Etching processing apparatus, etching processing system, analysis apparatus, etching processing method, and analysis program

Abstract

To provide a mechanism that generates or adjusts configuration data for each etching processing device.SOLUTION: The etching processing device comprises: a storage unit that stores the trained model of each group having been generated by each training process, when each processing condition acquired during the execution of a specific step of etching process is classified into a plurality of groups and a training process is carried out for each group, in accordance with the difference in effects when the specific step is executed; an update unit that updates the trained model for a specific group, when the effect of having executed the specific step on a wafer for test using configuration data included in a processing condition that corresponds to the specific group is not equivalent to the effect corresponding to the specific group; and a search unit that searches for configuration data, using only the updated trained model, with which the effect corresponding to the specific group is obtained when the specific step is executed on the wafer for test.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an etching processing apparatus, an etching processing system, an analysis apparatus, an etching processing method, and an analysis program. [Background technology]

[0002] Development is underway of a predictive model (trained model) that predicts the cross-sectional shape of a wafer after etching under specified processing conditions, using the cross-sectional shape of the wafer before processing and the specified processing conditions.

[0003] On the other hand, the specifications of the processing space (chamber) of the etching processing apparatus are various, and even if the specifications are the same, there are differences between the apparatuses, and the internal conditions may differ between the apparatuses. Furthermore, the film formation conditions during film formation on the unprocessed wafer are various, and even if the film is the same, the elemental composition, film density, film structure, etc. may be different. For this reason, it is not easy to build a universal prediction model for unprocessed wafers of the same film type, across multiple etching processing apparatuses, and of all elemental compositions, film densities, and film structures. For this reason, when applying the setting data, etc. searched for using the prediction model, it is required to appropriately adjust the setting data, etc., according to the etching processing apparatus to which it is applied and the unprocessed wafer. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2019 / 131608 Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure provides a mechanism for generating or adjusting setting data for each etching processing apparatus. [Means for solving the problem]

[0006] An etching processing apparatus according to an embodiment of the present disclosure has, for example, the following configuration. A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; A search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; and and a setting unit that sets the searched setting data when the specific step is executed for the wafer to be processed. Effect of the Invention

[0007] According to the present disclosure, it is possible to provide a mechanism for generating or adjusting setting data for each etching processing apparatus. [Brief description of the drawings]

[0008] [Figure 1] 1A to 1C are diagrams for explaining an overview of a system configuration and processing in each phase of an etching processing system. [Diagram 2] FIG. 2 illustrates an example of a hardware configuration of an analysis device. [Diagram 3] FIG. 13 is a diagram showing a specific example of a data group that is collected and is a target for analysis. [Figure 4] FIG. 13 is a diagram for explaining an overview of analysis result data. [Diagram 5] FIG. 13 is a diagram showing a specific example of an effect calculation process. [Figure 6]FIG. 2 is a diagram showing a specific example of a plurality of data groups stored in a data storage unit. [Figure 7] FIG. 11 is a diagram showing a specific example of classification processing. [Figure 8] FIG. 13 is a diagram showing a specific example of a generation process of a proxel. [Figure 9] FIG. 11 is a diagram illustrating a specific example of a prediction model generation process. [Figure 10] 11A and 11B are diagrams showing a specific example of a process for collecting cross-sectional shape data of a test wafer. [Figure 11] FIG. 13 is a diagram illustrating a specific example of a prediction model update process. [Figure 12] FIG. 11 is a diagram showing a specific example of a search process for setting data. [Figure 13] FIG. 11 is a diagram showing a specific example of a setting process for setting data. [Figure 14] 13 is an example of a flowchart showing the flow of an analysis process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, each embodiment will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0010] [First embodiment] <System configuration and processing overview of the etching processing system> First, an overview of the system configuration and the process in each phase of the etching processing system according to the first embodiment will be described. Fig. 1 is a diagram for explaining the system configuration and the process in each phase of the etching processing system.

[0011] As shown in FIG. 1, in the “generation phase”, the etching processing system 100 includes a plurality of etching processing devices 110, one or more shape measurement devices 120, and an analysis device .

[0012] The multiple etching processing devices 110 are devices for etching wafers, and may be, for example, multiple etching processing devices located in multiple business establishments, or multiple etching processing devices located in the same business establishment. The multiple etching processing devices 110 may be etching processing devices with different processing space (chamber) specifications, or may be etching processing devices with the same specifications.

[0013] The shape measuring device 120 is a device that measures the cross-sectional shape of a wafer before and after etching processing is performed in each of the multiple etching processing devices 110. The cross-sectional shape data of the wafer before and after processing measured by the shape measuring device 120 is stored in the data storage unit 133 of the analysis device 130 as a data group to be analyzed, in association with a data group acquired during the etching processing.

[0014] A generation program is installed in analysis device 130, and analysis device 130 functions as collection unit 131 and generation unit 132 by executing the program in the generation phase.

[0015] The collection unit 131 of the analysis device 130 collects, as a data group to be analyzed, a data group acquired during etching processing from the multiple etching processing devices 110 and the shape measuring device 120, and corresponding cross-sectional shape data of the unprocessed wafer and cross-sectional shape data of the processed wafer. The collection unit 131 of the analysis device 130 also stores the collected data group to be analyzed in the data storage unit 133.

[0016] Moreover, the collection unit 131 of the analysis device 130 analyzes a group of data to be analyzed that is stored in the data storage unit 133 .

[0017] Specifically, the collection unit 131 of the analysis device 130 analyzes a plurality of data groups of the same step in the etching process among the data groups to be analyzed, and generates a Proxel by grouping data groups that provide similar effects. That is, the collection unit 131 of the analysis device 130 executes an "effect calculation process" for calculating the effect of the etching process, a "classification process" for grouping the data groups, and a "Proxel generation process" for generating a Proxel. The collection unit 131 of the analysis device 130 also stores the generated Proxel in the data storage unit 133.

[0018] In this embodiment, Proxel is a group of processing conditions that provide the same effect in the change in cross-sectional shape before and after processing, and is a concept that represents the smallest data unit in microfabrication in etching processing. However, the "same effect" does not necessarily mean that the change in cross-sectional shape is completely the same, and also includes changes in cross-sectional shape that are about the same (within a specified range).

[0019] The generation unit 132 of the analysis device 130 performs a learning process using the cross-sectional shape data of the unprocessed wafer and the processing conditions, and the cross-sectional shape data of the processed wafer for each generated Proxel, and generates a prediction model (trained model). In other words, the generation unit 132 of the analysis device 130 executes a "prediction model generation process."

[0020] As shown in FIG. 1, in the “update phase”, the etching processing system 100 includes an etching processing device 140 and an analysis device 150.

[0021] The etching processing device 140 is a device that performs an etching process by applying the setting data used to generate the prediction model. In this embodiment, when applying the setting data, Differences in the specifications of the processing space between the multiple etching processing devices 110, -Instrument differences, -Differences in the internal state of the processing space, -Differences in elemental composition, film density, and film structure of the film to be etched, The setting data is adjusted appropriately taking into consideration the above.

[0022] In adjusting the setting data, an in-line measuring device 141 is disposed in the etching processing device 140, and measures the cross-sectional shape of a test wafer before and after the etching processing. The cross-sectional shape data of the test wafer before and after the etching processing measured by the in-line measuring device 141 is processed by the analysis device 150. For the test wafer before processing, a wafer corresponding to the processing wafer to be etched in the etching processing device 140 (a wafer having substantially the same elemental composition, film density, and film structure of the target film to be etched) is used.

[0023] An analysis program is installed in analysis device 150, and analysis device 150 functions as update unit 151 and search unit 152 by executing the analysis program in the update phase.

[0024] The update unit 151 of the analysis device 150 collects cross-sectional shape data of a test unprocessed wafer and cross-sectional shape data of a test processed wafer, and updates the model parameters of the prediction model. That is, the update unit 151 of the analysis device 150 executes a "test wafer cross-sectional shape data collection process" for collecting cross-sectional shape data of the test wafer, and an "prediction model update process" for updating the prediction model.

[0025] Search unit 152 of analysis device 150 uses the updated prediction model in which the model parameters have been updated to search for setting data for obtaining a corresponding effect, thereby adjusting the setting data used to generate the prediction model before the update. In other words, search unit 152 of analysis device 150 executes a "setting data search process" for searching for optimal setting data.

[0026] As shown in FIG. 1, in the “processing phase”, the etching processing system 100 includes an etching processing device 140 and an analysis device 150 .

[0027] The etching processing device 140 performs etching processing of the unprocessed wafer based on the setting data adjusted using the updated prediction model in which the model parameters have been updated. Note that the etching processing device 140 in the processing phase is the same as the etching processing device 140 in the update phase, but the in-line measuring device 141 is removed.

[0028] In the processing phase, the analysis device 150 functions as the setting unit 154. The setting unit 154 of the analysis device 150 sets, in the etching processing device 140, setting data adjusted using the updated prediction model in which the model parameters have been updated. That is, the setting unit 154 of the analysis device 150 executes a "setting data setting process" for setting the adjusted setting data.

[0029] In this manner, according to the etching processing system 100, it is possible to adjust the setting data using a prediction model updated for each etching processing apparatus, and perform the etching processing based on the adjusted setting data.

[0030] In other words, the etching processing system 100 can provide a mechanism for adjusting the setting data for each individual etching processing apparatus.

[0031] <Hardware configuration of the analysis device> Next, a description will be given of the hardware configuration of analysis devices 130 and 150. Fig. 2 is a diagram showing an example of the hardware configuration of an analysis device. Since analysis devices 130 and 150 have the same hardware configuration, the hardware configuration of analysis device 150 will be described here.

[0032] 2, analysis device 150 includes a processor 201, a memory 202, an auxiliary storage device 203, a user interface device 204, a connection device 205, a communication device 206, and a drive device 207. The hardware components of analysis device 150 are connected to each other via a bus 208.

[0033] The processor 201 includes various arithmetic devices such as a central processing unit (CPU), a graphics processing unit (GPU), etc. The processor 201 reads various programs (for example, an analysis program, etc.) onto the memory 202 and executes them.

[0034] The memory 202 has a main storage device such as a Read Only Memory (ROM) and a Random Access Memory (RAM). The processor 201 and the memory 202 form a so-called computer, and the processor 201 executes various programs read onto the memory 202, causing the computer to realize various functions.

[0035] The auxiliary storage device 203 stores various programs and various data used when the processor 201 executes the various programs.

[0036] The user interface device 204 includes, for example, a keyboard or touch panel used when a user of the analysis device 150 performs operations such as inputting various commands, and a display for displaying the processing contents of the analysis device 150 .

[0037] The connection device 205 is a connection device that connects to other devices (such as the etching processing device 140) in the etching processing system 100. The communication device 206 is a communication device for communicating with external devices (not shown) via a network.

[0038] The drive device 207 is a device for setting the 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 memories that record information electrically, such as ROM, flash memory, etc.

[0039] 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 207 and reading out the various programs recorded in the recording medium 210 by the drive device 207. 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 206.

[0040] <Example of processing in the generation phase> Next, a specific example of the process of the etching processing system 100 in the generation phase will be described.

[0041] (1) Specific examples of data sets to be collected and analyzed First, a specific example of a data group to be analyzed that is collected by the collection unit 131 in the generation phase and stored in the data storage unit 133 will be described. FIG. 3 is a diagram showing a specific example of a collected data group to be analyzed. As shown in FIG. 3, the etching process performed by each of the multiple etching processing devices 110 includes multiple steps (step names=steps 1 to N). Note that the "step" here refers to the smallest processing unit that changes the state (attributes of the wafer, state of the etching processing device, atmosphere in the processing space, etc.) in the etching process.

[0042] In FIG. 3, a data group 301_1 is a data group of collected data to be analyzed. An etching process is performed by a predetermined etching process device among the plurality of etching process devices 110, Among the multiple steps included in the etching process, the step with step name="STEP1" It is a data group associated with

[0043] As shown in FIG. 3, data group 301_1 includes, as information items, “cross-sectional shape data of unprocessed wafer (I)”, “setting data (R)”, “output data (E)”, “measurement data (Pl)”, “experimental data (Pr)”, and “cross-sectional shape data of processed wafer (Pf)”.

[0044] "Cross-sectional shape data of unprocessed wafer (I)" includes ·Initial CD(critical dimensions) ·Material Thickness Aspect ratio Mask coverage etc. are included.

[0045] "Setting data (R)" includes ·Pressure (pressure inside the chamber) Power (high frequency power source power) Gas (gas flow rate) ·Temperature (temperature inside the chamber or temperature on the wafer surface) etc. are included.

[0046] "Output data (E)" includes ·Vpp (potential difference) Vdc (DC self-bias voltage) -OES (Optical Emission Spectroscopy) ·Reflect(Reflected wave power) Top DCS current (detected by Doppler current meter) etc. are included.

[0047] "Measurement data (Pl)" includes Plasma density Ion energy Ion flux etc. are included.

[0048] "Experimental Data (Pr)" includes: Etching rate ·Deposition rate ·XY position Film type Vertical / Lateral (vertical / horizontal classification) etc. are included.

[0049] "Cross-sectional shape data of processed wafer (Pf)" includes · CD (critical dimension) Depth ·Taper (taper angle) Tilting ·Bowing etc. are included.

[0050] 3 is an example, and the types of data included in each information item are not limited to those shown in the figure. In addition, the collected data group to be analyzed may include different information items and different types of data for each step.

[0051] (2) Overview of analysis results Next, there will be described an overview of the analysis result data obtained by the collection unit 131 analyzing the data group to be analyzed stored in the data storage unit 133. Fig. 4 is a diagram for explaining the overview of the analysis result data.

[0052] In FIG. 4, data group 301 is a data group of a step with step name="STEP1" of the etching process, and includes a plurality of data groups (data groups 301_1, 301_2, . . . ) collected from the plurality of etching processing devices 110, respectively.

[0053] As described above, the collection unit 131 analyzes a plurality of data groups of the same step, and groups the data groups that provide similar effects. In the case of an etching processing apparatus, even when the same step is performed, different results may be obtained due to differences in the data included in the data groups. Therefore, the collection unit 131 groups the data groups that provide similar effects, and calculates the processing conditions specified by each group, thereby calculating the data range of each group that is allowed to obtain the same effect.

[0054] 4, groups 311 to 314 are groups obtained by grouping data groups that provide similar effects among data group 301. In the same step of the etching process, the processing conditions (data range) identified by the groups that provide similar effects can be said to be the smallest data unit that causes similar changes to the "state" of the wafer in the etching process. In other words, the processing conditions (data range) identified by the groups can be said to be the smallest data unit in the microfabrication in the etching process.

[0055] As described above, the smallest data unit (Process Element) in microfabrication in the etching process is called a "Process Element" in this embodiment.

[0056] The collection unit 131 of the analysis device 130 generates a "Proxel" by analyzing the collected data group of the analysis target, and stores the Proxel in the data storage unit 133 as analysis result data.

[0057] (3) Specific examples of effect calculation processing Next, a description will be given of an effect calculation process when the collection unit 131 analyzes a data group to be analyzed stored in the data storage unit 133 and generates Proxel as analysis result data. Fig. 5 is a diagram showing a specific example of the effect calculation process.

[0058] As shown in FIG. 5, the relationship between a predetermined step (step name="STEP1") of the etching process and data group 301 can be represented diagrammatically as shown by dotted line 500.

[0059] That is, when a specific etching processing device with the setting data executes a specific step of the etching process, the state before the execution (any of the attributes of the wafer before the execution, the state of the specific etching processing device, and the atmosphere in the processing space) changes after the execution. The processing conditions of the etching process at this time can be specified by the output data, the measurement data, and the experimental data.

[0060] In other words, under process conditions specified by the set data, output data, measurement data, and experimental data, the effect of a given step of the etching process is: Data indicating a state before the process (in this embodiment, cross-sectional shape data of the unprocessed wafer), Data indicating the state after the execution (in this embodiment, cross-sectional shape data of the processed wafer), It can be expressed as the difference between

[0061] Therefore, the collection unit 131 calculates the effect under each processing condition in the step by calculating the difference between the cross-sectional shape data of the unprocessed wafer and the cross-sectional shape data of the processed wafer corresponding to each data group for each step. The collection unit 131 also associates the calculated effect with the setting data, output data, measurement data, and experimental data, and stores them in the data storage unit 133 as multiple data groups (including the calculated effect).

[0062] FIG. 6 is a diagram showing a specific example of multiple data groups stored in the data storage unit, and is an example of multiple data groups (including calculated effects) stored in the data storage unit 133 by the collection unit 131 for the step of the etching process with step name="STEP1".

[0063] As shown in FIG. 6, the multiple data groups (including the calculated effects) stored in the data storage unit 133 include the following information items: “data group identifier”, “setting data (R)”, “output data (E)”, “measurement data (Pl)”, “experimental data (Pr)”, and “effect”.

[0064] The "data group identifier" is an identifier for identifying each data group. In FIG. 6, the data group identifier="DATA001" is, for example, a data group including a data group of a predetermined step (step name="STEP1") of an etching process collected from a predetermined etching processing device and its effects. Also, the data group identifier="DATA002" is, for example, a data group including a data group of a predetermined step (step name="STEP1") of an etching process collected from another etching processing device and its effects.

[0065] Each information item from "setting data (R)" to "experimental data (Pr)" stores the data group to be analyzed (see Figure 3) excluding the cross-sectional shape data of the unprocessed wafer (I) and the cross-sectional shape data of the processed wafer (Pf).

[0066] The calculated effect is stored in "Effect." According to the example of FIG. 6, in the case of the etching step with step name "STEP1," under the processing conditions specified by the setting data associated with the data group identifier "DATA 001," "Effect" is <1> Similarly, in the case of the etching step name="STEP1", under the processing conditions specified by the setting data etc. associated with the data group identifier="DATA 002", "Effect <2> " is obtained.

[0067] (4) Specific examples of classification processing Next, a description will be given of a process in which the collection unit 131 classifies a plurality of data groups (including the calculated effects) stored in the data storage unit 133. Fig. 7 is a diagram showing a specific example of the classification process.

[0068] As shown in Fig. 7, a plurality of data groups (including the calculated effects) stored in the data storage unit 133 are plotted in a feature space 700. In Fig. 7, a solid circle with a number written therein indicates one of the plurality of read data groups (including the calculated effects), and the number written within the solid circle indicates the data group identifier of the data group.

[0069] In the example of Figure 7, for the sake of simplicity, the feature space 700 is two-dimensional (i.e., the plot of two types of data (data type p and data type q) contained in the data group is shown).

[0070] 7, the dotted circle surrounding the solid circle indicates a grouping of data groups that provide the same effect. In other words, the data groups identified by the data group identifiers written in the solid circle included in the dotted circle are all data groups that have the same effect in the step name="STEP1" of the etching process.

[0071] For example, dotted circle 701 includes data group identifiers="DATA 001", "DATA 004", and "DATA 010". The solid circles with these data group identifiers are distributed in positions close to each other in feature space 700, but do not completely overlap. In other words, the data groups identified by each data group identifier are similar to each other, but do not completely match.

[0072] On the other hand, these data sets show that the etching process step name="STEP1" was not effective. <1> In other words, among the multiple data groups grouped by the dotted circle 701 in the feature space 700, no matter which data group is used to perform STEP 1 of the etching process, the etching process will be effective. <1> This is the data set that can be obtained.

[0073] 7, dotted circle 702 includes data group identifiers="data 005", "data 006", and "data 007", and dotted circle 703 includes data group identifier="data 002". The data groups included in dotted circle 702 and the data group included in dotted circle 703 are distributed at positions separated from each other.

[0074] On the other hand, the data groups identified by the data group identifiers written in the solid circles included in the dotted circle 702 are all effective when STEP 1 of the etching process is performed based on the respective data groups. <2> Similarly, the data group identified by the data group identifier written in the solid circle included in the dotted circle 703 is effective when STEP 1 of the etching process is performed based on the data group. <2> This is the data set that can be obtained.

[0075] Here, if the dotted circle 702 and the dotted circle 703 are grouped with one dotted circle, they will overlap with another dotted circle 701. For this reason, in this embodiment, even if the same effect is obtained, they are grouped separately (that is, when the feature space is divided, the data groups associated with different effects are grouped so that they do not coexist in the same area).

[0076] In FIG. 7, reference numeral 710 indicates each group, which is diagrammatically shown with a Proxel name attached thereto.

[0077] (5) A concrete example of the Proxel generation process Next, a specific example of the generation process of a Proxel performed by the collection unit 131 will be described. Fig. 8 is a diagram showing a specific example of the generation process of a Proxel.

[0078] As shown in FIG. 8, the minimum and maximum values ​​are calculated for each piece of data included in each data group grouped into the same group, thereby calculating each data range for each region in the feature space.

[0079] The example in Figure 8 shows the effect <1> The data group that can obtain the same effect as above is grouped into a group with group name="Group Gr1". In addition, the example of FIG. 8 shows that, among the data included in the data group grouped into the group with group name="Group Gr1", the setting data "Pressure" is ·min="Pressure_1" ·Maximum="Pressure_4" This shows that it was.

[0080] Specifically, each data range in the region of the feature space in which the data grouped into the group with group name="group Gr1" is distributed can be represented by a dotted line 800. Note that each data range represented by the dotted line 800 is none other than the Proxel (the processing condition specified by the group 311, referred to as "ProxelA") described in FIG.

[0081] In this way, the collection unit 131 can generate Proxel as analysis result data by analyzing a data group to be analyzed.

[0082] (6) Specific example of prediction model generation process Next, a specific example of the generation process of the prediction model performed by the generation unit 132 using the processing conditions specified by Proxel generated as the analysis result data will be described. Fig. 9 is a diagram showing a specific example of the prediction model generation process.

[0083] The generation unit 132 receives from the data storage unit 133, - Cross-sectional shape data of unprocessed wafers, The processing conditions specified by the corresponding Proxel (in the example of Figure 9, the processing conditions specified by "ProxelA"), - Cross-sectional shape data of the processed wafer, Read out.

[0084] Furthermore, the generation unit 132 inputs the read cross-sectional shape data of the unprocessed wafer and the processing conditions specified by "ProxelA" as input data to the prediction model 910 (model name=model α). The generation unit 132 also performs a learning process to update the model parameters of the prediction model 910 so that the output data output from the prediction model 910 approaches the cross-sectional shape data of the processed wafer, which is the correct data. Furthermore, the generation unit 132 stores the learned model (prediction model 910) or model parameters generated by performing the learning process in the data storage unit 133.

[0085] In addition, when the learning process by the generation unit 132 is completed, the following is performed for the prediction model 910: Cross-sectional shape data of an unprocessed wafer that is not used in the learning process; and - Processing conditions specified by "ProxelA"; By inputting the above, when the cross-sectional shape data of the processed wafer inferred by the prediction model 910 is compared with the cross-sectional shape data of the unprocessed wafer inputted, the difference between the two is <1> is equal to

[0086] The effect calculated by the generating unit 132 of the analysis device 130 <1> is stored in the data storage unit 133.

[0087] <Example of processing in the update phase> Next, a specific example of the process of the etching processing system 100 in the renewal phase will be described.

[0088] (1) Specific example of collection process of cross-sectional shape data of test wafer First, a specific example of the process of collecting cross-sectional shape data of a test wafer by the update unit 151 of the analysis device 150 will be described. Fig. 10 is a diagram showing a specific example of the process of collecting cross-sectional shape data of a test wafer. As described above, in the update phase, the in-line measuring device 141 is arranged in the etching processing device 140.

[0089] The in-line measuring device 141 measures the cross-sectional shape of a test unprocessed wafer before the etching processing device 140 performs the etching processing, and outputs cross-sectional shape data of the test unprocessed wafer.

[0090] In addition, the in-line measuring device 141 measures the cross-sectional shape of the processed test wafer after the etching process is performed by the etching processing device 140 under the setting data included in the processing conditions specified by "ProxelA". In addition, the in-line measuring device 141 outputs the measured cross-sectional shape data of the processed test wafer.

[0091] In addition, the output from the in-line measuring device 141 is - Cross-sectional shape data of unprocessed test wafers, Cross-sectional shape data of the processed test wafers, The update unit 151 of the analysis device 150 collects the data, and the effect is calculated based on the difference between the two. <1> At this time, the effect calculated by the update unit 151 of the analysis device 150 is the effect <1> The reason it is not equal to is An etching treatment device 140; A plurality of etching processing devices 110 used in the learning process of the prediction model 910; These differences are due to differences in the specifications of the processing space (chamber), mechanical differences in the equipment, differences in the internal state of the processing space, and differences in the elemental composition, film density, and film structure of the film to be etched.

[0092] The effect calculated by the update unit 151 of the analysis device 150 <1> ' is stored in the data storage unit 153.

[0093] (2) Specific example of update process for predictive model Next, a specific example of the process of updating the prediction model 910 by the update unit 151 of the analysis device 150 will be described. Fig. 11 is a diagram showing a specific example of the process of updating the prediction model.

[0094] The update unit 151 of the analysis device 150 In the generation phase, the effect calculated when the learning process of the prediction model 910 is completed and stored in the data storage unit 133 <1> and, In the update phase, the effect calculated by etching the test unprocessed wafer and stored in the data storage unit 153 <1> 'and, Furthermore, the update unit 151 of the analysis device 150 reads out the prediction model 910 or the model parameters of the prediction model 910 stored in the data storage unit 133. Furthermore, the update unit 151 of the analysis device 150 <1> and Effects <1> Based on the difference with ', the model parameters of the read prediction model 910 are updated to generate a prediction model 1010.

[0095] (3) Example of search process for setting data Next, a specific example of the setting data search process performed by search unit 152 of analysis device 150 will be described. Fig. 12 is a diagram showing a specific example of the setting data search process.

[0096] As shown in Fig. 12, the search unit 152 of the analysis device 150 has a generated prediction model 1010. In addition, the search unit 152 of the analysis device 150 - Cross-sectional shape data of unprocessed test wafers, - Processing conditions specified by "ProxelA"; is input to the prediction model 1010, the predicted value of the cross-sectional shape data of the processed test wafer is obtained, which is output from the prediction model 1010.

[0097] In addition, the search unit 152 of the analysis device 150 calculates the difference between the cross-sectional shape data of the unprocessed test wafer and the predicted value of the cross-sectional shape data of the processed test wafer, thereby effectively <x>Calculate.

[0098] In addition, the search unit 152 of the analysis device 150 calculates the effect <x>But effective <1> The setting data included in the processing conditions input to the prediction model 1010 is adjusted so as to approach the calculated effect. <x>But effective <1> Therefore, the optimal setting data that matches the above can be obtained.

[0099] <Examples of processing in the processing phase> Next, a specific example of the process of the etching process system 100 in the process phase will be described.

[0100] (1) Specific examples of setting data Next, a specific example of the setting process of the setting unit 154 of the analysis device 150 will be described. Fig. 13 is a diagram showing a specific example of the setting process of the setting data. As shown in Fig. 13, the setting unit 154 reads out optimal setting data adjusted by the search unit 152 and sets it in the etching processing device 140.

[0101] As a result, in the etching processing device 140, Differences in the specifications of the processing space between the multiple etching processing devices 110, -Instrument differences, -Differences in the internal state of the processing space, -Differences in elemental composition, film density, and film structure of the film to be etched, The etching process is carried out based on appropriately corrected setting data, and the results are <1> get.

[0102] <Analysis process flow> Next, a description will be given of the flow of the analysis process performed by the etching system 100. Fig. 14 is an example of a flowchart showing the flow of the analysis process.

[0103] As shown in FIG. 14, in step S1401 of the generation phase, analysis device 130 collects a group of data to be analyzed for generating a prediction model.

[0104] In step S1402, analysis device 130 generates a Proxel based on the collected data group to be analyzed, and specifies processing conditions.

[0105] In step S1403, the analysis device 130 performs a learning process using the cross-sectional shape data of the unprocessed wafer, the processing conditions, and the cross-sectional shape data of the processed wafer, and generates a prediction model. The analysis device 130 also uses the generated prediction model to calculate the effect. <1> Calculate.

[0106] Next, in step S1404 of the update phase, the etching processing device 140 performs an etching process on an unprocessed test wafer under the setting data included in the generated processing conditions.

[0107] In step S1405, the analysis device 150 acquires cross-sectional shape data of a test wafer before processing and cross-sectional shape data of a test wafer after processing, which are measured by the in-line measurement device 141 when the etching process is performed, and performs an effective <1> ' is calculated.

[0108] In step S1406, the analysis device 150 calculates the effect <1> and Effects <1> ', and update the model parameters of the prediction model based on the difference.

[0109] In step S1407, analysis device 150 searches for optimal setting data by adjusting the setting data based on the updated prediction model in which the model parameters have been updated.

[0110] In step S1408 of the processing phase, the analysis device 150 sets optimal setting data in the etching processing device 140. As a result, the etching processing device 140 performs etching processing based on the optimal setting data, and effectively <1> get.

[0111] <Summary> As is apparent from the above description, the etching processing system 100 according to the first embodiment has the following features: Depending on the differences in the effects of performing specific steps in the etching process, the process conditions obtained when performing those specific steps are classified into multiple groups, and a learning process is performed for each group to generate a predictive model (trained model) for each group. A wafer corresponding to the wafer to be etched in the etching processing device (a wafer having substantially the same elemental composition, film density, and film structure as the target film to be etched) is used as a test unprocessed wafer. When the effect of executing a specific step on an unprocessed test wafer using setting data included in the processing conditions corresponding to a specific group is not equivalent to the effect corresponding to the specific group, the predictive model for the specific group is updated. The updated prediction model is used to search for configuration data that will produce an effect corresponding to a specific group when a specific step is performed on a test unprocessed wafer. Set the searched setting data when performing a specific step on the wafer for processing.

[0112] In this manner, according to the etching processing system 100 of the first embodiment, it is possible to adjust the setting data using a prediction model updated for each etching processing apparatus, and perform the etching processing based on the adjusted setting data.

[0113] In other words, the etching processing system 100 according to the first embodiment can provide a mechanism for adjusting the setting data for each etching processing apparatus.

[0114] [Second embodiment] In the above first embodiment, a case was described in which when searching for setting data using an updated prediction model, the setting data is adjusted by inputting the processing conditions identified by "ProxelA" into the prediction model as initial values.

[0115] However, the method of searching for the setting data is not limited to this, and the setting data may be searched for from scratch by inputting processing conditions other than the processing conditions specified by "ProxelA" as initial values.

[0116] That is, the etching processing system 100 according to the second embodiment can provide a mechanism for generating setting data for each etching processing apparatus.

[0117] [Third embodiment] In the above first and second embodiments, the case where Proxel is generated for a specific step (step name="STEP1") of the etching process has been described, but Proxel is also generated for other steps of the etching process using a similar method.

[0118] In addition, in the above first and second embodiments, the process of collecting cross-sectional shape data of a test wafer and the process of updating a prediction model were performed for a specific Proxel ("Proxel A"), but the same process shall be performed for all generated Proxels.

[0119] In addition, in the above first and second embodiments, the update unit 151 and the search unit 152 are described as being realized in the analysis device 150, but some of the functions of the analysis device 150 may be realized in the etching processing device 140.

[0120] Furthermore, in the first and second embodiments, the etching processing device 140 and the analysis device 150 are configured as separate devices, but the etching processing device 140 and the analysis device 150 may be configured as an integrated device.

[0121] In the above first and second embodiments, no reference is made to the configuration of the etching processing device 140, but the etching processing device 140 may be configured with a plurality of components (processing space and associated mechanisms such as a transport mechanism). In the case where the etching processing device 140 is configured with a plurality of components, some of the functions of the analysis device 150 may be realized by any of the components in the etching processing device 140.

[0122] In the above first and second embodiments, analysis device 130 and analysis device 150 are configured as separate entities, but analysis device 130 and analysis device 150 may be configured as an integrated entity. In this case, the generation program is included in the analysis program and is executed as a part of the analysis program.

[0123] In the above first and second embodiments, the analysis device 150 has been described as executing the analysis program by itself. However, the analysis device 150 may be configured with, for example, a plurality of computers, and the analysis program may be installed in each of the computers, so that the analysis program is executed in a form of distributed computing.

[0124] In the above first and second embodiments, a method of downloading and installing the analysis program via a network has been mentioned as an example of a method of installing the analysis program in the auxiliary storage device 203 of the analysis device 150. At this time, no particular reference was made to the download source, but when installing by such a method, the download source may be, for example, a server device that stores the analysis program in an accessible manner. Furthermore, the server device may be a device on the cloud that accepts access from the analysis device 150 via a network and downloads the analysis program on the condition that a fee is paid. In other words, the server device may be a device on the cloud that provides a service of providing the analysis program.

[0125] The present invention is not limited to the configurations shown in the above embodiments, combinations with other elements, etc. These points can be changed without departing from the spirit of the present invention, and can be appropriately determined according to the application form.

[0126] For example, embodiments of the present disclosure include the following aspects.

[0127] (Appendix 1) A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; A search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; and a setting unit that sets the searched setting data when the specific step is executed on the wafer for processing; An etching processing apparatus comprising:

[0128] (Appendix 2) The etching processing apparatus described in Appendix 1, wherein the trained model is trained so that output data when cross-sectional shape data of a wafer before the specific step is performed and processing conditions corresponding to the specific group are inputted approaches cross-sectional shape data of a wafer after the specific step is performed.

[0129] (Appendix 3) 3. The etching processing apparatus according to claim 1, wherein the update unit updates model parameters of the trained model of the specific group so that an effect corresponding to the specific group is equivalent to an effect when the specific step is executed on a test wafer using setting data included in processing conditions corresponding to the specific group.

[0130] (Appendix 4) a measuring instrument for measuring cross-sectional shape data of the test wafer before and after the specific step is performed on the test wafer using setting data included in the process conditions corresponding to the specific group; An etching processing apparatus as described in any one of Appendix 1 to Appendix 3, wherein an effect when the specific step is executed on the test wafer using setting data included in processing conditions corresponding to a specific group is calculated based on cross-sectional shape data of the test wafer before the specific step is executed and cross-sectional shape data of the test wafer after the specific step is executed.

[0131] (Appendix 5) The etching processing apparatus according to any one of Appendix 1 to Appendix 4, wherein the search unit searches for setting data so that an effect calculated based on a predicted value of the cross-sectional shape data of the test wafer after processing when the cross-sectional shape data of the test wafer before processing is input to the updated trained model and the cross-sectional shape data of the test wafer before processing approaches an effect associated with the specific group.

[0132] (Appendix 6) The etching processing apparatus according to any one of appendices 1 to 5, wherein the elemental composition, film density, and film structure of the target film to be etched on the test wafer are substantially equal to those of the processing wafer.

[0133] (Appendix 7) A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; A search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; and a setting unit that sets the searched setting data when the specific step is executed on the wafer for processing; An etching processing system comprising:

[0134] (Appendix 8) A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; a search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; An analysis device having the above configuration.

[0135] (Appendix 9) A storage step of storing a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of the etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an updating step of updating the trained model of the specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to the effect corresponding to the specific group; a search step of searching for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; a setting step of setting the searched setting data when the specific step is performed on the wafer for processing; The etching method includes the steps of:

[0136] (Appendix 10) In a case where each processing condition acquired when a specific step of an etching process is executed is classified into a plurality of groups according to a difference in effect when the specific step is executed, and a learning process is performed for each group, a computer of an analysis device having a storage unit for storing a learned model of each group generated by each learning process, an updating step of updating the trained model of the specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to the effect corresponding to the specific group; a search step of searching for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; An analysis program for executing the above. [Explanation of symbols]

[0137] 100: Etching processing system 110: A plurality of etching processing devices 120: Shape measuring device 130:Analysis equipment 131: Collection Department 132 :Generation part 140: Etching treatment device 150:Analysis equipment 151: Update section 152: Search Department 154: Setting section 910: Predictive Model 1010 : Predictive model< / x> < / x> < / x>

Claims

1. A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; A search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; and a setting unit that sets the searched setting data when the specific step is executed on the wafer for processing; An etching processing apparatus comprising:

2. 2. The etching processing apparatus according to claim 1, wherein the trained model is trained so that output data when cross-sectional shape data of a wafer before the specific step is performed and processing conditions corresponding to the specific group are inputted approaches cross-sectional shape data of a wafer after the specific step is performed.

3. 3. The etching processing apparatus according to claim 2, wherein the update unit updates model parameters of the trained model of the specific group so that an effect corresponding to the specific group is equivalent to an effect when the specific step is executed on a test wafer using setting data included in processing conditions corresponding to the specific group.

4. a measuring instrument for measuring cross-sectional shape data of the test wafer before and after the specific step is performed on the test wafer using setting data included in the process conditions corresponding to the specific group; 4. The etching processing apparatus according to claim 3, wherein the effect of executing the specific step on the test wafer using setting data included in processing conditions corresponding to a specific group is calculated based on cross-sectional shape data of the test wafer before the specific step is executed and cross-sectional shape data of the test wafer after the specific step is executed.

5. 2. The etching processing apparatus according to claim 1, wherein the search unit searches for setting data such that an effect calculated based on a predicted value of the cross-sectional shape data of the test wafer after processing when the cross-sectional shape data of the test wafer before processing is input into the updated learned model and the cross-sectional shape data of the test wafer before processing approaches an effect associated with the specific group.

6. 2. The etching processing apparatus according to claim 1, wherein the test wafer has a film to be etched that is substantially equal in element composition, film density, and film structure to the processing wafer.

7. A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; A search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; and a setting unit that sets the searched setting data when the specific step is executed on the wafer for processing; An etching processing system comprising:

8. A storage unit that stores a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of an etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an update unit that updates the trained model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to an effect corresponding to the specific group; a search unit that searches for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; An analysis device having the above configuration.

9. A storage step of storing a trained model for each group generated by each learning process when each processing condition acquired during execution of a specific step of the etching process is classified into a plurality of groups according to differences in effects when the specific step is executed and a learning process is performed for each group; an updating step of updating the trained model of the specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to the effect corresponding to the specific group; a search step of searching for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; a setting step of setting the searched setting data when the specific step is performed on the wafer for processing; The etching method includes the steps of:

10. In a case where each processing condition acquired when a specific step of an etching process is executed is classified into a plurality of groups according to a difference in effect when the specific step is executed, and a learning process is performed for each group, a computer of an analysis device having a storage unit for storing a learned model of each group generated by each learning process, an updating step of updating the trained model of the specific group when an effect of executing the specific step on a test wafer using setting data included in a process condition corresponding to the specific group is not equivalent to the effect corresponding to the specific group; a search step of searching for setting data that provides an effect corresponding to the specific group when the specific step is executed on a test wafer, using the updated trained model; An analysis program for executing the above.

Images