Parameter optimization method and related device for low pressure chemical vapor deposition coating process

Abstract

The present application relates to a parameter optimization method for a low-pressure chemical vapor deposition coating process and a related device thereof, including: constructing a film thickness prediction model, the film thickness prediction model is used to indicate the relationship between the first process parameter of the control unit and the film thickness prediction value of the control unit; substituting the film thickness prediction model into the objective function model to obtain the process parameter optimization model of the control unit; optimizing the objective function model under constraint conditions to obtain the optimal value of the first process parameter of the control unit when the constraint conditions are met and the output of the objective function model is within the first threshold range. The solution provided by the present application can adjust the process parameters of the LPVCD coating equipment in real time, reduce the consumption of special gases in the process, and improve production capacity.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic cell technology, and in particular to a parameter optimization method for a low-pressure chemical vapor deposition coating process and a related device thereof. Background Art

[0002] In the manufacturing process of solar photovoltaic cells, low pressure chemical vapor deposition (LPCVD) process is generally used for coating, which is mainly used to deposit tunnel oxide layer and intrinsic polysilicon layer, which is beneficial to improve the efficiency and stability of photovoltaic cells.

[0003] In the actual process, the output is mainly increased by improving the structure of the LPCVD equipment. However, improving the equipment structure is rather cumbersome, the equipment replacement cycle is long, and it is not conducive to reducing the consumption of special gases in the process. Summary of the invention

[0004] In order to solve or partially solve the problems existing in the related technology, the present application provides a parameter optimization method for a low-pressure chemical vapor deposition coating process and a related device, which can adjust the process parameters of the LPVCD coating equipment production process in real time, reduce the consumption of special gases in the process, and improve production capacity.

[0005] The first aspect of the present application provides a parameter optimization method for a compressive chemical vapor deposition coating process, comprising constructing a film thickness prediction model, wherein the film thickness prediction model is used to indicate the relationship between a first process parameter of a control unit and a film thickness prediction value of the control unit; substituting the film thickness prediction model into an objective function model to obtain a process parameter optimization model of the control unit; optimizing the objective function model under constraints to obtain an optimal value of the first process parameter of the control unit when the constraints are satisfied and the output of the objective function model is within a first threshold range.

[0006] In combination with the first aspect, in a possible implementation manner of the first aspect, the first process parameter includes a time parameter, a temperature parameter, a flow parameter, and a pressure parameter.

[0007] In combination with the first aspect, in a possible implementation of the first aspect, the constructing of the film thickness prediction model includes: acquiring a first process parameter of the control unit, and obtaining a parameter influencing factor according to the first process parameter;

[0008] The film thickness prediction model is:

[0009] Y=A T X+B

[0010] Wherein, Y is the predicted value of the film thickness of the control unit, X is the first process parameter of the control unit, and A T is the transposed matrix of the parameter influencing factor, and B is the first constant.

[0011] In combination with the first aspect, in a possible implementation manner of the first aspect, the process parameter optimization model is:

[0012] f(X)=min Q(A T X+BY′) 2 +P(XX′) T (XX′)

[0013] Wherein, X is the first process parameter of the control unit, A T is the transposed matrix of the parameter influencing factors, B is the first constant, Y′ is the expected film thickness value, X′ is the expected process parameter of the control unit, Q is the second constant, and P is the third constant.

[0014] In combination with the first aspect, in a possible implementation of the first aspect, the constraint condition includes: the first process parameter is less than or equal to an upper limit value of the first process parameter, and the first process parameter is greater than or equal to a lower limit value of the first process parameter.

[0015] In combination with the first aspect, in a possible implementation manner of the first aspect, the second constant Q is greater than 0.5, and the third constant P is less than 0.3.

[0016] The second aspect of the present application provides a parameter optimization device for a low-pressure chemical vapor deposition coating process, including a construction module for constructing a film thickness prediction model, wherein the film thickness prediction model is used to indicate the relationship between a first process parameter of a control unit and a film thickness prediction value of the control unit; a processing module for substituting the film thickness prediction model into an objective function model to obtain a process parameter optimization model of the control unit; and an optimization module for optimizing the objective function model under constraints to obtain an optimal value of the first process parameter of the control unit when the constraints are met and the output of the objective function model is within a first threshold range.

[0017] In combination with the second aspect, in a possible implementation manner of the second aspect, the construction module is further used to obtain a first process parameter of the control unit, and obtain a parameter influencing factor according to the first process parameter.

[0018] A third aspect of the present application provides an electronic device, including:

[0019] Processor; and

[0020] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0021] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0022] A fifth aspect of the present application provides a computer program product, comprising: a computer program / instruction that implements the above-mentioned method when executed by a processor.

[0023] The technical solution provided by this application may have the following beneficial effects:

[0024] The present application discloses a parameter optimization method for a low-pressure chemical vapor deposition coating process and a related device thereof, comprising constructing a film thickness prediction model, wherein the film thickness prediction model is used to indicate the relationship between a first process parameter of a control unit and a film thickness prediction value of the control unit; substituting the film thickness prediction model into an objective function model to obtain a process parameter optimization model of the control unit; optimizing the objective function model under constraint conditions to obtain an optimal value of the first process parameter of the control unit when the constraint conditions are satisfied and the output of the objective function model is within a first threshold range, so as to be able to adjust the process parameters of the LPVCD coating equipment in real time, reduce the consumption of special gases in the process, and improve production capacity.

[0025] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0027] Figure 1 It is a schematic flow chart of a parameter optimization method for a low-pressure chemical vapor deposition coating process shown in an embodiment of the present application;

[0028] Figure 2 It is a schematic structural diagram of a parameter optimization device for a low-pressure chemical vapor deposition coating process shown in an embodiment of the present application;

[0029] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0031] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "a", "an" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0032] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0033] In the process of manufacturing solar photovoltaic cells, the current mainstream process routes include TOPCon, BC and HJT. Among the current solar photovoltaic cell manufacturers, TOPCon and BC routes account for more than 90%. For TOPCon and BC routes, low-pressure chemical vapor deposition (LPCVD) coating plays an important role in these two cell production routes, mainly reflected in its use in depositing tunnel oxide layers and intrinsic polysilicon layers. These steps are crucial to improving cell efficiency and stability.

[0034] The LPCVD process requires the introduction of a special gas called silane during operation to react chemically with oxygen to form a tunneling oxide layer and an intrinsic polysilicon layer. Therefore, in this process, in order to ensure the uniformity of the coating thickness, the process generally takes a long time and has a high cost, becoming a bottleneck process for reducing costs and increasing efficiency in the production of TOPCon and BC batteries.

[0035] In the related art, the output is mainly increased by improving the structure of the LPCVD equipment. However, improving the equipment structure is rather complicated, the equipment replacement cycle is long, and it is not conducive to reducing the consumption of special gases in the process.

[0036] In response to the above problems, the embodiments of the present application provide a parameter optimization method for a low-pressure chemical vapor deposition coating process and a related device, which can adjust the process parameters of the LPVCD coating equipment in real time, reduce the consumption of special gases during the process, and improve production capacity.

[0037] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0038] Figure 1 It is a flow chart of a parameter optimization method for a pressure chemical vapor deposition coating process shown in an embodiment of the present application.

[0039] See also Figure 1 , a method for optimizing parameters of a pressure chemical vapor deposition coating process, comprising:

[0040] S110: constructing a film thickness prediction model, where the film thickness prediction model is used to indicate the relationship between the first process parameter of the control unit and the film thickness prediction value of the control unit.

[0041] Specifically, the control unit can be a control unit for the control process steps of the LPVCD coating equipment. There can be several control units. A film thickness prediction model can be constructed, and the data obtained by the control unit can be input into the film thickness prediction model. The film thickness of the product can be predicted based on various data through the film thickness prediction model.

[0042] In a possible implementation, a first process parameter of the control unit is acquired, and a parameter influencing factor is obtained according to the first process parameter;

[0043] The film thickness prediction model is:

[0044] Y=A T X+B

[0045] Among them, Y is the predicted value of the film thickness of the control unit, X is the first process parameter of the control unit, and A T is the transposed matrix of the parameter influencing factor, and B is the first constant.

[0046] Specifically, a first process parameter of the control unit may be obtained. The first process parameter may include several parameters for controlling the film thickness. The first process parameter may be expressed as:

[0047]

[0048] in, represents the jth first process parameter of the i-th control unit, n represents that there are n control units in total, and m represents that there are m first process parameters in total; after obtaining the first process parameters, the parameter influencing factor of each first process parameter can be obtained according to the first process parameters. For example, the parameter influencing factor corresponding to the value of the first process parameter can be obtained from the database. The parameter influencing factor can be expressed as:

[0049]

[0050] in, The parameter influencing factor of the jth first process parameter of the ith control unit is represented. By inputting the parameter influencing factor corresponding to each first process parameter into the film thickness prediction model, the film thickness prediction value of each control unit can be obtained.

[0051] In a possible implementation manner, the first process parameter includes a time parameter, a temperature parameter, a flow parameter and a pressure parameter.

[0052] S120: Substitute the film thickness prediction model into the objective function model to obtain a process parameter optimization model of the control unit.

[0053] Specifically, the objective function model can be used to predict the first process parameter of the control unit, and the objective function model can be:

[0054] f(X)=min Q(YY′) 2 +P(XX′) T (XX′)

[0055] Among them, X is the first process parameter of the control unit, A T is the transposed matrix of parameter influencing factors, B is the first constant, Y′ is the expected film thickness value, X′ is the expected process parameter of the control unit, Q is the second constant, P is the third constant, and Y is the predicted value of the film thickness of the control unit. At this time, the predicted value of the film thickness of the control unit can be directly obtained through the first control parameter of the control unit. For example, the staff can obtain the predicted value of the film thickness at this time through the first control parameter.

[0056] In a possible implementation, the process parameter optimization model is:

[0057] f(X)=min Q(A T X+BY′) 2 +P(XX′) T (XX′)

[0058] Among them, X is the first process parameter of the control unit, A Tis the transposed matrix of the parameter influencing factors, B is the first constant, Y′ is the expected film thickness value, X′ is the expected process parameter of the control unit, Q is the second constant, and P is the third constant.

[0059] Specifically, the expected film thickness value Y′ can be obtained according to the LPCVD process control SPC standard, f(X) = min Q(A T X+BY′) 2 +P(XX′) T When (XX′) is minimum, the optimal value of the first process parameter of the control unit can be obtained.

[0060] S130: Optimizing the objective function model under the constraint conditions to obtain an optimal value of a first process parameter of the control unit when the constraint conditions are met and the output of the objective function model is within a first threshold range.

[0061] Specifically, the constraint condition can be used to constrain the value of the first process parameter to prevent the first process parameter from exceeding the allowable parameter range of the LPCVD coating equipment. When the output of the objective function model is within the first threshold range, for example, when f(X)=min Q(A T X+BY′) 2 +P(XX′) T When (XX′) is minimum, the optimal value of the first process parameter of the control unit is obtained. By changing the first process parameter of the control unit through the optimal value of the first process parameter, the consumption of special gases in the LPCVD coating equipment during operation can be reduced and the production capacity can be improved.

[0062] In a possible implementation, the constraint conditions include: the first process parameter is less than or equal to the upper limit value of the first process parameter, and the first process parameter is greater than or equal to the lower limit value of the first process parameter. The constraint conditions can prevent the first process parameter from exceeding the adjustable parameter range of the equipment after adjustment.

[0063] Furthermore, the value of the third constant P can be adjusted according to the expected process parameters of the control unit. For example, the first process parameter of the control unit numbered 1 can be Can be a time parameter, Can be a temperature parameter, Can be flow parameters, can be a pressure parameter, and the expected process parameter of the control unit can be Can be the expected time parameter, can be the expected temperature parameter, can be the expected flow parameters, can be the expected pressure parameter, then the value of the third constant P can be set to [0.3, 0.1, 0.1, 0.1]. That is, when the value of the third constant P with respect to the time parameter is greater than the values ​​of the other three parameters, then The value of is set to be close to and appropriately change For example, we can appropriately The value of becomes larger to ensure that the predicted film thickness value will not be too large or too small. By adjusting the value of P in this way, the production of LPCVD coating equipment can be increased.

[0064] Furthermore, the value of the third constant P can be adjusted according to the expected process parameters of the control unit. For example, the first process parameter of the control unit numbered 1 can be Can be a time parameter, Can be a temperature parameter, Can be flow parameters, can be a pressure parameter, and the expected process parameter of the control unit can be Can be the expected time parameter, can be the expected temperature parameter, can be the expected flow parameters, can be the expected pressure parameter, then the value of the third constant P can be set to [0.1, 0.1, 0.3, 0.1]. That is, when the value of the third constant P with respect to the flow parameter is greater than the values ​​of the other three parameters, then The value of is set to be close to And appropriately The value of becomes larger to ensure that the predicted film thickness value will not be too large or too small. By adjusting the value of P in this way, the consumption of LPCVD coating equipment can be reduced.

[0065] In a possible implementation manner, the value of Q is greater than 0.5, and the value of P is less than 0.3.

[0066] In a possible implementation, real-time film thickness data of the control unit may be obtained, the real-time film thickness data of the control unit may be compared with a predicted film thickness value of the control unit, and the real-time film thickness data that meets the requirements may be added to a database.

[0067] Corresponding to the aforementioned application function realization method embodiment, the present application also provides a parameter optimization device, electronic device and corresponding embodiments of a low-pressure chemical vapor deposition coating process.

[0068] Figure 2 It is a structural schematic diagram of a parameter optimization device for a low-pressure chemical vapor deposition coating process shown in an embodiment of the present application.

[0069] See also Figure 2 , a parameter optimization device 200 for a low-pressure chemical vapor deposition coating process, comprising:

[0070] The construction module 210 is used to construct a film thickness prediction model, where the film thickness prediction model is used to indicate the relationship between the first process parameter of the control unit and the film thickness prediction value of the control unit.

[0071] Specifically, the control unit can be a control unit for the control process steps of the LPVCD coating equipment. There can be several control units. A film thickness prediction model can be constructed, and the data obtained by the control unit can be input into the film thickness prediction model. The film thickness of the product can be predicted based on various data through the film thickness prediction model.

[0072] In a possible implementation, the construction module 210 is further used to obtain a first process parameter of the control unit, and obtain a parameter influencing factor according to the first process parameter;

[0073] The film thickness prediction model is:

[0074] Y=A T X+B

[0075] Among them, Y is the predicted value of the film thickness of the control unit, X is the first process parameter of the control unit, and A T is the transposed matrix of the parameter influencing factor, and B is the first constant.

[0076] Specifically, the first process parameter of the control unit may be obtained. The first process parameter may include several parameters for controlling the coating. The first process parameter may be expressed as:

[0077]

[0078] in, represents the jth first process parameter of the i-th control unit, n represents that there are n control units in total, and m represents that there are m first process parameters in total; after obtaining the first process parameters, the parameter influencing factor of each first process parameter can be obtained according to the first process parameters. For example, the parameter influencing factor corresponding to the value of the first process parameter can be obtained from the database. The parameter influencing factor can be expressed as:

[0079]

[0080] in, The parameter influencing factor of the jth first process parameter of the ith control unit is represented. By inputting the parameter influencing factor corresponding to each first process parameter into the film thickness prediction model, the film thickness prediction value of each control unit can be obtained.

[0081] The processing module 220 is used to substitute the film thickness prediction model into the objective function model to obtain a process parameter optimization model of the control unit.

[0082] Specifically, the objective function model can be used to predict the first process parameter of the control unit, and the objective function model can be:

[0083] f(X)=min Q(YY′) 2 +P(XX′) T (XX′)

[0084] Among them, X is the first process parameter of the control unit, A T is the transposed matrix of parameter influencing factors, B is the first constant, Y′ is the expected film thickness value, X′ is the expected process parameter of the control unit, Q is the second constant, P is the third constant, and Y is the predicted value of the film thickness of the control unit. At this time, the predicted value of the film thickness of the control unit can be directly obtained through the first control parameter of the control unit. For example, the staff can obtain the predicted value of the film thickness at this time through the first control parameter.

[0085] In a possible implementation, the processing module 220 is also used for the process parameter optimization model as follows:

[0086] f(X)=min Q(A T X+BY′) 2 +P(XX′) T (XX′)

[0087] Among them, X is the first process parameter of the control unit, A T is the transposed matrix of the parameter influencing factors, B is the first constant, Y′ is the expected film thickness value, X′ is the expected process parameter of the control unit, Q is the second constant, and P is the third constant.

[0088] The optimization module 230 is used to optimize the objective function model under the constraint condition to obtain the optimal value of the first process parameter of the control unit when the constraint condition is satisfied and the output of the objective function model is within the first threshold range.

[0089] Specifically, the constraint condition can be used to constrain the value of the first process parameter to prevent the first process parameter from exceeding the allowable parameter range of the LPCVD coating equipment. When the output of the objective function model is within the first threshold range, for example, when f(X)=min Q(A T X+BY′) 2 +P(XX′) T When it is minimum, the optimal value of the first process parameter of the control unit is obtained. By changing the first process parameter of the control unit according to the optimal value of the first process parameter, the consumption of special gases in the LPCVD coating equipment during operation can be reduced and the production capacity can be improved.

[0090] In a possible implementation, the optimization module 230 is also used when the constraint conditions include: the first process parameter is less than or equal to the upper limit value of the first process parameter, and the first process parameter is greater than or equal to the lower limit value of the first process parameter. The constraint condition can avoid the first process parameter exceeding the adjustable parameter range of the equipment after adjustment, and optimize the objective function model.

[0091] In a possible implementation, the optimization module 230 is further configured to adjust the value of the third constant P according to the expected process parameters of the control unit. For example, the first process parameter of the control unit numbered 1 may be: Can be a time parameter, Can be a temperature parameter, Can be flow parameters, can be a pressure parameter, and the expected process parameter of the control unit can be Can be the expected time parameter, can be the expected temperature parameter, can be the expected flow parameters, can be the expected pressure parameter, then the value of the third constant P can be set to [0.3, 0.1, 0.1, 0.1]. That is, when the value of the third constant P with respect to the time parameter is greater than the values ​​of the other three parameters, then The value of is set to be close to and appropriately change The value of becomes larger, for example, we can appropriately The value of becomes larger to ensure that the predicted value of the film thickness will not be too large or too small, and the objective function model is optimized accordingly. By adjusting the value of P in this way, the production of LPCVD coating equipment can be increased.

[0092] In a possible implementation, the optimization module 230 is further configured to adjust the value of the third constant P according to the expected process parameters of the control unit. For example, the first process parameter of the control unit numbered 1 may be Can be a time parameter, Can be a temperature parameter, Can be flow parameters, can be a pressure parameter, and the expected process parameter of the control unit can be Can be the expected time parameter, can be the expected temperature parameter, can be the expected flow parameters, can be the expected pressure parameter, then the value of the third constant P can be set to [0.1, 0.1, 0.3, 0.1]. That is, when the value of the third constant P with respect to the flow parameter is greater than the values ​​of the other three parameters, then The value is set to be less than And appropriately The value of becomes larger to ensure that the predicted value of film thickness will not be too large or too small. By adjusting the value of P in this way, the consumption of LPCVD coating equipment can be reduced, thereby optimizing the objective function model.

[0093] The present application discloses a parameter optimization device for a low-pressure chemical vapor deposition coating process, comprising constructing a film thickness prediction model, wherein the film thickness prediction model is used to indicate the relationship between a first process parameter of a control unit and a film thickness prediction value of the control unit; substituting the film thickness prediction model into an objective function model to obtain a process parameter optimization model of the control unit; optimizing the objective function model under constraints to obtain an optimal value of the first process parameter of the control unit when the constraints are met and the output of the objective function model is within a first threshold range, thereby being able to adjust the process parameters of the LPVCD coating equipment in real time, reduce the consumption of special gases in the process, and improve production capacity.

[0094] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0095] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0096] See also Figure 3 , the electronic device 300 includes a memory 310 and a processor 320.

[0097] The processor 320 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0098] The memory 310 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 320 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 310 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 310 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0099] The memory 310 stores executable codes, and when the executable codes are processed by the processor 320 , the processor 320 can execute part or all of the methods described above.

[0100] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0101] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0102] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A parameter optimization method for a low pressure chemical vapor deposition coating process, characterized in that: include: Constructing a film thickness prediction model, wherein the film thickness prediction model is used to indicate a relationship between a first process parameter of a control unit and a film thickness prediction value of the control unit; Substituting the film thickness prediction model into the objective function model to obtain a process parameter optimization model of the control unit; The objective function model is optimized under constraint conditions to obtain an optimal value of a first process parameter of the control unit when the constraint conditions are met and the output of the objective function model is within a first threshold range.

2. The method according to claim 1, characterized in that The first process parameters include time parameters, temperature parameters, flow parameters and pressure parameters.

3. The method according to claim 1, characterized in that The film thickness prediction model is constructed, comprising: Acquire a first process parameter of the control unit, and obtain a parameter influencing factor according to the first process parameter; The film thickness prediction model is: Y=A T X+B Wherein, Y is the predicted value of the film thickness of the control unit, X is the first process parameter of the control unit, and A T is the transposed matrix of the parameter influencing factor, and B is the first constant.

4. The method according to claim 3, characterized in that The process parameter optimization model is: f(X)=min Q(A T X+B-Y′) 2 +P(X-X′) T (X-X′) Wherein, X is the first process parameter of the control unit, A T is the transposed matrix of the parameter influencing factors, B is the first constant, Y′ is the expected film thickness value, X′ is the expected process parameter of the control unit, Q is the second constant, and P is the third constant.

5. The method according to claim 3, characterized in that: The constraint conditions include: the first process parameter is less than or equal to an upper limit value of the first process parameter, and the first process parameter is greater than or equal to a lower limit value of the first process parameter.

6. The method according to claim 3, characterized in that The second constant Q is greater than 0.5, and the third constant P is less than 0.

3.

7. A parameter optimization device for a low-pressure chemical vapor deposition coating process, characterized in that: include: A construction module, used to construct a film thickness prediction model, wherein the film thickness prediction model is used to indicate a relationship between a first process parameter of a control unit and a film thickness prediction value of the control unit; A processing module, used for substituting the film thickness prediction model into the objective function model to obtain a process parameter optimization model of the control unit; The optimization module is used to optimize the objective function model under constraint conditions to obtain an optimal value of the first process parameter of the control unit when the constraint conditions are met and the output of the objective function model is within a first threshold range.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in any one of claims 1 to 6.

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