation device 1310 also calculates shape data of the post-processed substrate based on the post-processing predicted cross-sectional image acquired from the shape simulator 130.

[0198] In step S1507, the evaluation device 1310 refers to the evaluation data and compares the calculated shape data of the unprocessed substrate and the processed substrate with the ranges specified by the first and second range information.

[0199] In step S1508, if the evaluation device 1310 determines that the range is included in the range specified by the first and second range information (Yes in step S1508), the process proceeds to step S1510.

[0200] On the other hand, if it is determined in step S1508 that the range specified by the first or second range information is exceeded (No in step S1508), the process proceeds to step S1509.

[0201] In step S1509, evaluation device 1310 imposes a penalty according to the result of the comparison in step S1508 on the corresponding simulation parameter set, and the process proceeds to step S1511.

[0202] In step S1510, the evaluation device 1310 determines whether the error between the shape data of the processed substrate calculated based on the final predicted cross-sectional image after processing and the final target shape data is equal to or less than a predetermined threshold value. If it is determined in step S1510 that the error is not equal to or less than the predetermined threshold value (No in step S1510), the process proceeds to step S1511.

[0203] In step S1511, the evaluation device 1310 generates a new combination of simulation parameter sets, and then returns to step S1502.

[0204] On the other hand, if it is determined in step S1511 that the difference is equal to or less than the predetermined threshold, the process proceeds to step S1512.

[0205] In step S1512, the evaluation device 1310 outputs the current combination of simulation parameter sets as the optimal combination of simulation parameter sets.

[0206] <Summary> As is clear from the above description, the evaluation device according to the third embodiment By running the shape simulator 130 multiple times and evaluating the prediction results, the target shape data is finally obtained, and an optimal combination of simulation parameter sets that will yield highly accurate prediction results is searched for.

[0207] As a result, the evaluation device according to the third embodiment can generate an optimal combination of simulation parameter sets.

[0208] [Other embodiments] In the above embodiments, a pre-processing cross-sectional image is input to the shape simulator 130 as shape information indicating the shape of the substrate. However, the shape information indicating the shape of the substrate input to the shape simulator 130 is not limited to a pre-processing cross-sectional image. For example, it may be data other than an image (e.g., shape data calculated based on a pre-processing cross-sectional image). Or it may be an image other than a cross-sectional image (e.g., a three-dimensional image).

[0209] Although the third embodiment does not mention a method for generating new combinations, the combination search unit 1312 may generate new combinations using, for example, a genetic algorithm.

[0210] In addition, in each of the above embodiments, the evaluation data generation device 120, the evaluation device 910, the evaluation device 1310, and the shape simulator 130 are configured as separate entities, but they may also be configured as an integrated device. In addition, in each of the above embodiments, the evaluation data generation device 120, the evaluation device 910, and the evaluation device 1310 are configured as separate entities, but they may also be configured as an integrated device.

[0211] The present invention is not limited to the configurations described in the above embodiments, but may be combined with other elements, etc. These aspects can be changed without departing from the spirit of the present invention, and can be appropriately determined depending on the application form. [Explanation of symbols]

[0212] 100: Shape simulation system 110: Substrate processing apparatus 111: Measuring equipment 112: Measuring equipment 120: Evaluation data generating device 121: Evaluation data generation unit 130: Shape simulator 300: Collected data 410: Simulation data generation unit 420: Acquisition Department 430: Simulation parameter calculation unit 510~530: Simulation data 700: Evaluation data 900: Shape simulation system 910: Evaluation device 911: Data Acquisition Department 912: Proxel input section 913: Simulator control unit 914: Evaluation Department 1230, 1240: Evaluation data 1310: Evaluation device 1311: Data Acquisition Unit 1312: Combination search section 1313: Simulator control unit

Claims

1. a storage unit that stores, in association with each other, simulation parameters of the shape simulator, which are calculated so that an output when shape information of an object before processing is input to the shape simulator approaches shape information of an object after processing when the object before processing is processed under predetermined processing conditions, first range information indicating a shape range that is considered to have the same shape as the object before processing, and second range information indicating a shape range that is considered to have the same shape as the object after processing; a prediction unit that predicts shape information of a new object after processing when the new object before processing is processed under the predetermined processing conditions by inputting shape information of the new object before processing and the simulation parameters into the shape simulator; an evaluation unit that outputs a result of comparing the shape information of the new object before the processing with the first range information and a result of comparing the shape information of the new object after the processing with the second range information; An evaluation device having the following:

2. The evaluation unit evaluating whether the shape information of the new object after the processing is a prediction result with high prediction accuracy or a prediction result with low prediction accuracy based on a result of comparing the shape information of the new object before the processing with the first range information and a result of comparing the shape information of the new object after the processing with the second range information; The evaluation device according to claim 1 .

3. The evaluation unit 3. The evaluation device according to claim 2, wherein if the shape information of the new object before the processing exceeds the first range information, or if the shape information of the new object after the processing exceeds the second range information, the shape information of the new object after the processing is evaluated as a prediction result with low prediction accuracy.

4. The evaluation unit 3. The evaluation device according to claim 2, wherein, when the shape information of the new object before the processing is included in the first range information and the shape information of the new object after the processing is included in the second range information, the shape information of the new object after the processing is evaluated as a prediction result with high prediction accuracy.

5. The prediction unit 5. The evaluation device according to claim 3 or 4, wherein the process of inputting shape information of the new processed object into the shape simulator is repeated using a plurality of simulation parameters, and the shape simulator is operated a plurality of times to predict the final shape information of the new processed object when the new unprocessed object is processed under a plurality of processing conditions.

6. The evaluation unit 6. The evaluation device according to claim 5, wherein the evaluation device outputs a result of comparing shape information of the new object after the nth processing (n is an integer equal to or greater than 1) predicted by the prediction unit with the second range information associated with the nth simulation parameter, and a result of comparing shape information of the new object after the nth processing predicted by the prediction unit with the first range information associated with the (n+1)th simulation parameter.

7. The evaluation unit 7. The evaluation device according to claim 6, wherein, as a result of comparing the first and second range information each time, if the result exceeds the range specified by the first range information or the range specified by the second range information in either time, the shape information of the new object after final processing is evaluated as a prediction result with low prediction accuracy.

8. The evaluation unit 7. The evaluation device according to claim 6, wherein, as a result of comparing the shape information with the first and second range information in each iteration, if the shape information of the new object after final processing is included in the range specified by the first range information and also included in the range specified by the second range information in both iterations, the evaluation device evaluates the shape information of the new object after final processing as being a prediction result with high prediction accuracy.

9. a generating unit that generates a combination of a plurality of simulation parameters; 7. The evaluation device of claim 6, wherein when the prediction unit searches for combinations by repeating the process of inputting multiple combinations of simulation parameters into the shape simulator, if the shape information of the new object after the nth processing exceeds the first range information associated with the (n+1)th simulation parameter of the mth combination (m is an integer greater than or equal to 1), the generation unit makes it less likely that the (n+1)th simulation parameter will be included when generating the (m+1)th combination.

10. The generation unit The evaluation device according to claim 9 , wherein the (m+1)th combination is generated using a genetic algorithm.

11. 10. The evaluation device according to claim 9, wherein the generation unit imposes a penalty on the (n+1)th simulation parameter, thereby making it less likely that the (n+1)th simulation parameter will be included when generating the (m+1)th combination.

12. An evaluation method for an evaluation device having a storage unit that stores, in association with each other, simulation parameters of a shape simulator calculated so that an output when shape information of an object before processing is input to the shape simulator approaches shape information of an object after processing when the object before processing is processed under predetermined processing conditions, first range information indicating a shape range that is considered to have the same shape as the object before processing and second range information indicating a shape range that is considered to have the same shape as the object after processing, the method comprising: a prediction step of predicting shape information of a new object after processing when the new object before processing is processed under the predetermined processing conditions by inputting shape information of the new object before processing and the simulation parameters into the shape simulator; an output step of outputting a result of comparing the shape information of the new object before the processing with the first range information and a result of comparing the shape information of the new object after the processing with the second range information; An evaluation method having the following characteristics.

13. a computer of an evaluation device having a storage unit that stores, in association with each other, simulation parameters of the shape simulator, which are calculated so that an output when shape information of an object before processing is input to the shape simulator approaches shape information of an object after processing when the object before processing is processed under predetermined processing conditions, first range information indicating a shape range that is considered to have the same shape as the object before processing and second range information indicating a shape range that is considered to have the same shape as the object after processing; a prediction step of predicting shape information of a new object after processing when the new object before processing is processed under the predetermined processing conditions by inputting shape information of the new object before processing and the simulation parameters into the shape simulator; an output step of outputting a result of comparing the shape information of the new object before the processing with the first range information and a result of comparing the shape information of the new object after the processing with the second range information; An evaluation program for executing the above.

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