Square resistance optimization method of low-pressure boron diffusion equipment and related device thereof

Abstract

The invention relates to a sheet resistance optimization method of low-pressure boron diffusion equipment and a related device thereof. The sheet resistance optimization method comprises the following steps: acquiring parameter data of a plurality of temperature zones and sheet resistance data of a plurality of small boats from the low-pressure boron diffusion equipment; calculating a plurality of key parameters according to the parameter data and the sheet resistance data; according to the sheet resistance data and the key parameters, constructing a sheet resistance result prediction model; and optimizing the square resistance result prediction model, and when the output of the square resistance result prediction model is within a first threshold range, obtaining the numerical value of the key parameter. According to the scheme provided by the invention, the process parameters of the low-voltage boron diffusion equipment can be optimized, so that the sheet resistance uniformity of the photovoltaic cell is improved, and the yield and the production efficiency of the photovoltaic cell are improved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic cell technology, and in particular to a method for optimizing the square resistance of a low-voltage boron diffusion device and a related device thereof. Background Art

[0002] In the photovoltaic industry, solar photovoltaic cell technology is a key production technology and the core material for preparing solar cell modules. In the production process of solar photovoltaic cells, the high temperature process is the key process for forming the PN junction in the cell production process, which directly determines the yield and efficiency of the cell.

[0003] The low-pressure boron diffusion production process is an important step in generating PN junctions. The low-pressure boron diffusion production process uses boron trichloride as the diffusion boron source, which is introduced into the equipment through saturated vapor pressure at a certain temperature in a gaseous state. During the diffusion process, the boron source is evenly distributed on the surface of the silicon wafer to form a PN junction. The process result of this process is characterized by the measurement parameter of square resistance. The uniformity of square resistance can make photovoltaic cells better.

[0004] In the related art, low-pressure boron diffusion equipment is generally modified and optimized, but the improvement of the equipment structure is relatively cumbersome, and the equipment replacement cycle is long, which is not conducive to improvement and cannot effectively improve the uniformity of square resistance. Summary of the invention

[0005] In order to solve or partially solve the problems existing in the related technology, the present application provides a square resistance optimization method of a low-voltage boron diffusion device and a related device, which can improve the square resistance uniformity of photovoltaic cells and improve the yield and production efficiency of photovoltaic cells by optimizing the process parameters of the low-voltage boron diffusion equipment.

[0006] The first aspect of the present application provides a method for optimizing the square resistance of a low-pressure boron diffusion device, comprising collecting parameter data of several temperature zones and square resistance data of several boats from the low-pressure boron diffusion device; calculating several key parameters based on the parameter data and the square resistance data; constructing a square resistance result prediction model based on the square resistance data and the key parameters; optimizing the square resistance result prediction model, and obtaining the numerical value of the key parameter when the output of the square resistance result prediction model is within a first threshold range.

[0007] In combination with the first aspect, in a possible implementation of the first aspect, the parameter data of several temperature zones and the square resistance data of several boats are collected from the low-pressure boron diffusion equipment, including performing multiple square resistance tests on each of the boats to obtain multiple target square resistance data for each boat; performing a legitimacy test on the target square resistance data, and using the target square resistance data that passes the legitimacy test as the square resistance data of the boat.

[0008] In combination with the first aspect, in a possible implementation of the first aspect, the parameter data includes: temperature data of the temperature zone, air flow data of the temperature zone, process time data of the temperature zone, pressure data of the temperature zone, and humidity data of the temperature zone.

[0009] In combination with the first aspect, in a possible implementation of the first aspect, several key parameters are calculated based on the parameter data and the square resistance data, including: calculating the square resistance average value of the square resistance data of each of the boats based on the square resistance data; calculating the first parameter average value of the parameter data of each of the temperature zones based on the parameter data; and calculating several key parameters based on the square resistance average value, the first parameter average value and a preset algorithm.

[0010] In combination with the first aspect, in a possible implementation of the first aspect, several key parameters are calculated based on the square resistance average value, the parameter average value and a preset algorithm, including: obtaining a second parameter average value of each parameter in the temperature zone in each process step based on the parameter data; and several key parameters are calculated based on the square resistance average value, the first parameter average value, the second parameter average value and the preset algorithm, and the key parameters are selected from the second parameter average value.

[0011] In combination with the first aspect, in a possible implementation manner of the first aspect, the square resistance result prediction model is:

[0012] f(X′)=A m*5 X′ T +C

[0013] Wherein, f(X′) is the square resistance result prediction model, X′ is the key parameter, A m*5 is the coefficient matrix, m is the number of boats, X′ T is the transposed matrix of the key parameters, and C is a preset constant.

[0014] The second aspect of the present application provides a square resistance optimization device for low-pressure boron diffusion equipment, including an acquisition module for collecting parameter data of several temperature zones and square resistance data of several boats from production equipment; a processing module for calculating several key parameters based on the parameter data and the square resistance data; a construction module for constructing a square resistance result prediction model based on the square resistance data and the key parameters; and an optimization module for optimizing the square resistance result prediction model to obtain the numerical value of the key parameter when the output of the square resistance result prediction model is within a first threshold range.

[0015] In combination with the second aspect, in a possible implementation of the second aspect, the acquisition module is also used to perform multiple square resistance tests on each of the boats to obtain multiple target square resistance data for each boat; perform a legitimacy check on the target square resistance data, and use the target square resistance data that passes the legitimacy check as the square resistance data of the boat.

[0016] In combination with the second aspect, in a possible implementation of the second aspect, the processing module is also used to calculate the square resistance average value of the square resistance data of each of the boats based on the square resistance data; calculate the first parameter average value of the parameter data of each of the temperature zones based on the parameter data; and calculate several key parameters based on the square resistance average value, the first parameter average value and a preset algorithm.

[0017] In combination with the second aspect, in a possible implementation of the second aspect, the processing module is also used to obtain a second parameter average value of each parameter in the temperature zone in each process step based on the parameter data; and calculate a number of key parameters based on the square resistance average value, the first parameter average value, the second parameter average value and the preset algorithm, and the key parameters are selected from the second parameter average value.

[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 square resistance optimization method for a low-voltage boron diffusion device and a related device thereof, comprising collecting parameter data of several temperature zones and square resistance data of several boats from the low-voltage boron diffusion device; calculating several key parameters based on the parameter data and the square resistance data; constructing a square resistance result prediction model based on the square resistance data and the key parameters; optimizing the square resistance result prediction model, and obtaining the value of the key parameter when the output of the square resistance result prediction model is within a first threshold range, which can improve the square resistance uniformity of photovoltaic cells and improve the yield and production efficiency of photovoltaic cells by optimizing the process parameters of the low-voltage boron diffusion device.

[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 method for optimizing the sheet resistance of a low-voltage boron diffusion device according to an embodiment of the present application;

[0028] Figure 2 It is a structural schematic diagram of a square resistance optimization device for a low-voltage boron diffusion device 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 for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "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 herein 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 photovoltaic industry, solar photovoltaic cell technology is a key production technology and the core material for preparing solar cell modules. In the production process of solar photovoltaic cells, the high temperature process is the key process for forming the PN junction in the cell production process, which directly determines the yield and efficiency of the cell.

[0034] The current mainstream solar photovoltaic cell process technology routes mainly include TOPCon, BC and HJT. In the TOPCon and BC process routes, the low-pressure boron diffusion production process is an important process for boron atom doping. The process result of this process is characterized by the measurement parameter of square resistance. In the actual production process, the uniformity of square resistance plays a vital role in the yield and efficiency of solar photovoltaic cells.

[0035] The low-pressure boron diffusion production process is an important step in generating PN junctions. The low-pressure boron diffusion production process uses boron trichloride as the diffusion boron source, which is introduced into the equipment through saturated vapor pressure at a certain temperature in a gaseous state. During the diffusion process, the boron source is evenly distributed on the surface of the silicon wafer to form a PN junction. The process result of this process is characterized by the measurement parameter of square resistance. The uniformity of square resistance can make photovoltaic cells better.

[0036] In the related art, low-pressure boron diffusion equipment is generally modified and optimized, but the improvement of the equipment structure is relatively cumbersome, and the equipment replacement cycle is long, which is not conducive to improvement and cannot effectively improve the uniformity of square resistance.

[0037] In response to the above problems, the embodiments of the present application provide a method for optimizing the square resistance of a low-voltage boron diffusion device and a related device, which can improve the square resistance uniformity of photovoltaic cells and improve the yield and production efficiency of photovoltaic cells by optimizing the process parameters of the low-voltage boron diffusion device.

[0038] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0039] Figure 1 It is a flow chart of a method for optimizing the square resistance of a low-voltage boron diffusion device shown in an embodiment of the present application.

[0040] See also Figure 1 , a method for optimizing the square resistance of a low-voltage boron diffusion device, comprising:

[0041] S110: Collect parameter data of several temperature zones and square resistance data of several boats from the low-pressure boron diffusion equipment.

[0042] Specifically, a plurality of temperature zones and a plurality of boats are provided in the low-pressure boron diffusion equipment. The temperature zone may be a processing area of ​​the material. There are a plurality of parameters affecting the process in the temperature zone. Parameter data of the plurality of temperature zones may be obtained. The parameter data may include data of a plurality of parameters. Sheet resistance data of the material may be obtained from the boat. Sheet resistance data may be sheet resistance detection data of each material. Specifically, the sheet resistance data may be expressed as:

[0043]

[0044] Among them, Y represents the square resistance data, represents the first square resistance data of the first boat, m represents the total number of m boats, and k represents the kth square resistance data of the boat;

[0045] Specifically, the collected parameter data of several temperature zones can be stored in the form of a table, for example:

[0046] Collection time Process steps <![CDATA[Parameter x 1 1 > <![CDATA[Parameter x 1 2 > <![CDATA[Parameter x 2 4 > … <![CDATA[Parameter x n j >

[0047] Where, j represents the jth parameter data of the temperature zone, x n j Indicates the jth parameter data of the nth temperature zone.

[0048] In one possible implementation, parameter data of several temperature zones and square resistance data of several boats are collected from a low-pressure boron diffusion device, including: performing multiple square resistance tests on each boat to obtain multiple target square resistance data for each boat; performing a legitimacy test on the target square resistance data, and using the target square resistance data that passes the legitimacy test as the square resistance data of the boat.

[0049] Specifically, the boats may be tested multiple times to obtain multiple target resistance data for each boat. After obtaining the target resistance data, a legitimacy check may be performed, and the resistance data in the target resistance data that fails the legitimacy check may be deleted. For example, when the value of the target resistance data is too large or too small, the target resistance data may be treated as illegal data, and the target resistance data that passes the legitimacy check may be retained and used as the resistance data of the boat.

[0050] In one possible implementation, the parameter data include: temperature data of the temperature zone, gas flow data of the temperature zone, process time data of the temperature zone, pressure data of the temperature zone, humidity data of the temperature zone, and may also include flow data of different types of special gases, speed of entering and exiting the boat, furnace door closing speed, temperature and humidity of the environment surrounding the low-pressure boron diffusion equipment, etc.

[0051] S120: Calculate and obtain several key parameters according to the parameter data and the square resistance data.

[0052] Specifically, several key parameter data can be calculated based on the acquired parameter data and square resistance data. There can be multiple key parameters, and the key parameters can be used to indicate that the parameters have a greater impact on the square resistance data. By adjusting the key parameters, the square resistance data of the boat can be better adjusted.

[0053] In a possible implementation, several key parameters are calculated based on the parameter data and the square resistance data, including: calculating the square resistance average value of the square resistance data of each boat based on the square resistance data; calculating the first parameter average value of the parameter data of each temperature zone based on the parameter data; and calculating several key parameters based on the square resistance average value, the first parameter average value and a preset algorithm.

[0054] Specifically, the square resistance average value of the square resistance data of each boat can be obtained by performing average aggregation on the square resistance data of each boat. The square resistance average value can be:

[0055]

[0056] in, The square resistance average value of the square resistance data of the first boat.

[0057] Specifically, the average value of the first parameter of each temperature zone may be obtained by performing average aggregation on the parameter data of each temperature zone. The average value of the first parameter may be:

[0058]

[0059] in, The first parameter average value representing the parameter data of the first temperature zone can be used to obtain several key parameters through the variance calculation formula after obtaining the square resistance average value and the first parameter average value.

[0060] In a possible implementation, several key parameters are calculated based on the square resistance average value, the parameter average value and a preset algorithm, including: obtaining the second parameter average value of each parameter in the temperature zone in each process step based on the parameter data; and calculating several key parameters based on the square resistance average value, the first parameter average value, the second parameter average value and a preset algorithm, wherein the key parameters are selected from the second parameter average value.

[0061] Specifically, the parameter data may be aggregated according to the temperature zone and the process step to obtain the second parameter average value, which may be:

[0062]

[0063] in, Indicates the parameter data x of the nth temperature zone 1 After obtaining the average value of the square resistance, the average value of the first parameter, and the average value of the second parameter in process step i, several key parameters can be obtained by using a variance calculation formula or a covariance formula. The key parameters are selected from the average value of the second parameter. For example, it can be expressed as:

[0064]

[0065] X′ is the key parameter, Parameter data x for temperature zone 1 1 The average value of the second parameter in process step 1, The parameter data x for temperature zone 2 2 The average value of the second parameter in process step 5, Parameter data x for temperature zone 3 6 The average value of the second parameter in process step 8, Parameter data x for temperature zone 4 13 The average value of the second parameter in process step 16, is the parameter x of temperature zone 4 14 In process step 16 the second parameter is averaged.

[0066] S130: Constructing a square resistance result prediction model based on the square resistance data and key parameters.

[0067] Specifically, the square resistance result prediction model can be:

[0068]

[0069] Among them, A m*5 is the coefficient matrix, A m*5 The size of the matrix corresponds to the number of X'. When the number of X' is 5, the size of the matrix is ​​m rows and 5 columns. T is the transposed matrix of the key parameters, C is the error coefficient matrix, and the optimized key parameters can be output through the square resistance result prediction model.

[0070] S140: Optimizing the square resistance result prediction model, and obtaining the value of the key parameter when the output of the square resistance result prediction model is within a first threshold range.

[0071] Specifically, the square resistance result prediction model can be optimized, that is, by adjusting the values ​​of key parameters to obtain the values ​​of key parameters when the output of the square resistance result prediction model is at a first threshold. The first threshold can be set according to actual needs and is not limited here.

[0072] Furthermore, constraints can be set on the resistance result prediction model. The constraints can be: Approaching 0, where Y Exp is the expected value of the square resistance data, that is, when the key parameters are adjusted, when the difference between the square resistance data in the square resistance result prediction model and the expected value of the square resistance data tends to 0, the error can be reduced. Furthermore, the difference between the minimum value of the square resistance data in the square resistance result prediction model and the expected value of the square resistance data can be greater than or equal to 0;

[0073] The constraints can also be:

[0074] X′ min ≤X′≤X′ max

[0075] Where X' is an integer, min is the lower limit of the key parameter, X′ max It is the upper limit value of the key parameter, that is, when the key parameter is optimized and adjusted through the square resistance result prediction model, the value of the key parameter cannot be greater than the upper limit value of the key parameter set by the low-pressure boron diffusion equipment, and cannot be less than the lower limit value of the key parameter set by the low-pressure boron diffusion equipment.

[0076] The present application discloses a method for optimizing square resistance of a low-voltage boron diffusion device, comprising collecting parameter data of several temperature zones and square resistance data of several boats from the low-voltage boron diffusion device; calculating several key parameters according to the parameter data and the square resistance data; constructing a square resistance result prediction model according to the square resistance data and the key parameters; optimizing the square resistance result prediction model, and obtaining the value of the key parameter when the output of the square resistance result prediction model is within a first threshold range. The method can improve the square resistance uniformity of photovoltaic cells and improve the yield and production efficiency of photovoltaic cells by optimizing the process parameters of the low-voltage boron diffusion device.

[0077] Corresponding to the aforementioned application function realization method embodiment, the present application also provides a square resistance optimization device of a low-voltage boron diffusion device, an electronic device and corresponding embodiments.

[0078] Figure 2 It is a structural schematic diagram of a square resistance optimization device for a low-voltage boron diffusion device shown in an embodiment of the present application.

[0079] See also Figure 2 , a sheet resistance optimization device 200 for a low-voltage boron diffusion device, comprising:

[0080] The acquisition module 210 is used to acquire parameter data of several temperature zones and square resistance data of several boats from the production equipment.

[0081] Specifically, a plurality of temperature zones and a plurality of boats are provided in the low-pressure boron diffusion equipment. The temperature may be a processing area of ​​the material. The temperature zone has a plurality of parameters that affect the process. The parameter data of the plurality of temperature zones may be obtained, and the parameter data may include data of a plurality of parameters. The square resistance data of the material may be obtained from the boat, and the square resistance data may be square resistance detection data of each material.

[0082] The processing module 220 is used to calculate several key parameters according to the parameter data and the square resistance data.

[0083] Specifically, several key parameter data can be calculated based on the acquired parameter data and square resistance data. There can be multiple key parameters, and the key parameters can be used to indicate that the parameters have a greater impact on the square resistance data. By adjusting the key parameters, the square resistance data of the boat can be better adjusted.

[0084] The construction module 230 is used to construct a square resistance result prediction model according to the square resistance data and key parameters.

[0085] Specifically, the square resistance result prediction model can be:

[0086]

[0087] Among them, A m*a is the coefficient matrix, the size of the matrix is ​​m rows and 5 columns, X′ T is the transposed matrix of the key parameters, C is the error coefficient matrix, and the optimized key parameters can be output through the square resistance result prediction model.

[0088] The optimization module 240 is used to optimize the square resistance result prediction model and obtain the value of the key parameter when the output of the square resistance result prediction model is within the first threshold range.

[0089] Specifically, the square resistance result prediction model can be optimized, that is, by adjusting the values ​​of key parameters to obtain the values ​​of key parameters when the output of the square resistance result prediction model is at a first threshold. The first threshold can be set according to actual needs and is not limited here.

[0090] The present application discloses a square resistance optimization device for a low-voltage boron diffusion device, comprising collecting parameter data of several temperature zones and square resistance data of several boats from the low-voltage boron diffusion device; calculating several key parameters based on the parameter data and the square resistance data; constructing a square resistance result prediction model based on the square resistance data and the key parameters; optimizing the square resistance result prediction model, and obtaining the value of the key parameter when the output of the square resistance result prediction model is within a first threshold range. The device can improve the square resistance uniformity of photovoltaic cells and improve the yield and production efficiency of photovoltaic cells by optimizing the process parameters of the low-voltage boron diffusion device.

[0091] 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.

[0092] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0093] See also Figure 3 , the electronic device 300 includes a memory 310 and a processor 320.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 method for optimizing the sheet resistance of a low-voltage boron diffusion device, characterized in that: include: Collect parameter data of several temperature zones and square resistance data of several boats from a low-pressure boron diffusion device; Calculating and obtaining several key parameters according to the parameter data and the square resistance data; Constructing a square resistance result prediction model according to the square resistance data and the key parameters; The square resistance result prediction model is optimized, and when the output of the square resistance result prediction model is within a first threshold range, a value of a key parameter is obtained.

2. The method according to claim 1, characterized in that The parameter data of several temperature zones and the square resistance data of several boats are collected from the low-pressure boron diffusion equipment, including: Performing multiple square resistance tests on each of the boats to obtain multiple target square resistance data of each boat; A validity check is performed on the target square resistance data, and the target square resistance data that passes the validity check is used as the square resistance data of the boat.

3. The method according to claim 1, characterized in that The parameter data includes: temperature data of the temperature zone, air flow data of the temperature zone, process time data of the temperature zone, pressure data of the temperature zone, and humidity data of the temperature zone.

4. The method according to claim 1, characterized in that: According to the parameter data and the square resistance data, several key parameters are calculated, including: According to the square resistance data, calculating the square resistance average value of the square resistance data of each of the boats; Calculate the first parameter average value of the parameter data of each temperature zone according to the parameter data; Several key parameters are calculated based on the average value of the square resistance, the average value of the first parameter and a preset algorithm.

5. The method according to claim 4, characterized in that According to the square resistance average value, the parameter average value and a preset algorithm, several key parameters are calculated, including: According to the parameter data, obtaining a second parameter average value of each parameter in the temperature zone in each process step; According to the square resistance average value, the first parameter average value, the second parameter average value and the preset algorithm, several key parameters are calculated, and the key parameters are selected from the second parameter average value.

6. The method according to claim 1, characterized in that The square resistance result prediction model is: f(X′)=A m *5X ′T +C Wherein, f(X′) is the square resistance result prediction model, X′ is the key parameter, A m*5 is the coefficient matrix, m is the number of boats, X′ T is the transposed matrix of the key parameters, and C is a preset constant.

7. A device for optimizing the square resistance of a low-voltage boron diffusion device, characterized in that: include: The acquisition module is used to collect parameter data of several temperature zones and square resistance data of several boats from the production equipment; A processing module, used for calculating a plurality of key parameters according to the parameter data and the square resistance data; A construction module, used to construct a square resistance result prediction model according to the square resistance data and the key parameters; The optimization module is used to optimize the square resistance result prediction model and obtain the value of the key parameter when the output of the square resistance result prediction 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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