Information processing device, information processing method and semiconductor manufacturing system

Abstract

To provide a simulation technology by a physical model that copes with a deterioration state of a semiconductor manufacturing device.SOLUTION: An information processing device for executing simulation of a process state during execution by a semiconductor manufacturing device by using a physical model of the semiconductor manufacturing device includes a simulation execution part for setting an operation condition of the physical model to the same condition as the semiconductor manufacturing device to execute a simulation, a physical coefficient change part for changing a physical coefficient of the physical model so as to make an output value of the physical model close to an output value of the semiconductor manufacturing device, a deterioration state analysis part for analyzing a deterioration state of the semiconductor manufacturing device on the basis of a change in the physical coefficient, and a deterioration state output part for outputting information of the analyzed deterioration state of the semiconductor manufacturing device.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a semiconductor manufacturing system. [Background technology]

[0002] Process simulation is used in the manufacturing or research and development of semiconductor products. Process simulation can handle various physical phenomena related to semiconductor processes (hereinafter, simply referred to as processes) using physical models (see, for example, Patent Document 1).

[0003] For example, in process simulation, the process state during the process (such as the deposition of a film on a wafer, temperature state, gas state, etc.) is estimated from the measurement results after the process is executed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-125451 A Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure provides a simulation technique using a physical model corresponding to the deterioration state of semiconductor manufacturing equipment. [Means for solving the problem]

[0006] One aspect of the present disclosure is an information processing device that executes a simulation of a process state being executed in a semiconductor manufacturing equipment using a physical model of the semiconductor manufacturing equipment, and includes a simulation execution unit that executes a simulation by setting operating conditions of the physical model to the same conditions as those of the semiconductor manufacturing equipment, a physical coefficient changing unit that changes physical coefficients of the physical model so as to approximate output values ​​of the physical model to corresponding output values ​​of the semiconductor manufacturing equipment, a degradation state analysis unit that analyzes a degradation state of the semiconductor manufacturing equipment based on changes in the physical coefficients, and a degradation state output unit that outputs information on the analyzed degradation state of the semiconductor manufacturing equipment. Effect of the Invention

[0007] According to the present disclosure, it is possible to provide a simulation technique using a physical model corresponding to the deterioration state of semiconductor manufacturing equipment. [Brief description of the drawings]

[0008] [Figure 1] 1 is a configuration diagram of an example of a semiconductor manufacturing system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating a hardware configuration of an example of a computer. [Diagram 3] FIG. 2 is a functional block diagram of an example of an autonomous controller according to the present embodiment. [Figure 4] 11 is a flowchart of an example of a data assimilation process of the semiconductor manufacturing system according to the present embodiment. [Diagram 5] 11 is a flowchart illustrating an example of a process for analyzing a deterioration state of the semiconductor manufacturing system according to the present embodiment. [Figure 6] 11 is a flowchart illustrating an example of a prediction process of the semiconductor manufacturing system according to the present embodiment. [Figure 7] 10 is a flowchart of an example of a process for adjusting a process parameter of the semiconductor manufacturing system according to the present embodiment. [Figure 8] 11 is a flowchart illustrating an example of optimization for each cleaning process of the semiconductor manufacturing system according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] <System configuration> 1 is a configuration diagram of an example of a semiconductor manufacturing system 1 according to this embodiment. The semiconductor manufacturing system 1 shown in FIG. 1 includes a semiconductor manufacturing equipment 10, an equipment controller 12, an autonomous controller 14, a server device 16, and an operator terminal 18.

[0011] The semiconductor manufacturing equipment 10, the equipment controller 12, and the autonomous controller 14 are installed in the manufacturing factory 2. The server device 16 and the operator terminal 18 may be installed in the manufacturing factory 2 or may be installed outside the manufacturing factory 2. The operator terminal 18 is an example of an information processing terminal operated by an operator such as an equipment operator or an analysis operator of the semiconductor manufacturing equipment 10 installed in the manufacturing factory 2. The semiconductor manufacturing equipment 10, the equipment controller 12, the autonomous controller 14, the server device 16, and the operator terminal 18 are communicatively connected via networks 20 and 22, such as the Internet or a LAN (Local Area Network).

[0012] The semiconductor manufacturing apparatus 10 is an apparatus that performs processes such as a film formation process, an etching process, or an ashing process, and processes, for example, semiconductor wafers (hereinafter simply referred to as wafers). The semiconductor manufacturing apparatus 10 is, for example, a substrate processing apparatus, a heat treatment apparatus, or a film formation apparatus.

[0013] The semiconductor manufacturing equipment 10 executes a process by receiving control commands (process parameters) according to, for example, a recipe from an equipment controller 12. The semiconductor manufacturing equipment 10 is equipped with a number of sensors, such as a temperature sensor for measuring temperature and a pressure sensor for measuring pressure, in order to monitor the process state.

[0014] The equipment controller 12 has a function of a man-machine interface that receives instructions for the semiconductor manufacturing equipment 10 from an operator and provides information about the semiconductor manufacturing equipment 10 to the operator. The equipment controller 12 receives output values ​​(one example of the output values ​​of the semiconductor manufacturing equipment 10) such as sensor values ​​output from a plurality of sensors installed in the semiconductor manufacturing equipment 10. The equipment controller 12 is an example of an information processing device that executes a simulation of the process state being executed in the semiconductor manufacturing equipment 10 by using a physical model of the semiconductor manufacturing equipment 10. The equipment controller 12 shown in FIG. 1 is provided for each semiconductor manufacturing equipment 10, but may be provided for each of a plurality of semiconductor manufacturing equipment 10. The equipment controller 12 may be provided inside the housing of the semiconductor manufacturing equipment 10 or outside the housing.

[0015] The autonomous controller 14 is a controller for autonomously controlling the semiconductor manufacturing equipment 10. The autonomous controller 14 receives output values ​​(one example of the output values ​​of the semiconductor manufacturing equipment 10) such as sensor values ​​output from a plurality of sensors installed in the semiconductor manufacturing equipment 10. The autonomous controller 14 is an example of an information processing device that executes a simulation of the process state being executed in the semiconductor manufacturing equipment 10 using a physical model of the semiconductor manufacturing equipment 10. The autonomous controller 14 optimizes the process parameters of the semiconductor manufacturing equipment 10, predicts the timing and content of failures, predicts the timing and content of maintenance, and the like.

[0016] 1 is provided for each semiconductor manufacturing apparatus 10, it may be provided for each of a plurality of semiconductor manufacturing apparatuses 10. The autonomous controller 14 may be provided inside the housing of the semiconductor manufacturing apparatus 10, or may be provided outside the housing.

[0017] Server device 16 may receive output values ​​(one example of the output values ​​of semiconductor manufacturing equipment 10), such as sensor values ​​output from a plurality of sensors installed in semiconductor manufacturing equipment 10, and store them as a process log. Server device 16 is an example of an information processing device that executes a simulation of the state of a process being executed in semiconductor manufacturing equipment 10 by using a physical model of semiconductor manufacturing equipment 10.

[0018] The server device 16 may store, for example, information related to the multiple semiconductor manufacturing apparatuses 10 in one or more manufacturing factories 2 (such as process parameters used to execute a process in the semiconductor manufacturing apparatuses 10, sensor values ​​when the process is executed according to the process parameters, and process result data) as a process log. Such process logs of the multiple semiconductor manufacturing apparatuses 10 may be used to improve the accuracy of a simulation of the state of a process being executed in the semiconductor manufacturing apparatuses 10.

[0019] The equipment controller 12, the autonomous control controller 14, and the server device 16 may notify the operator at the operator terminal 18 of information regarding the semiconductor manufacturing equipment 10, information regarding the results of the simulation, or information regarding the deterioration state of the semiconductor manufacturing equipment 10, using email or the like.

[0020] It should be noted that the semiconductor manufacturing system 1 shown in Fig. 1 is only one example, and it goes without saying that there are various system configurations depending on the application and purpose. The division of the equipment into the equipment controller 12, the autonomous controller 14, and the server device 16 shown in Fig. 1 is only one example. Various configurations are possible, such as a configuration in which at least two of the equipment controller 12, the autonomous controller 14, and the server device 16 are integrated, or a configuration in which they are further divided. In addition, the equipment controller 12 and the autonomous controller 14 may be configured to handle multiple semiconductor manufacturing devices 10 collectively.

[0021] <Hardware configuration> The equipment controller 12, the autonomous controller 14, the server device 16, and the operator terminal 18 of the semiconductor manufacturing system 1 shown in Fig. 1 are realized by, for example, a computer having a hardware configuration shown in Fig. 2. Fig. 2 is a hardware configuration diagram of an example of a computer 500.

[0022] 2 includes an input device 501, an output device 502, an external I / F (interface) 503, a RAM (Random Access Memory) 504, a ROM (Read Only Memory) 505, a CPU (Central Processing Unit) 506, a communication I / F 507, and a HDD (Hard Disk Drive) 508, all of which are connected to each other via a bus B. The input device 501 and the output device 502 may be connected and used when necessary.

[0023] The input device 501 is a keyboard, mouse, touch panel, etc., and is used by an operator to input operation signals. The output device 502 is a display, etc., and displays the results of processing by the computer 500. The communication I / F 507 is an interface that connects the computer 500 to the networks 20 and 22 shown in Fig. 1. The HDD 508 is an example of a non-volatile storage device that stores programs and data.

[0024] The external I / F 503 is an interface with an external device. The computer 500 can read a recording medium 503a such as a Secure Digital (SD) memory card via the external I / F 503. The external I / F 503 may be capable of writing to the recording medium 503a such as an SD memory card via the external I / F 503.

[0025] The ROM 505 is an example of a non-volatile semiconductor memory (storage device) that stores programs and data. The RAM 504 is an example of a volatile semiconductor memory (storage device) that temporarily holds programs and data. The CPU 506 is an arithmetic device that reads programs and data from a storage device such as the ROM 505 or HDD 508 onto the RAM 504 and executes processing to realize the control and functions of the entire computer 500.

[0026] The equipment controller 12, the autonomous controller 14, the server device 16, and the operator terminal 18 of the semiconductor manufacturing system 1 shown in FIG. 1 realize various functions by executing programs on the computer 500 shown in FIG.

[0027] <Functional configuration> In the following, an example will be described in which the information processing device that executes a simulation of a process state being executed in the semiconductor manufacturing equipment 10 using a physical model of the semiconductor manufacturing equipment 10 is the autonomous controller 14. The information processing device that executes a simulation of a process state being executed in the semiconductor manufacturing equipment 10 using a physical model of the semiconductor manufacturing equipment 10 may be the equipment controller 12 or the server device 16.

[0028] The autonomous controller 14 of the semiconductor manufacturing system 1 according to this embodiment is realized by, for example, the functional blocks shown in Fig. 3. Fig. 3 is a functional block diagram of an example of the autonomous controller 14 according to this embodiment. Note that the functional block diagram of Fig. 3 omits configurations that are not necessary for explaining this embodiment.

[0029] By executing a program for the autonomous control controller 14, the autonomous control controller 14 realizes an operating condition acquisition unit 30, a simulation execution unit 32, a physical model 34, an output value acquisition unit 36, a physical coefficient change unit 38, a deterioration state analysis unit 40, an adjustment unit 42, a prediction unit 44, a deterioration state memory unit 46, a result output unit 48, an inventory management unit 50, and a deterioration state output unit 52.

[0030] The operating condition acquisition unit 30 acquires, as operating conditions, process parameters of the semiconductor manufacturing equipment 10 that executes a process. The output value acquisition unit 36 ​​acquires output values ​​of the semiconductor manufacturing equipment 10, such as sensor values ​​and data values, when the semiconductor manufacturing equipment 10 executes a process according to the process parameters acquired by the operating condition acquisition unit 30 as operating conditions.

[0031] The simulation execution unit 32 has a physical model 34 of the semiconductor manufacturing equipment 10 for executing a simulation. The physical model 34 is a model that uses natural laws and is expressed by mathematical expressions derived from natural science. For example, the physical model 34 is a thermal model that outputs a predicted temperature of InnerT / C in a processing vessel of the semiconductor manufacturing equipment 10 according to heater power. For example, the thermal model of the semiconductor manufacturing equipment 10 is created based on information such as the equipment dimensions and physical property data of the semiconductor manufacturing equipment 10, and the relationship of heat exchange and the specific heat are modeled. The simulation execution unit 32 has a physical model 34 that corresponds to the output value of the semiconductor manufacturing equipment 10 acquired by the output value acquisition unit 36.

[0032] The simulation execution unit 32 sets the operating conditions of the physical model 34 that executes the simulation to the same conditions as the operating conditions of the semiconductor manufacturing equipment 10 , and executes a simulation of the output values ​​of the semiconductor manufacturing equipment 10 using the physical model 34 .

[0033] The physical coefficient changing unit 38 changes the physical coefficients of the physical model 34 so that the simulated output values ​​of the physical model 34 approximate (or may match) the output values ​​of the semiconductor manufacturing equipment 10 acquired by the output value acquiring unit 36 ​​(the output values ​​of the semiconductor manufacturing equipment 10 corresponding to the output values ​​of the simulated physical model 34).

[0034] The physical coefficient changing unit 38 performs data assimilation between the semiconductor manufacturing equipment 10 and the physical model 34 by successively changing the physical coefficients of the physical model 34 so that the output value of the physical model 34 approximates the output value of the semiconductor manufacturing equipment 10, provided that the operating conditions are the same.

[0035] The degradation state analysis unit 40 analyzes the degradation state of the semiconductor manufacturing equipment 10 based on the change in the physical coefficients of the physical model 34 changed by the physical coefficient change unit 38. For example, if the initial physical model 34 is an ideal model, the degradation state analysis unit 40 can analyze that the greater the difference between the initial physical model 34 and the physical coefficients, the more advanced the degradation. The degradation state analysis unit 40 can analyze that the smaller the difference between the initial physical model 34 and the physical coefficients, the less advanced the degradation. For example, if the physical coefficients of the physical model 34 of the heat transmission part of the heat insulating material have changed, the degradation state analysis unit 40 can analyze the possibility of degradation of the heat insulating material of the semiconductor manufacturing equipment 10. The degradation state analysis unit 40 can quantitatively analyze the degradation state of the semiconductor manufacturing equipment 10, and can also analyze the characteristics of the degradation state of the semiconductor manufacturing equipment 10 for a predetermined period of operation.

[0036] The degradation state analysis unit 40 may analyze the degradation state of the semiconductor manufacturing equipment 10 using a prediction model that has been machine-learned to learn the relationship between changes in one or more physical coefficients of the physical model 34 and the degradation state of the semiconductor manufacturing equipment 10. The machine learning of the prediction model may be performed by using training data in which changes in one or more physical coefficients of the physical model 34 are associated with the degradation state of the semiconductor manufacturing equipment 10, and adjusting parameters of the prediction model so as to output the degradation state of the semiconductor manufacturing equipment 10 when changes in one or more physical coefficients of the physical model 34 are input.

[0037] The degradation state storage unit 46 stores information on the degradation state of the semiconductor manufacturing equipment 10 analyzed by the degradation state analysis unit 40. The degradation state output unit 52 outputs information on the degradation state of the semiconductor manufacturing equipment 10 analyzed by the degradation state analysis unit 40. For example, the degradation state output unit 52 may transmit the information on the degradation state of the semiconductor manufacturing equipment 10 analyzed by the degradation state analysis unit 40 to the operator terminal 18 to notify the operator. In addition, the degradation state output unit 52 may transmit the information on the degradation state of the semiconductor manufacturing equipment 10 analyzed by the degradation state analysis unit 40 to the equipment controller 12 and cause the equipment controller 12 to display it.

[0038] The prediction unit 44 predicts the timing and content of maintenance that will be required for the semiconductor manufacturing equipment 10, based on the information on the deterioration state of the semiconductor manufacturing equipment 10 analyzed by the deterioration state analysis unit 40. For example, the prediction unit 44 may predict the timing and content of maintenance that will be required for the semiconductor manufacturing equipment 10, based on the change over time in the deterioration state of the semiconductor manufacturing equipment 10 analyzed by the deterioration state analysis unit 40 (for example, the range of change in the deterioration state in a predetermined period of time, etc.).

[0039] The inventory management unit 50 manages the inventory of parts required for the maintenance whose timing and content are predicted by the prediction unit 44. For example, the inventory management unit 50 identifies parts required for the maintenance whose timing and content are predicted by the prediction unit 44, and manages the inventory so that the parts required at the time of maintenance are in stock. The inventory management unit 50 may work in conjunction with a purchasing system to automatically order parts in time for the maintenance.

[0040] Based on the result of the simulation of the process state being executed by semiconductor manufacturing equipment 10 executed by simulation executing unit 32, adjustment unit 42 may adjust the process parameters of the operating conditions of semiconductor manufacturing equipment 10 so as to uniform the quality of wafers manufactured by semiconductor manufacturing equipment 10. Based on the result of the simulation, adjustment unit 42 optimizes the process parameters of the operating conditions of semiconductor manufacturing equipment 10. Result output unit 48 outputs the result of the simulation of the process state being executed by semiconductor manufacturing equipment 10 executed by simulation executing unit 32.

[0041] In this way, the semiconductor manufacturing system 1 according to this embodiment can realize a digital twin that reproduces in a virtual (cyber) space, with real-time linkage, changes in real (physical) space, that is, the process state of the semiconductor manufacturing equipment 10 during process execution. The digital twin can reproduce the process state of the semiconductor manufacturing equipment 10 in real time in the virtual space while the semiconductor manufacturing equipment 10 executes a process. By utilizing the digital twin environment, the semiconductor manufacturing system 1 according to this embodiment can monitor the process state of the semiconductor manufacturing equipment 10, and can also predict, for example, the timing and content of failures, the timing and content of maintenance, and adjust process parameters.

[0042] <Processing> Data Assimilation In the semiconductor manufacturing system 1 according to this embodiment, data assimilation is performed, for example, as shown in Fig. 4, based on the operating conditions acquired by the operating condition acquisition unit 30 from the semiconductor manufacturing equipment 10 in operation and the output values ​​acquired by the output value acquisition unit 36. Fig. 4 is a flowchart of an example of the data assimilation process of the semiconductor manufacturing system 1 according to this embodiment.

[0043] In step S10, when the semiconductor manufacturing equipment 10 executes a process, the autonomous controller 14 performs the processes of steps S12 to S16.

[0044] In step S12, the operating condition acquisition unit 30 acquires process parameters as operating conditions from the operating semiconductor manufacturing equipment 10 that is executing a process. In addition, the output value acquisition unit 36 ​​acquires output values ​​of the semiconductor manufacturing equipment 10, such as sensor values ​​and data values, of the semiconductor manufacturing equipment 10 that has executed a process according to the process parameters acquired by the operating condition acquisition unit 30 as operating conditions.

[0045] In step S14, the simulation execution unit 32 sets the operating conditions of the physical model 34 for executing the simulation to the same conditions as the operating conditions of the semiconductor manufacturing equipment 10, and executes a simulation of the output values ​​of the semiconductor manufacturing equipment 10 using the physical model 34.

[0046] In step S16, the physical coefficient changing unit 38 changes the physical coefficients of the physical model 34 so that the simulated output value of the physical model 34 approximates the output value of the semiconductor manufacturing equipment 10 acquired by the output value acquiring unit 36. In this way, if the operating conditions are the same, the physical coefficient changing unit 38 performs data assimilation between the semiconductor manufacturing equipment 10 and the physical model 34 by successively changing the physical coefficients of the physical model 34 so that the output value of the physical model 34 approximates the output value of the semiconductor manufacturing equipment 10. The process of successively changing the physical coefficients of the physical model 34 so that the output value of the physical model 34 approximates the output value of the semiconductor manufacturing equipment 10 is a process of tuning (adjusting) the deviation between the semiconductor manufacturing equipment 10 and the physical model 34 during operation.

[0047] By the data assimilation process of FIG. 4, the semiconductor manufacturing system 1 according to this embodiment can calibrate the physical model 34 based on the operating conditions of the semiconductor manufacturing equipment 10 in operation and the output values ​​of the semiconductor manufacturing equipment 10 that executes a process in accordance with those operating conditions so that the output values ​​are assimilated with those of the semiconductor manufacturing equipment 10 in operation.

[0048] Deterioration state analysis In the semiconductor manufacturing system 1 according to this embodiment, based on the changes in the physical coefficients of the physical model 34 changed by data assimilation, information on the degradation state of the semiconductor manufacturing equipment 10 in operation is analyzed and output, for example, as shown in Fig. 5. Fig. 5 is a flowchart of an example of a process of degradation state analysis in the semiconductor manufacturing system 1 according to this embodiment.

[0049] In step S20, when the physical coefficients of the physical model 34 are changed by the physical coefficient change unit 38, the autonomous controller 14 performs the processes of steps S22 to S26.

[0050] In step S22, the degradation state analyzing unit 40 acquires information on the changes in the physical coefficients of the physical model 34 changed by the physical coefficient changing unit 38. The information on the changes in the physical coefficients is quantitative information.

[0051] In step S24, the degradation state analysis unit 40 analyzes the degradation state of the operating semiconductor manufacturing equipment 10 based on the change in the physical coefficient. The degradation state analysis unit 40 may analyze the degradation state of the operating semiconductor manufacturing equipment 10 based on the trend of change in the physical coefficient. The degradation state of the operating semiconductor manufacturing equipment 10 is associated with, for example, the trend of change in the physical coefficient.

[0052] The degradation state analysis unit 40 may analyze the degradation state of the semiconductor manufacturing equipment 10 in operation after a predetermined period of time, for example, based on the trend of changes in physical coefficients, or may analyze the transition of the degradation state from the present to a predetermined period of time later.

[0053] In step S26, the degradation state output unit 52 outputs information on the degradation state of the semiconductor manufacturing equipment 10 analyzed by the degradation state analysis unit 40. For example, the degradation state output unit 52 transmits the information on the degradation state of the semiconductor manufacturing equipment 10 analyzed by the degradation state analysis unit 40 to the operator terminal 18 to notify the operator.

[0054] "prediction" In the semiconductor manufacturing system 1 according to this embodiment, the timing and content of maintenance is predicted, for example, as shown in Fig. 6, based on the deterioration state of the semiconductor manufacturing equipment 10 in operation analyzed by the deterioration state analysis unit 40. Fig. 6 is a flowchart of an example of prediction processing in the semiconductor manufacturing system 1 according to this embodiment.

[0055] In step S30, when the degradation state of the semiconductor manufacturing equipment 10 in operation is analyzed by the degradation state analysis unit 40, the autonomous controller 14 performs the processes of steps S32 to S36.

[0056] In step S32, prediction unit 44 acquires information on the degradation state of semiconductor manufacturing equipment 10 analyzed by degradation state analysis unit 40. In step S34, degradation state analysis unit 40 predicts the timing and content of maintenance that will be required for semiconductor manufacturing equipment 10, based on the information on the degradation state of semiconductor manufacturing equipment 10 during operation.

[0057] For example, the prediction unit 44 may set a threshold value for the deterioration state of the semiconductor manufacturing equipment 10 at which maintenance is required for each type of maintenance, and then predict the timing and type of maintenance that will be required for the semiconductor manufacturing equipment 10 based on the analyzed information on the deterioration state of the semiconductor manufacturing equipment 10.

[0058] In step S36, the inventory management unit 50 manages the inventory of parts required for the maintenance whose timing and content have been predicted by the prediction unit 44. For example, the inventory management unit 50 identifies parts required for the maintenance whose timing and content have been predicted by the prediction unit 44, and manages the inventory so that the parts required at the time of maintenance are in stock. The inventory management unit 50 may cooperate with a parts purchasing management server described in JP 2022-102821 A, which is an example of a purchasing system, and automatically order parts in time for the maintenance.

[0059] <<Process parameter adjustment>> In the semiconductor manufacturing system 1 according to this embodiment, the process parameters of the semiconductor manufacturing equipment 10 in operation are adjusted using the physical model 34 in which the physical coefficients have been changed by data assimilation, for example, as shown in Fig. 7. Fig. 7 is a flowchart of an example of a process for adjusting the process parameters of the semiconductor manufacturing system 1 according to this embodiment.

[0060] In step S40, when the semiconductor manufacturing equipment 10 executes a process, the autonomous controller 14 performs the processes of steps S42 to S46.

[0061] In step S42, adjustment unit 42 acquires the results of the simulation of the process state being executed by semiconductor manufacturing equipment 10, which has been executed by simulation execution unit 32. In step S44, based on the acquired simulation results, adjustment unit 42 determines process parameters of the operating conditions of semiconductor manufacturing equipment 10 so as to uniform the quality of wafers manufactured by semiconductor manufacturing equipment 10.

[0062] For example, adjustment unit 42 determines process parameters so that process conditions such as the predicted temperature in the processing chamber of semiconductor manufacturing equipment 10 become constant, and the quality of wafers manufactured by semiconductor manufacturing equipment 10 becomes uniform.

[0063] In step S46, the adjustment unit 42 adjusts the process parameters of the operating semiconductor manufacturing equipment 10 to the determined process parameters. By the process parameter adjustment process of Fig. 7, the semiconductor manufacturing system 1 according to this embodiment can uniformize the quality of the wafers it manufactures.

[0064] <Summary> For example, semiconductor manufacturing equipment 10 that has been developed and will be delivered to a customer's manufacturing factory 2 is tested to ensure that there are no problems before being delivered to the customer's manufacturing factory 2. In terms of development, many processes are carried out before shipping, such as planning, requirements analysis, functional design, structural design, detailed design, coding, unit testing, integration testing, functional testing, actual equipment testing, and shipping decision meetings. However, even if many processes are carried out before shipping, it cannot be guaranteed that the semiconductor manufacturing equipment 10 delivered to the customer's manufacturing factory 2 will operate normally at the customer's manufacturing factory 2 due to differences in the environment of the customer's manufacturing factory 2.

[0065] Therefore, in the semiconductor manufacturing system 1 according to this embodiment, after the semiconductor manufacturing equipment 10 is delivered to the customer's manufacturing plant 2, a physical model 34 that has been assimilated with the data of the semiconductor manufacturing equipment 10 as a digital twin is used to simulate the process state under the same operating conditions as the semiconductor manufacturing equipment 10 in operation.

[0066] In the semiconductor manufacturing system 1 according to this embodiment, the physical coefficients of the physical model are changed so that the output values ​​of the physical model 34 are made to approximate the output values ​​of the corresponding semiconductor manufacturing equipment 10, and the degradation state of the semiconductor manufacturing equipment 10 in operation is analyzed based on the change in the physical coefficients. The semiconductor manufacturing equipment 10 in operation for which the degradation state is analyzed may be the semiconductor manufacturing equipment 10 immediately after delivery to the customer's manufacturing factory 2 and may be the semiconductor manufacturing equipment 10 in operation at the customer's manufacturing factory 2. Furthermore, in the semiconductor manufacturing system 1 according to this embodiment, an autonomous digital twin system can be realized in the customer's manufacturing factory 2, which can predict the timing and content of failures and the timing and content of maintenance based on the analyzed deterioration state of the semiconductor manufacturing equipment 10.

[0067] According to an autonomous digital twin system in which prediction of the timing and content of failures and the timing and content of maintenance based on the analyzed deterioration state of the semiconductor manufacturing equipment 10 is performed entirely at the customer's manufacturing factory 2, improvements in reliability, performance, and maintainability can be achieved autonomously.

[0068] In addition, in the semiconductor manufacturing system 1 of this embodiment, in response to the problem that it is difficult to apply the physical model 34 to a simulation of the process state of the semiconductor manufacturing equipment 10, data assimilation with the semiconductor manufacturing equipment 10 in operation using a digital twin is performed, thereby making it easy to apply the physical model 34.

[0069] Furthermore, in the semiconductor manufacturing system 1 according to this embodiment, after a fault occurs in the semiconductor manufacturing equipment 10 during operation, the physical coefficient change unit 38 changes the physical coefficients, and the relationship between the change in the physical coefficients and the fault (degraded state) that has occurred in the semiconductor manufacturing equipment 10 is learned, and this may be used for advance detection of the fault and prevention of the fault. Furthermore, the semiconductor manufacturing system 1 according to this embodiment can also handle recovery of the degraded state of the semiconductor manufacturing equipment 10 through maintenance, a degraded state that worsens from a long-term perspective even after maintenance, or determining the lifespan of parts through future prediction.

[0070] <Implementation image> An example of optimization for each cleaning process will be described below as an example of the implementation of the semiconductor manufacturing system 1 according to this embodiment. For example, depending on the type of process in which dry cleaning is performed, there is a semiconductor manufacturing apparatus 10 that performs dry cleaning once every three times.

[0071] In a semiconductor manufacturing apparatus 10 that performs dry cleaning once every three times, the process is carried out consecutively from the thinnest part of the film in the first, second, and third times after the dry cleaning, so that the film thickness changes greatly due to the change in conditions.

[0072] In the semiconductor manufacturing system 1 of this embodiment, it is possible to check whether the optimized process parameters are correct by re-simulating using previously optimized process parameters, thereby checking whether the optimization is appropriate and how effective it is, and it is also possible to apply the process parameters by adjusting the operating conditions using the digital twin system.

[0073] FIG. 8 is a flowchart showing an example of optimization for each cleaning process of the semiconductor manufacturing system 1 according to this embodiment.

[0074] In step S100, the semiconductor manufacturing equipment 10 starts the process. In step S102, the semiconductor manufacturing equipment 10 accumulates the accumulated film thickness. In step S104, the semiconductor manufacturing equipment 10 ends the process.

[0075] In step S106, the semiconductor manufacturing equipment 10 determines whether the number of times to execute the cleaning process has been reached. For example, the semiconductor manufacturing equipment 10 determines to execute the cleaning process every three processes. If the number of times to execute the cleaning process has not been reached, the semiconductor manufacturing equipment 10 returns to the process of step S100. If the number of times to execute the cleaning process has been reached, the semiconductor manufacturing equipment 10 proceeds to the process of step S108.

[0076] In step S108, the semiconductor manufacturing equipment 10 starts a cleaning process to perform dry cleaning. In step S110, the semiconductor manufacturing equipment 10 clears the cumulative film pressure that was accumulated in step S102. In step S112, the semiconductor manufacturing equipment 10 ends the cleaning process and returns to the process of step S100.

[0077] When the semiconductor manufacturing equipment 10 starts a process in step S100, the autonomous controller 14 performs processing in step S120. In step S120, the autonomous controller 14 sets the operating conditions of the physical model 34 of the semiconductor manufacturing equipment 10 to the same conditions as the operating conditions of the semiconductor manufacturing equipment 10, and executes a simulation of the process simultaneously with the process of the semiconductor manufacturing equipment 10.

[0078] In step S122, the autonomous controller 14 saves the results of the simulation. In step S124, the operator terminal 18 acquires the process results every three times. In step S126, the operator terminal 18 analyzes the condition changes every three times. In step S128, the operator terminal 18 determines parameters corresponding to the condition changes from the analysis results. In step S130, the operator terminal 18 sets parameters such as temperature, humidity, or gas to be changed every three times.

[0079] In step S132, the autonomous controller 14 performs a simulation based on past operation data to determine whether the function works effectively using the set parameters. In step S134, the operator terminal 18 prompts the operator to confirm that the function works normally based on the simulation results.

[0080] In step S136, the autonomous controller 14 changes the process parameters in accordance with the optimization function defined by the operator for each of the three processes, and the process returns to step S100.

[0081] The semiconductor manufacturing system 1 according to the present embodiment can provide a simulation technique using a physical model 34 that corresponds to the deterioration state of the semiconductor manufacturing equipment 10. Furthermore, the semiconductor manufacturing system 1 according to the present embodiment can provide a technique for analyzing and outputting the deterioration state of the semiconductor manufacturing equipment 10 in real time.

[0082] Although the preferred embodiments of the present invention have been described in detail above, the present invention 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 invention. [Explanation of symbols]

[0083] 1. Semiconductor manufacturing systems 10. Semiconductor manufacturing equipment 12 Equipment Controller 14 Autonomous Controller 16 Server equipment 18 Worker terminal 32 Simulation Execution Department 34 Physical Model 38 Physical coefficient change section 40 Deterioration State Analysis Section 42 Adjustment section 44 Prediction Department 50 Inventory Management Department 52 Degradation state output section

Claims

1. 1. An information processing device that executes a simulation of a process state being executed in a semiconductor manufacturing device by using a physical model of the semiconductor manufacturing device, a simulation execution unit that executes a simulation by setting operating conditions of the physical model to the same conditions as those of the semiconductor manufacturing equipment; a physical coefficient changing unit that changes a physical coefficient of the physical model so as to approximate an output value of the physical model to a corresponding output value of the semiconductor manufacturing device; a degradation state analysis unit that analyzes a degradation state of the semiconductor manufacturing equipment based on the change in the physical coefficient; a degradation state output unit that outputs information on the analyzed degradation state of the semiconductor manufacturing equipment; An information processing device having the above configuration.

2. The simulation execution unit executes a simulation of a process state being executed in the semiconductor manufacturing equipment by using the physical model whose physical coefficients have been changed by the physical coefficient change unit.

2. The information processing device according to claim 1.

3. a prediction unit that predicts the timing and content of maintenance required for the semiconductor manufacturing equipment based on the deterioration state of the semiconductor manufacturing equipment analyzed by the deterioration state analysis unit; 3. The information processing apparatus according to claim 1, further comprising:

4. an inventory management unit that manages an inventory of parts necessary for the maintenance whose timing and contents have been predicted by the prediction unit; The information processing apparatus according to claim 3 , further comprising:

5. an adjustment unit that adjusts process parameters of operating conditions of the semiconductor manufacturing equipment so as to uniformize the quality of wafers manufactured by the semiconductor manufacturing equipment, based on a result of a simulation of a process state being executed by the semiconductor manufacturing equipment, the simulation result being executed using the physical model; 3. The information processing apparatus according to claim 1, further comprising:

6. The physical coefficient changing unit changes the physical coefficients of the physical model so as to approximate an output value of the physical model to an output value of the semiconductor manufacturing equipment or another semiconductor manufacturing equipment accumulated through operation.

3. The information processing device according to claim 1 or 2.

7. The deterioration state of the semiconductor manufacturing equipment is associated with a change trend of the physical coefficient.

3. The information processing device according to claim 1 or 2.

8. 1. An information processing method of an information processing device that executes a simulation of a process state being executed in a semiconductor manufacturing device by using a physical model of the semiconductor manufacturing device, comprising: executing a simulation under operating conditions of the physical model set to the same conditions as those of the semiconductor manufacturing equipment; changing a physical coefficient of the physical model so as to approximate an output value of the physical model to a corresponding output value of the semiconductor manufacturing device; Analyzing a deterioration state of the semiconductor manufacturing equipment based on the change in the physical coefficient; outputting information on the analyzed deterioration state of the semiconductor manufacturing equipment; An information processing method comprising the steps of:

9. 1. A semiconductor manufacturing system comprising: a semiconductor manufacturing apparatus; and an information processing apparatus that executes a simulation of a process state being executed in the semiconductor manufacturing apparatus by using a physical model of the semiconductor manufacturing apparatus, a simulation execution unit that executes a simulation by setting operating conditions of the physical model to the same conditions as those of the semiconductor manufacturing equipment; a physical coefficient changing unit that changes a physical coefficient of the physical model so as to approximate an output value of the physical model to a corresponding output value of the semiconductor manufacturing device; a degradation state analysis unit that analyzes a degradation state of the semiconductor manufacturing equipment based on the change in the physical coefficient; a degradation state output unit that outputs information on the analyzed degradation state of the semiconductor manufacturing equipment; A semiconductor manufacturing system having

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