Prediction program

Abstract

This program provides a prediction method that can efficiently handle complex etching distributions in wet etching. [Solution] The prediction program causes a computer to perform the following: an update process that updates the parameters of a model representing the relationship between process parameters for controlling the operation of multiple nozzles in wet etching of a substrate and the distribution of etching amounts within the plane of the substrate, based on training data; a calculation process that uses the model with updated parameters to calculate the process parameters corresponding to a specified distribution of etching amounts; and a provision process that provides the calculated process parameters to an external party.

Description

Technical Field

[0001] This disclosure relates to a prediction program.

Background Art

[0002] Conventionally, in semiconductor processing, a developing apparatus and a single wafer cleaning apparatus that discharge a chemical solution such as a developing solution onto a rotating substrate are known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] This disclosure provides an etching control system and an etching control method that can efficiently realize a complex etching amount distribution in wet etching.

Means for Solving the Problems

[0005] The prediction program according to the embodiment causes a computer to execute an update process for updating parameters of a model representing a relationship between process parameters for controlling operations of a plurality of nozzles and a distribution of an etching amount in the plane of a substrate based on training data in wet etching of the substrate, a calculation process for calculating process parameters corresponding to a specified etching amount distribution using the model with updated parameters, and a provision process for providing the calculated process parameters to the outside.

[0006] According to this disclosure, a complex etching amount distribution in wet etching can be efficiently realized.

Brief Description of the Drawings

[0007] [Figure 1] Figure 1 shows an example of the configuration of an etching control system. [Figure 2] Figure 2 illustrates the distribution of etching amounts. [Figure 3] Figure 3 is a diagram illustrating the dual dispensing process. [Figure 4] Figure 4 shows an example of the configuration of the prediction device. [Figure 5] Figure 5 shows an example of the configuration of an etching control device. [Figure 6] Figure 6 illustrates how to update a process recipe. [Figure 7] Figure 7 is a flowchart showing the processing flow of the etching control system. [Figure 8] Figure 8 is a flowchart showing the flow of the model update process and the process parameter prediction process. [Figure 9] Figure 9 is a flowchart showing the flow of the model update process and the process parameter prediction process. [Figure 10] Figure 10 shows an example of a computer running a program. [Modes for carrying out the invention]

[0008] In recent years, with the increasing complexity of semiconductor processing, the performance requirements for wet etching control in single-wafer washing equipment have become more diverse. For example, in addition to the conventional uniform etching profile, there are cases where control capabilities are required to correct and cancel out the amount of residual film generated in previous processes to achieve uniformity.

[0009] Another known technique for controlling the wet etching profile is the swing sequence. The swing sequence is a method in which a nozzle that dispenses the chemical solution moves back and forth in the radial direction of a rotating substrate.

[0010] However, in the conventional technology, there is a problem that it is difficult to efficiently achieve a complex etching amount distribution in wet etching.

[0011] Here, the etching amount is the depth of etching. Also, the distribution of the etching amount is the etching amount for each radial position (radius position) of the substrate.

[0012] For example, in the conventional swing sequence, since the nozzle continues to move during the rotation of the substrate (wafer), the operation shown in the process recipe becomes complicated, and it is difficult to meet the need to increase or decrease the etching amount at an arbitrary radial position.

[0013] Note that the process recipe is information that defines the operation of one or more nozzles in the swing sequence.

[0014] Also, for example, in the conventional swing sequence, the method for optimizing the process recipe is not automated, and the optimization of the process recipe is performed based on the engineer's experience and many trials.

[0015] Therefore, a technology that can efficiently achieve a complex etching amount distribution in wet etching is expected.

[0016] Hereinafter, embodiments of an etching control system and an etching control method will be described in detail based on the drawings. Note that the technology disclosed by the following embodiments is not limited.

[0017] In the embodiment, it is assumed that wet etching called a dual dispense process is performed. In the dual dispense process, two nozzles discharge liquid onto a circular substrate.

[0018] The first of the two nozzles discharges a rinse (e.g., water). The second of the two nozzles discharges a chemical solution (e.g., an etching solution). The chemical solution corrodes the substrate. The rinse dilutes the chemical solution and suppresses the degree of substrate corrosion.

[0019] One of the two discharges liquid toward the central portion of the substrate, and the other discharges liquid toward the outer peripheral portion of the substrate. The etching control system creates a distribution of the intended etching amount on the substrate by moving the positions of the two nozzles according to a process recipe.

[0020] In the dual dispense process, controllability can be improved by fixing the position of one nozzle at the central portion of the substrate and moving the position of the other nozzle at the outer peripheral portion. Also, the dual dispense process can be adopted in development, single-sheet cleaning, etc.

[0021] [Configuration of Embodiment] Using FIG. 1, the configuration of the etching control system will be described. FIG. 1 is a diagram showing a configuration example of the etching control system.

[0022] " As shown in FIG. 1, the etching control system 1 includes a prediction device 10 and an etching control device 20.

[0023] The prediction device 10 updates a model representing the relationship between the distribution of the etching amount and the process parameters, and predicts the process parameters using the model. Note that the etching amount may be replaced not only by the decrease in film thickness on a single-layer wafer but also by the etching amount or CD (Critical Dimension) on a device pattern.

[0024] The process parameters are information for defining the operations of the two nozzles in the dual dispense process. For example, a process recipe is generated based on the process parameters.

[0025] As shown in Figure 2, the etching amount distribution represents the etching amount at each location on the substrate. Figure 2 is a diagram illustrating the etching amount distribution. The horizontal axis of Figure 2 represents the distance from the center of the substrate, i.e., the radial position. The vertical axis of Figure 2 represents the etching amount at each radial position.

[0026] The etching amount distribution may be data that actually shows the etching amount at a certain radius position (for example, every 1 mm), or it may be a parameter used to specify the shape of the curve.

[0027] The model can be any model capable of representing the relationship between the etching amount distribution and the process parameters, such as a regression model. Furthermore, the model is not limited to regression models; it may also be a neural network or similar.

[0028] The etching control device 20 controls the etching apparatus based on process parameters. Specifically, the etching control device 20 controls the operation of the two nozzles in the dual dispense process.

[0029] Here, we will explain the dual dispense process using Figure 3. Figure 3 is a diagram illustrating the dual dispense process.

[0030] As shown in Figure 3, the dual dispensing process in this embodiment includes steps S501, S502, S503, S504, S505, S506, S507, and S508. In the dual dispensing process, all of these steps may be performed, or some steps may be omitted. Process parameters define the operation of the nozzle (dispensing position, dispensing time, speed, etc.) in each step.

[0031] Wafer 61 is a circular (disk-shaped) substrate that is etched in a dual-dispense process. Nozzle 62 is a nozzle that dispenses rinse (e.g., water). Nozzle 63 is a nozzle that dispenses chemical solution (e.g., etching solution).

[0032] In step S501 (Type 3 outer), nozzle 62 dispenses rinse into the center of wafer 61, and nozzle 63 dispenses the chemical solution into the outer periphery of wafer 61. Furthermore, for step S501, the dispensing position and dispensing time of nozzle 63 are defined by process parameters.

[0033] In step S502 (Type 3 scan-in), nozzle 62 dispenses rinse into the center of wafer 61, and nozzle 63 dispenses the chemical solution while moving from the outer edge of wafer 61 to the center. In addition, for step S502, the movement speed of nozzle 63 is defined by process parameters.

[0034] In step S503 (Type 3 inner), nozzle 62 discharges rinse to the center of wafer 61, and nozzle 63 discharges the chemical solution to the outer edge of wafer 61 (however, closer to the center than in step S501 (Type 3 outer)). Furthermore, for step S503, the discharge position and discharge time of nozzle 63 are defined by process parameters.

[0035] In step S504 (Type 2 inner), nozzle 63 dispenses the chemical solution into the center of wafer 61, and nozzle 62 dispenses rinse onto the outer periphery of wafer 61. Furthermore, for step S504, the dispensing position and dispensing time of nozzle 62 are defined by process parameters.

[0036] In step S505 (Type 2 scanout), nozzle 63 dispenses the chemical solution into the center of wafer 61, and nozzle 62 dispenses rinse while moving from the center to the outer edge of wafer 61. Furthermore, for step S505, the movement speed of nozzle 62 is defined by process parameters.

[0037] In step S506 (Type 2 outer), nozzle 63 discharges the chemical solution to the center of wafer 61, and nozzle 62 discharges the rinse to the outer edge of wafer 61 (but further from the center than in step S504 (Type 2 inner)). In addition, for step S506, the discharge position and discharge time of nozzle 62 are defined by process parameters.

[0038] In step S507 (Type 1), the nozzle 63 dispenses the chemical solution into the center of the wafer 61. Furthermore, the dispensing time of the nozzle 63 in step S507 is defined by process parameters.

[0039] In step S508 (Type 1), the nozzle 62 dispenses rinse into the center of the wafer 61.

[0040] In the Type 3 recipe, the amount of etching increases in the area where the chemical solution is discharged by nozzle 63, and the longer the discharge time of nozzle 63 (or the slower the movement speed in scanning), the greater the etching compared to other areas. In the Type 2 recipe, the amount of etching decreases in the area where the rinse is discharged by nozzle 62, and the longer the discharge time of nozzle 62 (or the slower the movement speed in scanning), the greater the etching compared to other areas.

[0041] The start and end positions of movement in the scan (S502, S505) are derived from the discharge positions of the preceding and succeeding steps. Furthermore, the discharge time is derived from the movement speed, start position, and end position. Therefore, for the scan, only the movement speed needs to be defined by the process parameters.

[0042] By adjusting the process parameters of each step as explained in Figure 3, the etching control device 20 can achieve various etching shapes.

[0043] Returning to Figure 1, let's explain the processing flow of the etching control system 1. First, the prediction device 10 updates the model using training data (step S1).

[0044] The training data consists of the shape of the etching amount (calibration curve data) obtained by actually measuring the substrate, combined with the process parameters used at that time. Such a combination of data can be called training data in machine learning. The prediction device 10 can update its model using known learning methods.

[0045] Next, the prediction device 10 predicts the process parameters based on the updated model and target profile (step S2). The target profile is the distribution of a specified etching amount. The process parameters corresponding to the target profile are unknown.

[0046] For example, the target profile is an etching amount distribution that cancels out any residual film on the substrate created in the previous process.

[0047] The prediction device 10 inputs the target profile into the updated model and outputs the process parameters. In other words, in step S2, inference processing is performed using the updated model.

[0048] Next, the prediction device 10 outputs process parameters (step S3). Then, the etching control device 20 performs etching based on the process parameters (step S4). At this time, the etching control device 20 reflects the process parameters in the process recipe and performs etching according to the process recipe.

[0049] Furthermore, the etching amount distribution for training data and target profiles may be measured using a spectrophotometer, scatterometry, SEM (Scanning Electron Microscope), etc.

[0050] The configuration of the prediction device 10 will be explained using Figure 4. Figure 4 is a block diagram showing an example of the configuration of the prediction device according to this embodiment.

[0051] As shown in Figure 4, the prediction device 10 includes an I / F (interface) unit 11, a storage unit 12, and a control unit 13.

[0052] The I / F unit 11 is an interface for exchanging data between other devices. For example, the I / F unit 11 is a NIC (Network Interface Card). Furthermore, the I / F unit 11 may be connected to input / output devices such as a mouse, keyboard, display, and speaker.

[0053] The memory unit 12 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or storage devices such as hard disks and optical discs.

[0054] The memory unit 12 stores model information 121. Model information 121 is information for constructing a model. Model information 121 is updated by the prediction device 10. For example, model information 121 may be parameters such as regression coefficients in a regression model.

[0055] The control unit 13 is implemented, for example, by a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), etc., which executes a program stored in an internal memory device using RAM as the working area.

[0056] Furthermore, the control unit 13 may be implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0057] The control unit 13 includes a calculation unit 131, an update unit 132, and a provision unit 133. Note that the internal configuration of the control unit 13 is not limited to the configuration described here; other configurations are also acceptable as long as they perform the information processing described later.

[0058] The calculation unit 131 inputs the etching amount distribution into the model constructed based on the model information 121 and calculates process parameters. The calculation unit 131 can input the etching amount distribution included in the training data, or the etching amount distribution specified as the target profile, into the model.

[0059] The update unit 132 updates the model parameters so that the model representing the relationship between process parameters, which are parameters for controlling the operation of multiple nozzles for etching the substrate, and the distribution of etching amount within the plane of the substrate is optimized.

[0060] Here, the update unit 132 updates the model parameters (model information 121) so that the difference between the etching amount distribution of the training data and the etching amount distribution predicted from the process parameters becomes smaller.

[0061] For example, the update unit 132 can update parameters using the least squares method if the model is a regression model, or use the backpropagation method if the model is a neural network.

[0062] The supply unit 133 provides process parameters calculated by the calculation unit 131. The supply unit 133 may output the process parameters to an output device such as a display and a printer, or it may transmit the process parameters to another device including the etching control device 20.

[0063] The configuration of the etching control device 20 will be explained using Figure 5. Figure 5 is a diagram showing an example of the configuration of the etching control device.

[0064] As shown in Figure 5, the etching control device 20 has an I / F unit 21, a storage unit 22, and a control unit 23.

[0065] The I / F unit 21 is an interface for exchanging data between other devices. For example, the I / F unit 21 is a NIC (Network Interface Card). Furthermore, the I / F unit 21 may be connected to input / output devices such as a mouse, keyboard, display, and speaker.

[0066] The memory unit 22 is implemented by, for example, semiconductor memory elements such as RAM and flash memory, or storage devices such as hard disks and optical discs.

[0067] The memory unit 22 stores the process recipe 221. The process recipe 221 is information that defines the operation of each nozzle in the dual dispensing process.

[0068] The control unit 23 is implemented, for example, by a CPU, MPU, GPU, etc., which executes a program stored in an internal memory device using RAM as the working area.

[0069] Furthermore, the control unit 23 may be implemented by an integrated circuit such as an ASIC or FPGA.

[0070] The control unit 23 includes an acquisition unit 231, an update unit 232, and an operation control unit 233. Note that the internal configuration of the control unit 13 is not limited to the configuration described here; other configurations are also acceptable as long as they perform the information processing described later.

[0071] The acquisition unit 231 acquires process parameters provided by the prediction device 10. The specific method for acquiring process parameters by the acquisition unit 231 will be described later.

[0072] The update unit 232 updates the process recipe 221 based on the process parameters acquired by the acquisition unit 231.

[0073] Figure 6 illustrates how to update process recipe 221.

[0074] As shown in Figure 6, the model outputs the following 11 process parameters. The model then represents the relationship between process parameters, which are parameters for determining the discharge time, discharge position, and travel speed of the first nozzle that discharges rinse onto the rotating substrate and the second nozzle that discharges the chemical solution that etches the substrate, and the distribution of the etching amount. (1) Central discharge (Type 1) time: Related to the discharge time of nozzle 63 in step S507 (Type 1). (2) Type 2 outer circumference discharge position_1: Related to the discharge position of nozzle 62 in step S504 (Type 2 inner). (3) Type 2 outer discharge time_1: Related to the discharge time of both nozzles in step S504 (Type 2 inner). (4) Type 2 Scan speed: Related to the movement speed of the nozzle 62 in step S505 (Type 2 scan out). (5) Type 2 outer circumference discharge position_2: Related to the discharge position of nozzle 62 in step S506 (Type 2 outer). (6) Type 2 outer discharge time_2: Related to the discharge time of both nozzles in step S506 (Type 2 outer). (7) Type 3 outer circumference discharge position_1: Related to the discharge position of nozzle 63 in step S501 (Type 3 outer). (8) Type 3 outer discharge time_1: Related to the discharge time of both nozzles in step S501 (Type 3 outer). (9) Type 3 Scan speed: Related to the movement speed of the nozzle 63 in step S502 (Type 3 scan in). (10) Type 3 outer circumference discharge position_2: Related to the discharge position of nozzle 63 in step S503 (Type 3 inside). (11) Type 3 outer discharge time_2: Related to the discharge time of both nozzles in step S503 (Type 3 inner).

[0075] Process parameters are given names such as discharge position, discharge time, and speed, but the values ​​of these process parameters do not necessarily directly determine the nozzle's discharge position, discharge time, and speed.

[0076] For example, the process parameter "(2) Type2 outer periphery discharge position_1" is related to the discharge position of the nozzle 62 in step S506 (Type2 outer) in Figure 3, but does not necessarily uniquely determine the discharge position of the nozzle 62.

[0077] For example, the etching control device 20 processes the values ​​of each process parameter as appropriate, determines the units, and generates (updates) a process recipe 221. In this case, the etching control device 20 controls the operation of the nozzle according to the process recipe 221.

[0078] The update unit 232 sets values ​​calculated based on process parameters into a pre-prepared template (recipe framework). The recipe framework shows each step of the dual-dispense process explained in Figure 3.

[0079] The update unit 232 updates the process recipe 221 by setting values ​​based on process parameters in a template (recipe framework) that associates the discharge time, discharge position, and travel speed of multiple nozzles with each of the multiple steps included in etching by multiple nozzles.

[0080] The update unit 232 updates the values ​​of the items "Time," "Etching Nozzle Operation," and "Rinse Nozzle Operation" in the process recipe (recipe framework) shown in Figure 6. The step symbols (S501, etc.) used in the item "Step Name" correspond to the step symbols in Figure 3. The etching nozzle is nozzle 62, and the rinse nozzle is nozzle 63.

[0081] One possible approach is for the update unit 232 to directly set the values ​​of each process parameter to the corresponding items in a pre-associated process recipe. For example, the update unit 232 sets the value of the process parameter "Type3 Outer Peripheral Discharge Position_1" (as in (7) above) to the "Etching Nozzle Operation" item in "S501:Type3_1" of the process recipe.

[0082] On the other hand, when actually performing etching, it is necessary to consider the operating times of other parts within the module, and the values ​​of the process parameters may not be directly usable as a process recipe. The update unit 232 updates the process recipe, taking into account operations other than etching (such as driving the mist guard) between etching steps.

[0083] Furthermore, the recipe framework does not need to include all steps S501 to S508 shown in Figure 3. The update unit 232 may insert values ​​calculated based on process parameters into a recipe framework (template) that contains at least some of the steps of the dual dispense process. This makes it possible to disable specific steps and realize various combinations of steps.

[0084] In this way, the update unit 232 updates the process recipe 221 by setting values ​​based on process parameters in a template that associates the discharge time, discharge position, and travel speed of the multiple nozzles with each of the multiple steps included in etching by multiple nozzles. In this case, steps other than the selected steps are disabled.

[0085] The update unit 232 may also disable a specific step by setting the value of the "Time" item in the process recipe to 0.

[0086] The operation control unit 233 controls the operation of multiple nozzles using process parameters acquired by the acquisition unit 231. The operation control unit 233 performs a dual dispense process by operating the nozzles according to the process recipe 221 updated by the update unit 232.

[0087] For example, step S502 in Figure 3 is a scan-in in which the nozzle moves from the outer edge towards the center. Also, in the process recipe "S502:Type3_scan" in Figure 6, "Etching nozzle movement" is set to "50mm (6mm / s)". This means that the nozzle 63 (etching nozzle) moves from a position 50mm from the center of the wafer 61 toward the center of the wafer 61 at a speed of 6mm / s (mm / second) for 5 seconds.

[0088] [Processing of the embodiment] Figure 7 illustrates the processing flow of the etching control system 1. Figure 7 is a flowchart showing the processing flow of the etching control system.

[0089] First, the prediction device 10 accepts training data as input (step S101). Next, the prediction device 10 updates the model using the training data (step S102).

[0090] Next, the prediction device 10 inputs the target profile into the updated model and predicts the process parameters (step S103). The etching control device 20 performs etching based on the process parameters (step S104). Here, the etching control device 20 reflects the process parameters in the process recipe and performs etching according to the process recipe.

[0091] The method for obtaining process parameters using the etching control device 20 is as described in the example.

[0092] Here, we will explain the flow of the model update process and process parameter prediction process by the prediction device 10 using Figures 8 and 9. Figures 8 and 9 are flowcharts showing the flow of the model update process and process parameter prediction process. The process parameters output by the model are assumed to be the 11 parameters mentioned above.

[0093] Figure 8 is a flowchart showing the model update process. First, the prediction device 10 acquires the central etching amount as training data (step S201). Specifically, the prediction device 10 acquires a combination of the central etching amount and the process parameters (actual values) from (1).

[0094] Here, by referring to various etching shapes prepared in advance, the prediction device 10 can update the model using only the etching amount in the central part and the process parameters of (1). If the model is updated using a pre-prepared etching shape, that is, if the shape is not fine-tuned (step S202, No), the prediction device 10 proceeds to step S204.

[0095] On the other hand, if the model is not updated using a pre-prepared etching shape, that is, if the shape is to be fine-tuned (step S202, No), the prediction device 10 proceeds to step S203.

[0096] In shape fitting (step S203), the prediction device 10 updates the model based on the combination of process parameters (actual values) (2), (5), (7), and (10) and the etching amount.

[0097] Furthermore, the prediction device 10 performs outer perimeter discharge position optimization (step S204). Here, the prediction device 10 uses the current model to calculate the process parameters (predicted values) (1) to (11) from the target profile.

[0098] The prediction device 10 then updates the provisional parameter set so that the error between the predicted value calculated in step S204 and the process parameters included in the training data is reduced. The provisional parameter set is a set of provisional parameters determined by the target profile and the process parameters (1) to (11), and is used in steps S206 and S207.

[0099] The prediction device 10 performs discharge time optimization (step S206). Here, the prediction device 10 adjusts the process parameters (2), (4), (5), (7), (9), and (10).

[0100] Furthermore, the prediction device 10 calculates the error between the etching amount distribution derived from the process parameters predicted up to step S206 and the target profile as a model residual (step S207).

[0101] At this point, if the termination condition is met (step S208, Yes), the prediction device 10 outputs the process parameters predicted up to step S206 as the final process parameters and terminates the process. For example, termination conditions include the model residual becoming sufficiently small, or the iteration being performed a certain number of times.

[0102] If the termination condition is not met (step S208, No), the prediction device 10 returns to step S206 and repeats the process.

[0103] In steps S201, S203, and S204, process parameters mainly related to the ejection position are adjusted, and the shape of the etching amount distribution is determined. Furthermore, in step S206, process parameters mainly related to the ejection time are adjusted, and the shape of the etching amount distribution expands and contracts, approaching the target profile.

[0104] The prediction device 10 may perform the update process according to the flow shown in Figure 9. Figure 9 is a flowchart showing the flow of the model update process. In the example in Figure 9, the process branches depending on whether or not fine-tuning of the shape is performed in the first step.

[0105] If the model is updated using a pre-prepared etching shape, that is, if no fine adjustments are made to the shape (step S301, No), the prediction device 10 proceeds to step S304.

[0106] In step S304, during the optimization of the outer edge discharge position, the prediction device 10 uses a model to calculate the process parameters (predicted values) (1) to (11) from the target profile. The prediction device 10 also obtains the central etching amount (step S305).

[0107] On the other hand, if the model is not updated using a pre-prepared etching shape, that is, if the shape is to be fine-tuned (step S301, Yes), the prediction device 10 proceeds to step S302.

[0108] Then, the prediction device 10 performs central etching amount selection and shape fitting (step S302), similar to steps S201 and S203 in Figure 8, and proceeds to step S303.

[0109] Then, in the outer perimeter discharge position optimization step S303, the prediction device 10 uses the model to calculate the process parameters (predicted values) (2) to (11) from the target profile. The prediction device 10 has already obtained the process parameter (1) in step S302.

[0110] Then, the prediction device 10 updates the provisional parameter set (step S306) so that the error between the predicted value calculated in step S304 or S305 and the process parameters included in the training data is reduced. The provisional parameter set is a set of provisional parameters determined by the target profile and the process parameters (1) to (11), and is used in steps S307 and S308.

[0111] The prediction device 10 performs discharge time optimization (step S307). Here, the prediction device 10 adjusts the process parameters (2), (4), (5), (7), (9), and (10).

[0112] Furthermore, the prediction device 10 calculates the error between the etching amount distribution derived from the process parameters predicted up to step S307 and the target profile as a model residual (step S308).

[0113] At this point, if the termination condition is met (step S309, Yes), the prediction device 10 outputs the process parameters predicted up to step S307 as the final process parameters and terminates the process. For example, the termination condition may be that the model residual has become sufficiently small, or that a certain number of iterations have been performed.

[0114] If the termination condition is not met (step S309, No), the prediction device 10 returns to step S206 and repeats the process.

[0115] In steps S301, S302, S303, S304, and S305, process parameters mainly related to the ejection position are adjusted, and the shape of the etching amount distribution is determined. Furthermore, in step S307, process parameters mainly related to the ejection time are adjusted, and the shape of the etching amount distribution expands and contracts, approaching the target profile.

[0116] As described above, the etching control system 1 of the embodiment includes a prediction device 10 and an etching control device 20. The prediction device 10 includes a calculation unit 131. The calculation unit 131 uses a model that represents the relationship between process parameters, which are parameters for controlling the operation of a plurality of nozzles for etching a substrate, and the distribution of etching amounts in the plane of the substrate, to calculate process parameters corresponding to a specified etching amount distribution. The etching control device 20 includes an update unit 232 and an operation control unit 233. The update unit 232 updates a process recipe 221, which is information including the discharge time, discharge position, and travel speed of each of the plurality of nozzles, based on the process parameters. The operation control unit 233 controls the operation of the plurality of nozzles according to the process recipe 221 updated by the update unit 232. As a result, according to the embodiment, a complex etching amount distribution can be efficiently realized in wet etching.

[0117] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The above embodiments may be omitted, replaced, or modified in various forms without departing from the scope and spirit of the appended claims.

[0118] The various processes described in the above embodiments can be achieved by executing a pre-prepared program on a computer. Therefore, below, an example of a computer that executes a program having the same functions as those in the above embodiments will be described. Figure 10 is a diagram showing an example of a computer that executes a program.

[0119] As shown in Figure 10, the computer 1000 includes a computer 1010 that performs various calculations, an input device 1020 that receives data input, and a monitor 1030. The computer 1000 also includes an interface device 1040 for connecting to various devices and a communication device 1050 for connecting to other information processing devices, etc., by wired or wireless connection. The computer 1000 also includes a RAM 1060 for temporarily storing various information and a storage device 1070. Each of the devices 1010 to 1070 is connected to a bus 1080.

[0120] The storage device 1070 stores programs having the same functions as the calculation unit 131, update unit 132, and provision unit 133 shown in Figure 4. The storage device 1070 also stores model information 121. The input device 1020 receives various types of information, such as operation information, from the user of the computer 1000. The monitor 1030 displays various screens, such as display screens, to the user of the computer 1000. The interface device 1040 is connected to, for example, a printing device. The communication device 1050 is connected to, for example, a network (not shown) and exchanges various types of information with other information processing devices.

[0121] Computer 1010 performs various processes by reading each program stored in the storage device 1070, loading it into the RAM 1060, and executing it. These programs also enable computer 1000 to function as the calculation unit 131, update unit 132, and provision unit 133 shown in Figure 4.

[0122] The above program does not necessarily have to be stored in the storage device 1070. For example, the program may be stored on a storage medium that the computer 1000 can read and execute. Examples of storage media that the computer 1000 can read include portable recording media such as CD-ROMs, DVDs (Digital Versatile Discs), and USB (Universal Serial Bus) memory, semiconductor memory such as flash memory, and hard disk drives. Alternatively, the program may be stored on a device connected to a public network, the internet, or a LAN, and the computer 1000 may read and execute the program from there.

[0123] Here, we have described an example of a computer for realizing the prediction device 10, but the etching control device 20 is also realized by a computer with a similar configuration to the one described here. [Explanation of Symbols]

[0124] 1. Etching control system 10 Prediction device 11, 21 I / F section 12, 22 Storage section 13, 23 Control Unit 20 Etching control device 61 wafers 62, 63 nozzles 121 Model Information 131 Calculation Department 132 Update Department 133 Provision Department 221 Process Recipes 231 Acquisition Department 232 Update Department 233 Operation Control Unit

Claims

1. In wet etching of a substrate, an update process is performed to update the parameters of a model that represents the relationship between process parameters for controlling the operation of multiple nozzles and the distribution of etching amount within the plane of the substrate, based on training data. A calculation process that uses the model with updated parameters to calculate the process parameters corresponding to the distribution of a specified etching amount, A provision process that provides the calculated process parameters to an external party, A prediction program characterized by having a computer execute it.

2. The update process optimizes the model parameters using the training data based on the distribution of the process parameters that define the discharge position, discharge time, and movement speed of the plurality of nozzles. The prediction program according to feature 1.

Images