Patent ID: 12253838

DETAILED DESCRIPTION

Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, systems, and components have not been described in detail so as not to unnecessarily obscure aspects of the various embodiments.

System Configuration

FIG.1is a configuration diagram of an example of an information processing system according to an embodiment of the present embodiment. An information processing system1illustrated inFIG.1includes a semiconductor manufacturing device10, a device controller20, a host computer22, an external measuring instrument24, and an analysis server26.

The semiconductor manufacturing device10, the device controller20, the host computer22, the external measuring instrument24, and the analysis server26are communicably connected to one another via a network40such as a local area network (LAN).

The semiconductor manufacturing device10is, for example, a heat treatment device for film formation, and executes a process according to each step of a semiconductor manufacturing process (e.g., film formation, etching, ashing, cleaning, or the like) according to a control command (process condition) output from the device controller20. The process condition is a condition of the semiconductor manufacturing process and is a combination of parameters for controlling (adjusting) control components (control knobs) of the semiconductor manufacturing device10. There are a wide variety of parameter combinations for adjusting the control knobs.

The device controller20is a controller having a computer configuration for controlling the semiconductor manufacturing device10. The device controller20outputs a process condition, which is calculated and selected as will be described later, to the semiconductor manufacturing device10as parameters for adjusting the control knobs of the semiconductor manufacturing device10.

The host computer22is an example of a man-machine interface (MMI) that receives an instruction for the semiconductor manufacturing device10from an operator and provides information about the semiconductor manufacturing device10to the operator by displaying the information or the like.

The external measuring instrument24is a measuring instrument, for example, a film thickness measuring instrument, a sheet resistance measuring instrument, or a particle measuring instrument, that measures a result (a process result) after the execution of the process according to the process condition. The external measuring instrument24measures an actually measured value of an indicator for determining success or failure of the process result. There are a wide variety of indicators for determining success or failure of the process result. For example, the external measuring instrument24measures a film formation state (a film formation result) on a wafer, such as a monitor wafer, as an example of the actually measured value of the process result.

As will be described later, the analysis server26creates a plurality of machine learning models by using a plurality of regression methods, and calculates a process condition that can achieve a target process result by using the created machine learning models. In addition, as will be described later, the analysis server26selects, from a plurality of process conditions that can achieve the target process result, a process condition to be proposed to an operator or the like according to predicted values of a process result corresponding to each process condition, reliability of the predicted values, or the like.

The operator causes the semiconductor manufacturing device10to execute a process according to the process condition selected by the analysis server26. After executing the process according to the process condition, the operator measures an actually measured value of the process result by the external measuring instrument24. The process condition and the process result (the process result corresponding to the process condition) when the semiconductor manufacturing device10is caused to execute the process according to the process condition are fed back to the analysis server26, whereby accuracy of process conditions which will be calculated from a next time onward is improved.

When the actually measured value of the process result reaches a target value of the process result, the operator terminates searching for a process condition that can achieve the target process result. When the actually measured value does not reach the target value of the process result, the operator continues searching for a process condition that can achieve the target process result.

The information processing system1inFIG.1is an exemplary embodiment, and there are various system configuration examples depending on an application or purpose. The classification of devices illustrated inFIG.1, that is, the semiconductor manufacturing device10, the device controller20, the host computer22, the external measuring instrument24, and the analysis server26, is an example.

For example, the information processing system1may have various configurations, such as a configuration in which two or more of the semiconductor manufacturing device10, the device controller20, the host computer22, the external measuring instrument24, and the analysis server26are integrated or a configuration in which the devices are further divided.

Hardware Configuration

The device controller20, the host computer22, and the analysis server26of the information processing system1are implemented by, for example, a computer having a hardware configuration illustrated inFIG.2.FIG.2is a hardware configuration diagram of an example of a computer.

A computer500ofFIG.2includes an input device501, an output device502, an external interface (I/F)503, a random access memory (RAM)504, a read only memory (ROM)505, a central processing unit (CPU)506, and a communication I/F507, a hard disk drive (HDD)508, and the like, which are connected to one another by a bus B. The input device501and the output device502may be connected and used when necessary.

The input device501is a keyboard, mouse, touch panel, or the like and is used by the operator or the like to input respective operation signals. The output device502is a display or the like and displays a process result by the computer500. The communication I/F507is an interface for connecting the computer500to the network40. The HDD508is an example of a nonvolatile storage device that stores a program or data.

The external I/F503is an interface with external devices. The computer500can read and/or write data from and/or into a recording medium503a, such as a secure digital (SD) memory card, via the external I/F503. The ROM505is an example of a nonvolatile semiconductor memory (a storage device) in which a program or data is stored. The RAM504is an example of a volatile semiconductor memory (a storage device) which temporarily holds a program or data.

The CPU506is an arithmetic operation device that controls the entirety of the computer500or implements functions of the computer500by reading a program or data from a storage device such as a ROM505or an HDD508onto the RAM504and executing a process.

The device controller20, the host computer22, and the analysis server26illustrated inFIG.1can implement various functions to be described later by executing a program in the computer500having, for example, the hardware configuration illustrated inFIG.2.

Functional Configuration

The analysis server26of the information processing system1according to the present embodiment is implemented by, for example, functional blocks illustrated inFIG.3.FIG.3is a functional block diagram of an example of an analysis server according to the present embodiment. In the functional block diagram ofFIG.3, illustration of configurations unnecessary for explaining the present embodiment is omitted.

By executing a program for the analysis server26, the analysis server26implements a machine learning model creation part50, a machine learning model selection part52, a calculation part54, a process condition selection part56, a display controller58, a process result receiving part60, a determination part62, a feedback part64, and a data set storage part66.

The data set storage part66stores a data set used by the machine learning model creation part50and the machine learning model selection part52. The data set stored in the data set storage part66associates a combination of parameters (process condition) for adjusting the control knobs with an actually measured value of a process result corresponding to the process condition. For example, the data set stored in the data set storage part66is prepared by causing the semiconductor manufacturing device10to execute a process under a plurality of process conditions and collecting actually measured value of process results as data when the process is executed under the respective process conditions.

The data set stored in the data set storage part66includes data for learning used by the machine learning model creation part50and data for evaluation used by the machine learning model selection part52. For example, in a statistical method of evaluating generalization performance called cross-validation, data is divided into k pieces to use one piece as data for evaluation and the remaining k-1pieces as data for learning.

The machine learning model creation part50creates a plurality of machine learning models of regression methods such as linear regression and non-linear regression by using the data for learning in the data set stored in the data set storage part66.

The machine learning model selection part52selects, among the machine learning models created by the machine learning model creation part50, one or more machine learning models appropriate for the prepared data set by using the data for evaluation in the data set stored in the data set storage part66.

The calculation part54acquires, for example, a target process result set by the operator. The calculation part54may acquire a target process result stored in the data set storage part66or the like, or may acquire a target process result input by the operator on an input screen or the like.

The calculation part54performs multi-objective optimization calculation by using the one or more machine learning models selected by the machine learning model selection part52to calculate a plurality of process conditions that achieve the target process result. Since a plurality of indicators of the target process result exists, the calculation part54performs multi-objective optimization to calculate a plurality of process conditions each of which achieves a process result satisfying all the indicators of the target process result, or a plurality of process conditions each of which achieves a process result close to the target process result.

In addition, some machine learning models may calculate reliability of the predicted values. Therefore, the calculation part54calculates a process condition, predicted values of a process result corresponding to the calculated process condition, and reliability of the predicted values. Reliability of predicted values of a machine learning model that cannot calculate the reliability of the predicted values is calculated by using another machine learning model that can calculate reliability of predicted values. The reliability of the predicted values serves as one of factors used for determining under which process condition the semiconductor manufacturing device executes a process, in other words, one of the factors used for selecting a process condition by a program.

The process condition selection part56selects one or more process conditions from the plurality of process conditions calculated by the calculation part54. For example, the process condition selection part56refers to predicted values of a process result of each process condition calculated by the calculation part54and reliability of the predicted values, and selects one or more process conditions having good predicted process performance and high reliability. The predicted process performance may be determined based on achievement levels of the predicted values with respect to the target values of the process result. The reliability of the process condition may be determined based on the reliability of the predicted values of the process result.

The display controller58displays, on an output screen or the like, the one or more process conditions selected by the process condition selection part56, the achievement levels of the predicted values with respect to the target values of the process result corresponding to each process condition, and the reliability of the predicted values. Therefore, the operator may recognize one or more process conditions which are predicted, by the machine learning models, to achieve a target process result. In addition, the display controller58performs displaying an input screen or the like via which a target process result input from the operator is received, and displaying a result of determining whether to continue or terminate the process condition search, which will be described later, or the like.

Thereafter, the operator causes the semiconductor manufacturing device10to execute a process according to each of the one or more process conditions selected by the process condition selection part56. In addition, the operator measures, by the external measuring instrument24, actually measured values of the process result after the process according to each process condition is executed. The operator provides the analysis server26with the actually measured values of the process result measured by using the external measuring instrument24. The actually measured values of the process result may be provided to the analysis server26from the external measuring instrument24via the network40, or may be provided via a recording medium such as a universal serial bus (USB) memory.

The process result receiving part60receives the actually measured values of the process result corresponding to each of the one or more process conditions. The determination part62compares the target values of the process result of each of the one or more process conditions selected by the process condition selection part56with the actually measured values of the process result received by the process result receiving part60, and determines whether to continue or terminate the process condition search based on achievement levels of the actually measured values with respect to the target values of the process result. The determination part62causes, for example, the display controller58to display the result of determining whether to continue or terminate the process condition search.

The feedback part64adds the one or more process conditions and the actually measured values of the process result corresponding to each process condition to the data set stored in the data set storage part66, thereby feeding back the process result. Since the analysis server26according to the present embodiment feeds back the actually measured values of the process result corresponding to each process condition as described above, the learning function operates, and the calculation accuracy from a next time onward is improved.

Process

In the following, a description will be made on an example in which an operator searches for a process condition that can achieve a target film formation result by using machine learning models. The film formation result is an example of a process result.

FIG.4is a flowchart of an example of a process performed by an information processing system according to the present embodiment. The information processing system1according to the present embodiment requires a prepared data set to be used for machine learning. In step S10, for example, the operator causes the semiconductor manufacturing device10to execute a film formation process while changing process conditions, and collects as data actually measured values of a film forming result when the film formation process is executed according to each process condition, thereby preparing the data set. In addition, the data set does not have to be prepared manually by the operator. For example, the data set may be prepared by automatically performing a design of experiment. The prepared data set is stored in the data set storage part66.

The operator who starts searching for a process condition that can achieve the target film formation result may set necessary information on an input screen1000as illustrated inFIG.5, and may start the process condition search by performing an operation of pushing a calculation execution button1008.

FIG.5is an image diagram of an example of an input screen. The input screen1000ofFIG.5includes a target value input field1002, a control knob swing width limit input field1004, a display field1006for displaying a list of models to be used, a calculation execution button1008, and a film formation result input field1010.

The target value input field1002is a field for setting indicators (target values) for determining success or failure of a target film formation result. The target values of the film formation result include a plurality of indicators such as a film thickness, in-plane uniformity, a refractive index, a wet etching rate (WER), and the like. In addition, the target value input field1002may be configured to set a priority for each target value.

The control knob swing width limit input field1004is a field for setting upper and lower limits of parameters for adjusting the control knobs (a temperature, a pressure, a gas, and the like). The upper and lower limits of parameters are set for each control knob. Further, the control knob swing width limit input field1004may be configured to set a use priority for each control knob. For example, when N2gas is used, upper and lower limits of a flow rate of N2gas are set.

The display field1006for displaying a list of models to be used displays one or more machine learning models appropriate for the prepared data set as a list of machine learning models to be used. The display field1006may be configured to receive, from the operator, a change in the list of machine learning models to be used.

The calculation execution button1008is an example of a start instruction receiving part configured to receive an instruction for starting the process condition search from the operator. The analysis server26, which has received the operation of pushing the calculation execution button1008from the operator, starts multi-objective optimization calculation by using machine learning models displayed as, for example, models to be used in the display field1006.

The film formation result input field1010is a field for inputting actually measured values of the film formation result measured by the external measuring instrument24after causing the semiconductor manufacturing device10to execute the film formation process according to each of the one or more process conditions selected by the process condition selection part56.

Returning back to step S12ofFIG.4, the machine learning model creation part50of the analysis server26creates a plurality of machine learning models of regression methods such as linear regression and non-linear regression by using the data for learning in the data set stored in the data set storage part66. In addition, the machine learning model selection part52selects, among the machine learning models created by the machine learning model creation part50, one or more machine learning models appropriate for the prepared data set by using the data for evaluation in the data set stored in the data set storage part66.

In step S14, the calculation part54acquires, for example, target values of the film formation result set in the target value input field1002. In addition, the calculation part54performs multi-objective optimization calculation by using the one or more machine learning models selected by the machine learning model selection part52to calculate process conditions for achieving the target values of the film formation result, predicted values of the film formation result corresponding to each process condition, and reliability of the predicted values for each process condition.

In step S16, the process condition selection part56selects one or more process conditions from the process conditions calculated by the calculation part54. The process condition selection part56refers to the predicted values of the process result of each process condition calculated by the calculation part54and the reliability of the predicted values, and selects one or more process conditions having good predicted process performance and high reliability. The process condition selection part56may receive an operation of selecting process conditions from the operator.

In step S18, the display controller58displays, for example, on an output screen1100illustrated inFIG.6or the like, the one or more process conditions selected by the process condition selection part56, achievement levels of the predicted values with respect to the target values of the film formation result corresponding to each condition, and the reliability of the predicted values.

FIG.6is an image diagram of an example of an output screen. The output screen1100ofFIG.6includes a multi-objective optimization result display field1102, a prediction reliability display field1104, a predicted film formation result display field1106, and a proposed process condition list display field1108.

The multi-objective optimization result display field1102displays a result of multi-objective optimization calculation performed by the calculation part54by using one or more machine learning models. For example, in the multi-objective optimization result display field1102, a result of multi-objective optimization calculation is displayed in a graph as shown inFIG.7.

FIG.7is a display example of a result of multi-objective optimization calculation. The multi-objective optimization result display field1102inFIG.7is an example in which process conditions as a result of multi-objective optimization calculation are indicated by a graph, where a horizontal axis represents an achievement level of a predicted value with respect to a first target value of a film formation result, and a vertical axis represents an achievement level of a predicted value with respect to a second target value of the film formation result. In the graph ofFIG.7, each plot represents a corresponding one of the process conditions that are the result of multi-objective optimization calculation.

The prediction reliability display field1104displays reliability of prediction based on the result of multi-objective optimization calculation performed using one or more machine learning models. For example, in the prediction reliability display field1104, the reliability of prediction is displayed in a graph as illustrated inFIG.8.

FIG.8is a display example of an example of reliability of prediction. The prediction reliability display field1104of the inFIG.8is an example in which an achievement level of a predicted value with respect to a target value for each process condition and reliability of the predicted value are indicated by a graph, where a horizontal axis represents an achievement level of a predicted value with respect to the first target value of the film formation result, and a vertical axis represents reliability of the predicted value. In the graph ofFIG.8, each plot represents a corresponding one of the process conditions that are the result of multi-objective optimization calculation.

The proposed process condition list display field1108displays a list of process conditions, which are selected from the process conditions that are the result of multi-objective optimization calculation, and under which execution of the film formation process in the semiconductor manufacturing device10is proposed. In addition, the proposed process condition list display field1108may also be configured to receive a change in the list of process conditions for executing the film formation process in the semiconductor manufacturing device10from the operator. The predicted film formation result display field1106displays predicted values of a film formation result for each process condition proposed in the proposed process condition list display field1108.

In addition, the first target value and the second target value displayed in the multi-objective optimization result display field1102and the prediction reliability display field1104of the output screen1100ofFIG.6are examples, and it is desirable that the operator can select the first target value and the second target value. The operator determines one or more process conditions of the film formation process executed in the semiconductor manufacturing device10with reference to the output screen1100ofFIG.6.

The operator causes the semiconductor manufacturing device10to execute the film formation process under the one or more determined process conditions. When the film formation process is terminated, the operator measures actually measured values of a film forming result according to each process condition by using the external measuring instrument24. The operator inputs the actually measured values of the film formation result measured by the external measuring instrument24to, for example, the film formation result input field1010ofFIG.5, thereby providing the actually measured values to the analysis server26. The process result receiving part60receives the actually measured values of the film formation result corresponding to each of the one or more process conditions.

When the process result receiving part60receives the actually measured values of the film formation result corresponding to each of the one or more process conditions, the determination part62of the analysis server26proceeds the process from step S20to step S22.

In step S22, the determination part62determines whether to continue or terminate the process condition search based on achievement levels of the actually measured values with respect to target values of the film formation result, by comparing the target values of the film formation result and the actually measured values of the film formation result according to each process condition under which the film formation process is executed in the semiconductor manufacturing device10.

When the determination part62determines to continue the process condition search, the process proceeds from step S24to step S26. The feedback part64feed backs a process result by adding a process condition, under which the film formation process has been executed by the semiconductor manufacturing device10, and actually measured values of the process result corresponding to the process condition to the data set stored in the data set storage part66. The analysis server26returns the process from step S26to step S12. When the determination part62determines to terminate the process condition search, the analysis server26terminates the process in the flowchart ofFIG.4.

In the above, an example in which process conditions are selected based on the predicted values of the film formation result of each process condition calculated by the calculation part54and the reliability of the predicted values in step S16has been described, but the present disclosure is not limited to this example. In step S16, the process conditions may be selected according to priorities of target values of a film formation result, use priorities of control knobs, a degree of similarity of process conditions calculated by machine learning models, and the like.

In step S16, the process condition selection part56selects one or more process conditions from the process conditions calculated by the calculation part54by performing multi-objective optimization calculation, for example, as follows.

For example, in step S16, it is assumed that a process condition having the largest sum Ji of the following two objective functions fj(xi) is selected. The process condition calculated by performing the multi-objective optimization calculation is xi. The objective function is fj(xi). In the following example, when J1>J2>J3 and the number of process conditions to be proposed is one, the process condition selection part56proposes the process condition x1to the operator.

J⁢⁢1=f⁢⁢1⁢(x⁢⁢1)+f⁢⁢2⁢(x⁢⁢1)J⁢⁢2=f⁢⁢1⁢(x⁢⁢2)+f⁢⁢2⁢(x⁢⁢2)J⁢⁢3=f⁢⁢1⁢(x⁢⁢3)+f⁢⁢2⁢(x⁢⁢3)

Further, as described above, when priorities of target values of a film formation result are set or use priorities of control knobs are set, weight factors according to the priorities may be assigned to the above formula.

In addition, in step S12, the plurality of regression methods may be separately used for each objective. For example, the regression methods may be used such that the machine learning models are separately used according to linearity or non-linearity of each control knob. A linear regression model is adopted for a process condition of a control knob that can be fully represented by a linear model. In contrast, a non-linear model is adopted for a process condition of a control knob that has strong non-linearity. The selection of a machine learning model is performed by the machine learning model selection part52, but it may be performed through, for example, an operation by the operator.

In step S12, switching from a non-linear model to a locally linear regression model may be performed. In a region in which data has been sufficiently increased, it is possible to perform optimization using a more accurate machine learning model by performing switching from a nonlinear model to a locally linear regression model.

In the present embodiment, an example in which a film formation result includes a plurality of target values and a process condition includes a plurality of parameters for adjusting a plurality of control knobs has been described. However, the present disclosure is also applicable even when a film formation result includes a single target value, and a processing condition includes a single parameter for adjusting a single control knob.

According to the present embodiment, it is possible to solve a problem in that it takes time to search for a process condition for achieving a customer-specific target value of a process result because there are various target values for determining success of failure of a process result, and customers of the semiconductor manufacturing device10have target values of a process result different from one another.

Although embodiments of the disclosure have been described in detail above, the present disclosure is not limited to the above-described embodiments, and various modifications and substitutions can be made to the above-described embodiments without departing from the scope of the present disclosure. For example, in the present embodiment, a heat treatment device for film formation has been described as an example, but the present disclosure is also applicable to a batch film forming apparatus for a chemical vapor deposition (CVD) method, a thermal oxidation method, an ALD (atomic layer deposition) method, or the like.

According to the present disclosure, it is possible to search for process conditions that can achieve a target process result by using machine learning models of a semiconductor manufacturing device.

While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the disclosures. Indeed, the embodiments described herein may be embodied in a variety of other forms. Furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the disclosures. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the disclosures.