Crystal growth method and system, apparatus, and storage medium

Abstract

A crystal growth method and system, an apparatus, and a storage medium. The crystal growth method comprises: determining a parameter set of a crystal growth device in a target heat, wherein the parameter set comprises at least one target parameter, and the target parameter is a parameter, affecting crystal growth, in the crystal growth device; acquiring a first temperature measurement value of a first temperature measurement point of the crystal growth device in a target time period of the target heat; and on the basis of the target parameter and the first temperature measurement value, by means of a crystal plane temperature determination model, determining a simulated crystal plane temperature corresponding to the first temperature measurement value.

Description

Crystal growth method, system, device and storage medium Technical Field

[0001] This specification relates to the field of crystal preparation, and in particular to a crystal growth method, system, device and storage medium. Background Art

[0002] With the development of science and technology, crystals (such as silicon carbide) are widely used in various optoelectronic devices and electronic devices. As the demand for crystals gradually increases, how to improve the quality of crystal growth has also become a focus of attention in the field.

[0003] During the crystal growth process, the crystal surface temperature is the core factor of the crystal growth quality, but it is difficult to observe the crystal surface temperature during the crystal growth process, resulting in the inability to accurately control the crystal surface temperature during the crystal growth process and the low crystal growth quality.

[0004] Therefore, how to improve the quality of crystal growth is a technical problem that needs to be solved urgently in this field.

[0005] Summary of the Invention

[0006] One of the embodiments of the present specification provides a crystal growth method, which includes: determining a parameter set of a crystal growth device in a target furnace, wherein the parameter set includes at least one target parameter, and the target parameter is a parameter in the crystal growth device that affects crystal growth; obtaining a first temperature detection value of a first temperature measuring point of the crystal growth device in a target time period of the target furnace; and determining a simulated crystal surface temperature corresponding to the first temperature detection value through a crystal surface temperature determination model based on the target parameter and the first temperature detection value.

[0007] In some embodiments, the method further includes: determining temperature control parameters of the crystal growth equipment in a target time period of a target furnace based on the simulated crystal surface temperature.

[0008] In some embodiments, the method further includes: automatically sending a temperature adjustment instruction based on the temperature control parameter to adjust the crystal growth temperature of the crystal growth device in the target time period.

[0009] In some embodiments, the target parameters include a target first parameter and a target second parameter, the target first parameter includes the first parameter of the preset object in the crystal growth equipment in the target furnace, the first parameter reflects the physical properties of the material in the preset object related to crystal growth, the target second parameter includes the second parameter of the crystal growth equipment in the target furnace, and the second parameter includes parameters related to the settings of the crystal growth equipment.

[0010] In some embodiments, the material physical properties include at least one of thermal conductivity, electrical conductivity, surface emissivity, thermal conductivity, constant-pressure heat capacity, and relative magnetic permeability.

[0011] In some embodiments, the preset object includes at least one of a crucible, a thermal insulation felt, a raw material melt, and a protective gas.

[0012] In some embodiments, determining the parameter set of the crystal growth equipment in the target furnace includes: obtaining initial first parameters, wherein the initial first parameters reflect the first parameters of the preset object in the crystal growth equipment in the initial environment; processing the initial first parameters based on a reinforcement learning model to determine the target first parameters, and the reward value of the reinforcement learning model is related to the furnace information of the crystal growth equipment.

[0013] In some embodiments, determining the parameter set of the crystal growth equipment in the target furnace includes: obtaining a first correspondence between the furnace of the crystal growth equipment and the first parameter; and determining the target first parameter based on the target furnace and the first correspondence.

[0014] In some embodiments, the first correspondence is determined by the following steps: for each of a plurality of preset furnaces of the crystal growth equipment, the initial first parameter is processed based on a reinforcement learning model to determine a reference first parameter corresponding to the preset furnace, the initial first parameter reflects the first parameter of the preset object in the crystal growth equipment in the initial environment, the reference first parameter is the first parameter of the crystal growth equipment in the preset furnace, and the reward value of the reinforcement learning model is related to the furnace information of the crystal growth equipment; based on the reference first parameters corresponding to a plurality of the preset furnaces, the first correspondence between the furnace of the crystal growth equipment and the first parameter is determined.

[0015] In some embodiments, determining the parameter set of the crystal growth equipment in the target furnace includes: obtaining the historical first parameters of the crystal growth equipment in multiple historical furnaces and the historical operating condition parameters corresponding to the historical first parameters, wherein the historical first parameters include the first parameters reflecting the preset object in the crystal growth equipment in the historical furnace; determining the target first parameters based on the historical first parameters of multiple historical furnaces and the historical operating condition parameters.

[0016] In some embodiments, the historical operating condition parameters include the second temperature detection value of the crystal growth equipment at the second temperature measurement point of multiple historical furnaces; determining the first parameter based on the historical first parameters of multiple historical furnaces and the historical operating condition parameters includes: determining a second corresponding relationship based on the historical first parameters of multiple historical furnaces and the second temperature detection value, wherein the second corresponding relationship reflects the corresponding relationship between the temperature detection value of the second temperature measurement point of the crystal growth equipment and the first parameter; obtaining the third temperature detection value of the crystal growth equipment at the second temperature measurement point of the target furnace; and determining the target first parameter based on the third temperature detection value and the second corresponding relationship.

[0017] In some embodiments, the historical operating condition parameters include the historical growth time of the crystal growing equipment in the historical furnaces for growing crystals; the determining of the target first parameter based on the historical first parameters of the multiple historical furnaces and the historical operating condition parameters includes: determining a third corresponding relationship based on the historical first parameters of the multiple historical furnaces and the historical growth time, the third corresponding relationship reflecting the corresponding relationship between the growth time of the crystal and the first parameter; obtaining the target growth time of the crystal growing equipment in the target furnace for growing crystals; and determining the target first parameter based on the target growth time and the third corresponding relationship.

[0018] In some embodiments, determining the target first parameter based on the historical first parameters of multiple historical furnaces and the historical operating parameters includes: establishing a parameter prediction model based on the historical first parameters of multiple historical furnaces and the historical operating parameters; determining the preset operating parameters of the crystal growth equipment in the target furnace; and determining the target first parameter based on the preset operating parameters and the parameter prediction model.

[0019] In some embodiments, the type of the first parameter is determined by the following steps: obtaining multiple sets of reference data, each set of the reference data includes multiple candidate first parameters and verification information corresponding to the multiple candidate first parameters, each of the candidate first parameters reflects a material physical property of the preset object in the crystal growth equipment; processing the multiple sets of reference data through an importance analysis model to determine the type of the first parameter, and the importance analysis model is a random forest model.

[0020] In some embodiments, determining the temperature control parameters of the crystal growth equipment in the target time period of the target furnace based on the simulated crystal surface temperature includes: obtaining multiple reference crystal surface temperatures of each of multiple reference equipment in the reference furnace and multiple reference control parameters corresponding to the multiple reference crystal surface temperatures, wherein the reference equipment is a device of the same type as the crystal growth equipment; determining the temperature control parameters based on the reference crystal surface temperatures of multiple reference equipment in the reference furnace, the reference control parameters and the simulated crystal surface temperatures.

[0021] In some embodiments, determining the temperature control parameters based on the multiple reference crystal surface temperatures, the multiple reference control parameters and the simulated crystal surface temperature includes: obtaining crystal detection data of the crystals generated by the multiple reference devices in the reference furnace; and determining the temperature control parameters based on the reference crystal surface temperatures, the reference control parameters, the crystal detection data and the simulated crystal surface temperature of the multiple reference devices in the reference furnace.

[0022] One of the embodiments of the present specification provides a crystal growth system, which includes: a parameter determination module for determining a parameter set of a crystal growth device in a target furnace, wherein the parameter set includes at least one target parameter, and the target parameter is a parameter in the crystal growth device that affects crystal growth; a temperature detection module for obtaining a first temperature detection value of a first temperature measuring point of the crystal growth device in a target time period of the target furnace; and a temperature determination module for determining a simulated crystal surface temperature corresponding to the first temperature detection value through a crystal surface temperature determination model based on the target parameter and the first temperature detection value.

[0023] In some embodiments, the system further includes: a temperature control module for determining temperature control parameters of the crystal growth equipment in a target time period of a target furnace based on the simulated crystal surface temperature.

[0024] In some embodiments, the system further includes: a temperature adjustment module for automatically sending a temperature adjustment instruction based on the temperature control parameter to adjust the crystal growth temperature of the crystal growth equipment in a target time period of a target furnace.

[0025] In some embodiments, the target parameters include a target first parameter and a target second parameter, the target first parameter includes the first parameter of the preset object in the crystal growth equipment in the target furnace, the first parameter reflects the physical properties of the material in the preset object related to crystal growth, the target second parameter includes the second parameter of the crystal growth equipment in the target furnace, and the second parameter includes parameters related to the settings of the crystal growth equipment.

[0026] In some embodiments, the parameter determination module is further used to: obtain an initial first parameter, wherein the initial first parameter reflects the first parameter of the preset object in the crystal growth equipment in the initial environment; process the initial first parameter based on a reinforcement learning model to determine the target first parameter, and the reward value of the reinforcement learning model is related to the furnace information of the crystal growth equipment.

[0027] In some embodiments, the parameter determination module is further used to: obtain a first correspondence between the furnace of the crystal growth equipment and the first parameter; and determine the target first parameter based on the target furnace and the first correspondence.

[0028] In some embodiments, the parameter determination module is also used to: for each of multiple preset furnaces of the crystal growth equipment, process the initial first parameter based on the reinforcement learning model to determine the reference first parameter corresponding to the preset furnace, the initial first parameter reflects the first parameter of the preset object in the crystal growth equipment in the initial environment, the reference first parameter is the first parameter of the crystal growth equipment in the preset furnace, and the reward value of the reinforcement learning model is related to the furnace information of the crystal growth equipment; based on the reference first parameters corresponding to multiple preset furnaces, determine the first correspondence between the furnace of the crystal growth equipment and the first parameter.

[0029] In some embodiments, the parameter determination module is further used to: obtain the historical first parameters of the crystal growth equipment in multiple historical furnaces and the historical operating condition parameters corresponding to the historical first parameters, wherein the historical first parameters include the first parameters reflecting the preset object in the crystal growth equipment in the historical furnace; determine the target first parameter based on the historical first parameters of multiple historical furnaces and the historical operating condition parameters.

[0030] In some embodiments, the historical operating condition parameters include the second temperature detection value of the crystal growth equipment at the second temperature measurement point of multiple historical furnaces; the parameter determination module is further used to: determine a second corresponding relationship based on the historical first parameters and the second temperature detection value of multiple historical furnaces, wherein the second corresponding relationship reflects the corresponding relationship between the temperature detection value of the second temperature measurement point of the crystal growth equipment and the first parameter; obtain the third temperature detection value of the crystal growth equipment at the second temperature measurement point of the target furnace; and determine the target first parameter based on the third temperature detection value and the second corresponding relationship.

[0031] In some embodiments, the historical operating condition parameters include the historical growth time of the crystal growth equipment in the historical furnaces for growing crystals; the parameter determination module is further used to: determine a third corresponding relationship based on the historical first parameters and the historical growth time of multiple historical furnaces, and the third corresponding relationship reflects the corresponding relationship between the growth time of the crystal and the first parameter; obtain the target growth time of the crystal growth equipment in the target furnace for growing crystals; and determine the target first parameter based on the target growth time and the third corresponding relationship.

[0032] In some embodiments, the parameter determination module is further used to: establish a parameter prediction model based on the historical first parameters and the historical operating parameters of multiple historical furnaces; determine the preset operating parameters of the crystal growth equipment in the target furnace; and determine the target first parameter based on the preset operating parameters and the parameter prediction model.

[0033] In some embodiments, the parameter determination module is also used to: obtain multiple sets of reference data, each set of the reference data includes multiple candidate first parameters and verification information corresponding to the multiple candidate first parameters, each of the candidate first parameters reflects a material physical property of the preset object in the crystal growth equipment; process the multiple sets of reference data through an importance analysis model to determine the type of the first parameter, and the importance analysis model is a random forest model.

[0034] In some embodiments, the temperature control module is further used to: obtain multiple reference crystal surface temperatures of each of the multiple reference devices in the reference furnace and multiple reference control parameters corresponding to the multiple reference crystal surface temperatures, wherein the reference device is a device of the same type as the crystal growth device; determine the temperature control parameters based on the reference crystal surface temperatures of the multiple reference devices in the reference furnace, the reference control parameters and the simulated crystal surface temperatures.

[0035] In some embodiments, the temperature control module is further used to: obtain crystal detection data of crystals generated by multiple reference devices in the reference furnace; and determine the temperature control parameters based on the reference crystal surface temperature, the reference control parameters, the crystal detection data and the simulated crystal surface temperature of multiple reference devices in the reference furnace.

[0036] One of the embodiments of this specification provides a crystal growth apparatus, including a crystal growth device and a processor, wherein the processor is configured to execute the crystal growth method as described in any one of the above embodiments.

[0037] One of the embodiments of this specification provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the crystal growth method as described in any one of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0039] FIG1 is a schematic diagram of an application scenario of a crystal growth system according to some embodiments of this specification;

[0040] FIG2 is an exemplary flow chart of a crystal growth method according to some embodiments of the present specification;

[0041] FIG3 is an exemplary flowchart of determining a first parameter according to some embodiments of this specification;

[0042] FIG4 is a schematic diagram of a reinforcement learning model according to some embodiments of this specification;

[0043] FIG5 is another exemplary flowchart of determining a first target parameter according to some embodiments of this specification;

[0044] FIG6 is another exemplary flowchart of determining a first target parameter according to some embodiments of this specification;

[0045] FIG. 7 is an exemplary module diagram of a crystal growth system according to some embodiments of the present specification. DETAILED DESCRIPTION

[0046] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0047] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0048] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0049] This specification uses flowcharts to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0050] FIG1 is a schematic diagram of an application scenario of a crystal growth system according to some embodiments of this specification.

[0051] As shown in FIG. 1 , an application scenario 100 of a crystal growth system may include a crystal growth device 110 , a temperature detection device 120 , a network 130 , a storage device 140 , and a processor 150 .

[0052] Crystal growth equipment 110 is a device used for growing crystals. The aforementioned crystals may include, but are not limited to, silicon carbide, germanium single crystals, and the like. Crystal growth equipment 110 may include, but is not limited to, physical vapor transport crystal growth equipment, liquid phase epitaxy crystal growth equipment, and Czochralski crystal growth equipment. Crystal growth equipment 110 may include, among other structures, a furnace, a crucible, an insulation layer, and a seed crystal holder. It is understood that any of the aforementioned crystal growth methods can be used to determine the crystal surface temperature during crystal growth using any of the crystal growth methods described in any of the embodiments herein to complete crystal growth.

[0053] Temperature detection device 120 is a device for detecting temperature. It may include, but is not limited to, an infrared thermal imager, a fiber optic temperature sensor, and the like. Temperature detection device 120 may be installed in crystal growth equipment 110 to obtain a first temperature measurement value at a first temperature measurement point in crystal growth equipment 110 during a target time period of a target heat. For more information about the target heat, target time period, first temperature measurement point, and first temperature measurement value, please refer to FIG. 2 and its related description.

[0054] The network 130 can connect the various components of the application scenario 100 of the crystal growth system and / or connect to external resources. The network 130 enables communication between the various components and with other components, facilitating the exchange of data and / or information. For example, the processor 150 can obtain the first temperature detection value detected by the temperature detection device 120 through the network 130. For another example, the processor 150 can also control the crystal growth device 110 through the network 130. The network 130 can be any one or more of a wired network or a wireless network. For example, the network 130 can include a cable network, a fiber optic network, a telecommunications network, the Internet, a local area network, a wide area network, a wireless local area network, a metropolitan area network, a public switched telephone network, a Bluetooth network, a ZigBee network, near-field communication, an in-device bus, an in-device line, a cable connection, or any combination thereof. The network connection between the various components can adopt one of the above methods or multiple methods. The network can be a point-to-point, shared, centralized, or other topological structure or a combination of multiple topological structures.

[0055] Storage device 140 can be used to store data and / or instructions. For example, storage device 140 can be used to store historical data, device information, etc. of crystal growth device 110. Storage device 140 may include one or more storage components, each of which may be a standalone device or part of another device. Storage device 140 may include random access memory, read-only memory, mass storage, removable memory, volatile read-write memory, etc., or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state disk, etc. In some embodiments, storage device 140 may be implemented on a cloud platform.

[0056] The processor 150 can process data and / or information obtained from external resources or various components of the application scenario 100 of the crystal growth system. For example, the processor 150 can determine the parameter set of the crystal growth device 110 in the target furnace. The processor can execute program instructions based on these data, information and / or processing results to perform one or more functions described in this specification. For example, the processor 150 can determine the simulated crystal surface temperature corresponding to the first temperature detection value through the crystal surface temperature determination model based on the target parameter and the first temperature detection value. For another example, the processor 150 can also determine the temperature control parameters of the crystal growth device 110 in the target time period of the target furnace based on the simulated crystal surface temperature. For more information about the above examples, please refer to the following in this specification. The processor 150 may include one or more sub-processing devices (for example, a single-core processing device or a multi-core multi-core processing device). By way of example only, the processor 150 may include a central processing unit, an application-specific integrated circuit, a dedicated instruction processor, a graphics processor, a physical processor, a digital signal processor, a field programmable gate array, an editable logic circuit, a controller, a microcontroller unit, a reduced instruction set computer, a microprocessor, or any combination thereof.

[0057] It should be noted that the application scenario 100 of the crystal growth system is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those skilled in the art, various modifications or variations can be made based on the description of this specification. For example, the application scenario 100 of the crystal growth system can also include a database, an information source, etc. For another example, the application scenario 100 of the crystal growth system can be implemented on other devices to achieve similar or different functions. However, changes and modifications will not deviate from the scope of this specification.

[0058] FIG2 is an exemplary flow chart of a crystal growth method according to some embodiments of the present disclosure. In some embodiments, process 200 can be performed by a crystal growth system 700. As shown in FIG2 , process 200 includes the following steps:

[0059] Step 210 , determining a parameter set of the crystal growth equipment for the target heat. In some embodiments, step 210 may be performed by parameter determination module 710 .

[0060] The target heat refers to the current heat of crystal growth performed by the crystal growth apparatus (e.g., the crystal growth apparatus 110 shown in FIG1 ). For example, the target heat may be the 20th heat, indicating that the crystal growth apparatus is currently performing the 20th heat of crystal growth.

[0061] The parameter set is a collection of target parameters of the crystal growth equipment in a target furnace, and the parameter set may include at least one target parameter.

[0062] The target parameters are parameters that affect crystal growth in the crystal growth apparatus. Specifically, the target parameters may affect the speed of crystal growth, the quality of the grown crystal, etc. In some embodiments, the target parameters may include a target first parameter and a target second parameter.

[0063] The target first parameter may include a first parameter of a preset object within the crystal growth apparatus during a target heat. The preset object may include at least one of a crucible, insulation felt (e.g., soft felt and / or hard felt), a raw material melt, and a protective gas (e.g., argon). The first parameter may reflect a physical property of the material in the preset object that is relevant to crystal growth.

[0064] In some embodiments, the parameter determination module 710 can determine the type of the first parameter. It is understood that the material physical properties of the object include various parameters. For example, the material physical properties of the object may include parameters related to the object's density, optical properties, mechanical properties, thermal properties, electrical properties, and magnetic properties. However, changes in some material physical properties (e.g., mechanical properties) of the object may have a greater impact on the crystal surface temperature, while changes in some material physical properties (e.g., thermal properties) of the object may have a smaller impact on the crystal surface temperature. In some embodiments, the parameter determination module 710 may determine the material physical properties of the object that have a greater impact on the crystal surface temperature when changed as the first parameter, and detect the target first parameter in the target heat of the crystal growth equipment using the aforementioned embodiments, thereby reducing computational complexity and avoiding waste of computing resources. In some embodiments, the first parameter may include at least one of the parameters related to the object's thermal properties, electrical properties, and magnetic properties. For example, the first parameter may include at least one of thermal conductivity, electrical conductivity, surface emissivity, thermal conductivity, constant-pressure heat capacity, and relative magnetic permeability.

[0065] In some embodiments, the parameter determination module 710 can obtain multiple sets of reference data. The reference data can be data used to determine the type of the first parameter. Each set of reference data may include multiple candidate first parameters and verification information corresponding to the aforementioned multiple candidate first parameters. The candidate first parameter can be a parameter of the first parameter to be evaluated for determination. The candidate first parameter can include all material physical properties of the preset object or some user-specified material physical properties. Each candidate first parameter can reflect a material physical property of the preset object in the crystal growth equipment. Verification information is information used to verify the importance of the first parameter. For example, verification information may include but is not limited to furnace temperature, furnace pressure, crystal growth quality, etc. Reference data can be obtained through historical data of the crystal growth equipment. For example, multiple candidate first parameters and verification information in a certain set of reference data can be obtained by measuring the preset object of the crystal growth equipment in historical furnaces.

[0066] In some embodiments, the parameter determination module 710 may process multiple sets of reference data using an importance analysis model to determine the type of the first parameter, where the importance analysis model is a random forest model. For example, the parameter determination module 710 may process multiple sets of reference data using an importance analysis model to determine four types of first parameters from 20 material physical properties of predetermined objects, namely, surface emissivity, thermal conductivity, constant-pressure heat capacity, and relative magnetic permeability.

[0067] The importance analysis model can analyze the multiple candidate first parameters input through the input verification information to determine the importance of each candidate first parameter, thereby determining the type of the first parameter. The importance of each candidate first parameter can be expressed in a variety of ways, such as numerical values, ratios, etc. Exemplarily, the parameter determination module 710 can determine the type of the candidate first parameter whose importance exceeds a preset importance threshold as the type of the first parameter. Some embodiments of this specification screen multiple candidate first parameters through a random forest model to determine the type of the first parameter, avoid interference from the physical properties of the material in the preset object that has little effect on crystal growth, and reduce the amount of calculation of the crystal growth system 700.

[0068] In some embodiments, the parameter determination module 710 may determine the target first parameters of the crystal growth equipment for the target heat based on the initial first parameters corresponding to the preset object. For example, the parameter determination module 710 may determine the target first parameters of the crystal growth equipment for the target heat based on equipment information of the crystal growth equipment. The aforementioned equipment information may include relevant information about the crystal growth equipment, and the equipment information may include but is not limited to the type of crystal growth equipment, overall dimensions, dimensions of the preset object, initial first parameters, etc. The equipment information may be provided by the manufacturer of the crystal growth equipment. For more information about the initial first parameters, see Figure 4 and its related description.

[0069] In some embodiments, the parameter determination module 710 can determine the specific value of the target first parameter. It is worth noting that when in a room temperature state, the first parameter is relatively stable. For example, the electrical conductivity can be a fixed value. However, when the crystal grows in a crystal growth device, the crystal growth device will induction heat the crucible through an induction coil. Therefore, the temperature of the crystal growth environment is relatively high (for example, about 900 to 7200°C) and is located in an electric field and a magnetic field. At this time, the first parameter may change according to the different environments in the furnace of the crystal growth device. In addition, when the crystal growth device is in use, the material properties of the preset object may also change with use, thereby causing the first parameter to change. Based on this, in order to more accurately determine the crystal surface temperature of the crystal during the growth process, the parameter determination module 710 can determine the target first parameter of the crystal growth device in the target furnace by a variety of methods, that is, the specific value of the first parameter in the target furnace.

[0070] In some embodiments, parameter determination module 710 may obtain initial first parameters, where the initial first parameters reflect first parameters of a preset object within the crystal growth apparatus in an initial environment; process the initial first parameters based on a reinforcement learning model to determine target first parameters, where a reward value of the reinforcement learning model is related to information within the crystal growth apparatus furnace. For further details on the aforementioned embodiments, see FIG3 and its related description.

[0071] In some embodiments, the parameter determination module 710 may further obtain a first correspondence between a heat of the crystal growth equipment and the first parameter; and determine a target first parameter based on the target heat and the first correspondence. For more details on the aforementioned embodiments, see FIG. 4 and its related description.

[0072] In some embodiments, parameter determination module 710 may further obtain historical first parameters of the crystal growth equipment in multiple historical furnaces and historical operating condition parameters corresponding to the historical first parameters, wherein the historical first parameters include first parameters reflecting the first parameters of a preset object within the crystal growth equipment in the historical furnaces; and determine the target first parameter based on the historical first parameters and historical operating condition parameters of the multiple historical furnaces. For more information on the aforementioned embodiments, see FIG5 and its related description.

[0073] The target second parameter may include a second parameter of the crystal growth equipment in the target heat. The second parameter may include a parameter of the crystal growth equipment set in the target heat.

[0074] In some embodiments, parameter determination module 710 may determine the type of the second parameter by presetting and further determine the target second parameter value for the target heat. The second parameter may include heat equipment parameters and heat growth parameters. The aforementioned heat equipment parameters may be parameters related to the crystal growth equipment. For example, heat equipment parameters may include, but are not limited to, the number of top insulation layers, the distance between the top insulation bottom and the crucible top, the number of soft felts, the minimum inner diameter, the maximum outer diameter, the lid thickness, the charge spacing, the gasket inner diameter, the charge height, the bottom insulation soft felt, the inner insulation tube hard felt, the inner insulation tube soft felt, the outer insulation tube hard felt, the outer insulation tube soft felt, the outermost layer of soft felt, and the ring height. The heat growth parameters may be parameters related to the crystal growth settings. For example, the heat growth parameters may include growth pressure, growth power, high temperature temperature, high temperature line, crystal growth time, center thickness, edge thickness, and the like. Different heats of crystal growth equipment may have different corresponding second parameters due to differences in the type of crystals produced and the crystal requirements.

[0075] In some embodiments, the target second parameter can be obtained by presetting the target heat. For example, the target second parameter can be determined by a user (e.g., a crystal growth equipment manager) inputting the heat equipment parameters and heat growth parameters of the target heat. For another example, the heat equipment parameters of the crystal growth equipment for the target heat can be determined based on a pre-set crystal growth equipment type. For another example, the heat growth parameters of the crystal growth equipment for the target heat can be determined based on pre-set crystal types, crystal requirements, etc.

[0076] Step 220 , obtaining a first temperature detection value at a first temperature measurement point of the crystal growth equipment during a target time period of the target heat. In some embodiments, step 220 may be performed by the temperature detection module 720 .

[0077] The target time period refers to the time period during which the temperature at the first temperature measurement point is measured. The target time period can be pre-set by a user (e.g., an operator of a crystal growth apparatus). A target heat can include multiple time periods, and the target time period can be the current time period.

[0078] The temperature detection module 720 can perform at least one temperature detection within a target time period to obtain a first temperature detection value of a first temperature measurement point of the crystal growth device. The temperature detection module 720 can be implemented by a temperature detection device (e.g., the temperature detection device 120 shown in FIG1 ).

[0079] The first temperature measurement point is a temperature detection point in the crystal growth equipment (for example, in the furnace of the crystal growth equipment), and the first temperature detection value is the temperature of the first temperature measurement point obtained by detection.

[0080] The crystal growth apparatus may include one or more first temperature measurement points. The first temperature measurement points may be pre-set by the user. The user may install a temperature detection device at one or more locations, and the temperature detection module 720 may obtain a first temperature detection value at the first temperature measurement point during a target time period for a target heat batch using the temperature detection device.

[0081] Step 230 : Based on the target parameter and the first temperature detection value, determine the simulated crystal surface temperature corresponding to the first temperature detection value using the crystal surface temperature determination model. In some embodiments, step 230 may be performed by the temperature determination module 730 .

[0082] The simulated crystal surface temperature refers to a crystal surface temperature corresponding to the first temperature detection value determined through simulation.

[0083] The crystal surface temperature determination model can be a numerical simulation model. The processor 150 can establish multiple mathematical equations based on the historical target parameters of multiple historical furnaces, the historical first temperature detection chamber, and the historical crystal surface temperature, and discretize the multiple mathematical equations, converting them into discrete forms for easy processing, and then using numerical calculation methods, such as finite difference method, finite element method, etc., to solve the aforementioned discretized mathematical equations to obtain the crystal surface temperature determination model. The temperature determination module 730 can input the target parameters and the first temperature detection value into the crystal surface temperature determination model, and the crystal surface temperature determination model performs simulation based on the input data to determine the simulated crystal surface temperature.

[0084] Some embodiments of this specification can use a crystal surface temperature determination model to simulate and determine the simulated crystal surface temperature, thereby solving the problem that the crystal surface temperature is difficult to observe.

[0085] In some embodiments, the crystal surface temperature determination model can also be a machine learning model. For example, the crystal surface temperature determination model can include but is not limited to one or a combination of a logistic regression model, a decision tree model, a stochastic gradient descent model, etc. The input of the crystal surface temperature determination model can include target parameters and a first temperature detection value, and the output can include a simulated crystal surface temperature corresponding to the first temperature detection value. The aforementioned crystal surface temperature determination model can be obtained by training after modeling the crystal growth equipment using numerical simulation software (for example, Virtual reactor, COMSOL, etc.) or a designed heat transfer calculation program to obtain an initial crystal surface temperature determination model. The first training sample can include the sample target parameter and the sample first temperature detection value, and the first training label can include the sample crystal surface temperature. The aforementioned first training sample and the first training label can be obtained by performing a heating experiment on the crystal growth equipment.

[0086] In some embodiments, the temperature determination module 730 may further determine a corresponding crystal surface temperature determination model based on the crystal growth equipment type. The crystal surface temperature determination model corresponding to each crystal growth equipment type may be obtained by training based on the first training sample and the first training label corresponding to each crystal growth equipment type.

[0087] In some embodiments, the input to the crystal surface temperature determination model may also include the location information of the first temperature measurement point. Accordingly, the first training sample during training may also include the sample location information of the first temperature measurement point. By also including the location information of the first temperature measurement point as input to the crystal surface temperature determination model, errors caused by differences in the location of the first temperature measurement point can be avoided, thereby improving the accuracy of the output of the crystal surface temperature determination model.

[0088] In some embodiments, the crystal surface temperature determination model may also include a numerical simulation model and a machine learning model. For example, the temperature determination module 730 may process the target parameter and the first temperature detection value based on the numerical simulation model and the machine learning model, and perform weighted summation on the output results of the two, and determine the result of the weighted summation as the simulated crystal surface temperature. Among them, the aforementioned numerical simulation model and machine learning model can be obtained by preset. Numerical simulation models are usually affected by uncertainties from different sources, such as model parameter uncertainty, noise in input data, etc. Machine learning models can be used to estimate and model uncertainty to provide more accurate prediction results and effective uncertainty quantification. By combining the output results of the two, a more accurate simulated crystal surface temperature can be obtained.

[0089] Some embodiments of this specification can more accurately determine the parameter set of crystal growth equipment for a target heat, thereby more accurately determining the crystal surface temperature during the crystal growth process and improving the quality of crystal growth. In addition, determining the crystal surface temperature using a crystal surface temperature determination model can improve judgment efficiency and reduce manual judgment costs.

[0090] In some embodiments, after determining the simulated crystal surface temperature, process 200 may further determine temperature control parameters for adjusting the temperature of the crystal growth equipment during the target time period. Optionally, process 200 may further include the following steps:

[0091] Step 240 , based on the simulated crystal surface temperature, determines the temperature control parameters of the crystal growth equipment in the target time period of the target furnace. In some embodiments, step 240 may be performed by the temperature control module 740 .

[0092] Temperature control parameters are parameters used by the crystal growth equipment to adjust the temperature within the furnace. Temperature control parameters can include the power of the furnace within the crystal growth equipment. For example, a temperature control parameter could be +5 kW, indicating that the current furnace power needs to be increased by 5 kW within the target time period. Temperature control parameters can also include the adjustment value of the furnace temperature within the crystal growth equipment. For example, a temperature control parameter could be +100°C, indicating that the current furnace temperature needs to be increased by 100°C within the target time period.

[0093] In some embodiments, the temperature control module 740 can feed back the simulated crystal surface temperature to the operator of the target furnace, and the aforementioned operator can determine the temperature control parameters of the crystal growth equipment in the target time period of the target furnace based on the simulated crystal surface temperature.

[0094] In some embodiments, the temperature control module 740 can determine the temperature control parameters of the crystal growth equipment in the target time period of the target furnace based on the crystal growth information of the target furnace and the simulated crystal surface temperature through the temperature control relationship. Crystal growth information can refer to relevant information of the crystal growth of the target furnace. For example, the crystal growth information can include but is not limited to crystal type, current crystal growth situation (such as size), current crystal growth time, target growth time, etc. The aforementioned target growth time refers to the time required for crystal growth in the target furnace. The crystal growth information can be directly determined by user input, or it can be obtained in real time during the crystal growth process. For example, the crystal type, target growth time, etc. can be directly determined by user input; for another example, the current crystal growth situation (such as crystal size) and the current crystal growth time can be obtained in real time during the crystal growth process.

[0095] Temperature control module 740 can determine the desired crystal surface temperature for the crystal within a target time period based on the crystal growth information, and determine the temperature control parameters for the crystal growth equipment within the target time period by combining the simulated crystal surface temperature with a preset control rule relationship. For more information on temperature control relationships, please refer to the relevant description below in this specification.

[0096] The temperature control module 740 can obtain multiple reference crystal surface temperatures of each reference device in a reference furnace and multiple reference control parameters corresponding to the multiple reference crystal surface temperatures. The reference device is a device of the same type as the crystal growth device, and the reference furnace is a crystal growth furnace performed by the reference device. The reference crystal surface temperature can refer to the crystal surface temperature of the reference device in the reference furnace, and the aforementioned reference crystal surface temperature can be a temperature sequence, representing the different crystal surface temperatures of the reference device in multiple time periods of the reference furnace. The reference control parameter can refer to the temperature control parameter of the reference device in the reference furnace, and the aforementioned reference crystal surface temperature can be a temperature control parameter sequence, representing the different temperature control parameters of the reference device in multiple time periods of the reference furnace.

[0097] In some embodiments, the temperature control module 740 may determine the temperature control parameters based on reference crystal surface temperatures of a plurality of reference devices in reference heats, reference control parameters, and simulated crystal surface temperatures.

[0098] For example, the temperature control module 740 can determine at least one reference furnace based on the simulated crystal surface temperature and the reference crystal surface temperature, and then determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace based on the reference control parameters corresponding to the at least one reference furnace. When there is only one reference furnace, the temperature control module 740 can directly determine the reference control parameters corresponding to the reference furnace as the temperature control parameters of the crystal growth equipment during the target time period of the target furnace. It is understandable that the reference control parameters corresponding to the same reference crystal surface temperature may be different in different reference furnaces. Therefore, when there are multiple reference furnaces, the temperature control module 740 can comprehensively evaluate the reference control parameters of multiple reference furnaces to determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace. For example, the average value or median value of the reference control parameters corresponding to the multiple reference furnaces can be determined and determined as the temperature control parameter of the crystal growth equipment during the target time period of the target furnace.

[0099] For example, the temperature control module 740 can determine a temperature control relationship between the temperature control parameters within the crystal growth equipment and the crystal surface temperature based on the reference crystal surface temperatures and reference control parameters of multiple reference equipment in reference furnaces. The temperature control relationship can represent the effect of adjusting the temperature control parameters on the crystal surface temperature.

[0100] In some embodiments, the temperature control relationship can be characterized by a temperature control model whose input may include a simulated crystal surface temperature from the start of crystal growth to a target time period, and whose output is a temperature control parameter from the target time period to the completion of crystal growth.

[0101] The temperature control model can be obtained by training multiple reference devices using reference crystal surface temperatures and reference control parameters from a reference furnace. The temperature control module 740 can block or remove the crystal surface temperatures during a portion of the reference crystal surface temperatures to obtain a second training sample. The second training label can be the reference control parameter corresponding to the blocked or removed time period. The temperature control module 740 can train the initial temperature control model based on the second training sample and the second training label to obtain the temperature control model.

[0102] The temperature control module 740 can determine the crystal surface temperature required by the crystal growth equipment in the target time period of the target furnace based on the crystal growth information of the target furnace. Based on the crystal surface temperature required by the crystal growth equipment in the target time period of the target furnace and the simulated crystal surface temperature, the temperature control parameters of the crystal growth equipment in the target time period of the target furnace are determined through the temperature control relationship so that the grown crystals meet the requirements.

[0103] The temperature control module 740 may also obtain crystal detection data for crystals generated by multiple reference devices in reference heats. The crystal detection data may refer to relevant detection data of the crystals. For example, the crystal detection data may include, but is not limited to, crystal size, number of crystals, presence of crystals, etc.

[0104] In some embodiments, the temperature control module 740 may determine the temperature control parameters based on the reference crystal surface temperatures, reference control parameters, crystal detection data, and simulated crystal surface temperatures of multiple reference devices in a reference furnace. The temperature control module 740 may screen the reference crystal surface temperatures and reference control parameters of multiple reference devices in the reference furnace based on the crystal growth information of the target furnace and the crystal detection data of multiple reference devices in the reference furnace, obtain the screened reference crystal surface temperatures and reference control parameters, and determine the temperature control parameters of the crystal growth device in the target time period of the target furnace based on the screened reference crystal surface temperatures, reference control parameters, and simulated crystal surface temperatures. For example, the temperature control module 740 may only retain the reference crystal surface temperatures and reference control parameters corresponding to the reference furnaces whose crystal sizes in the crystal detection data are equal to or greater than a preset size threshold. By screening the reference furnaces of the reference devices through the crystal detection data, data that does not meet the requirements can be screened out, so that the temperature control parameters of the target furnace in the target time period are more in line with the requirements of the crystal growth information.

[0105] It is worth noting that if the simulated crystal surface temperature is only fed back to the operator for reference, the operator still needs to set the temperature of the crystal growth furnace based on experience. This makes the monitoring of the crystal surface temperature less comprehensive and cannot ensure that the crystal surface temperature is constant during the crystal growth process, which may reduce the quality of crystal growth. Some embodiments of this specification determine the temperature control parameters by temperature control module 740, which can make the determined temperature control parameters more suitable for crystal growth, realize automated control, reduce labor costs, and reduce the rate of operational errors.

[0106] In some embodiments, the crystal growth system 200 may further adjust the crystal growth temperature of the crystal growth device during a target time period based on the temperature control parameter. Optionally, the process 200 may further include the following steps:

[0107] In step 250 , based on the temperature control parameters, a temperature adjustment instruction is automatically sent to adjust the crystal growth temperature of the crystal growth equipment during the target time period of the target furnace. In some embodiments, step 250 may be performed by the temperature adjustment module 750 .

[0108] Temperature adjustment instructions are control instructions for each component in the crystal growth equipment to adjust the temperature. Temperature adjustment module 750 can generate corresponding temperature adjustment instructions based on temperature control parameters and send the temperature adjustment instructions to the corresponding components in the crystal growth equipment to adjust the crystal growth temperature of the crystal growth equipment during the target time period of the target furnace, so that the crystal growth temperature meets the requirements of crystal growth and improves the quality of crystal growth.

[0109] For example, when the temperature control parameter includes the power of the furnace in the crystal growth equipment + 5KW, the temperature adjustment module 750 can convert it into a corresponding temperature adjustment instruction, and the aforementioned temperature adjustment instruction can be sent to the furnace; the furnace can increase the current power by 5KW in response to the temperature adjustment instruction.

[0110] Some embodiments of this specification can automatically control the temperature of crystal growth equipment through temperature control parameters, thereby realizing intelligent control of crystal growth and reducing the cost caused by manual operation.

[0111] It is worth noting that in the target furnace of the crystal growth equipment, the crystal growth system 700 can continuously execute process 200 to determine the crystal surface temperature in each time period, thereby realizing intelligent control of the crystal growth equipment based on the aforementioned crystal surface temperature and improving the quality of crystal growth.

[0112] It should be noted that the above description of process 200 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and alterations to process 200 under the guidance of this specification. However, such modifications and alterations remain within the scope of this specification. For example, the crystal growth system 700 may first perform step 220 and then perform step 210 to implement the crystal growth method described in the preceding embodiments of this specification.

[0113] FIG3 is an exemplary flow chart of determining a first parameter according to some embodiments of this specification. In some embodiments, process 300 may be performed by parameter determination module 710. As shown in FIG3 , process 300 may include the following steps:

[0114] Step 310: Obtain an initial first parameter.

[0115] The initial first parameter may reflect the first parameter of the preset object in the crystal growth device in the initial environment. The initial environment may refer to the environment when the manufacturer of the crystal growth device measures the first parameter of the preset object. At this time, the crystal growth device has not yet performed crystal growth. One or more of the temperature, magnetic field, electric field, etc. in the initial environment may be determined by preset. The initial first parameter may be determined by the device information of the crystal growth device. For example, the designer of the crystal growth device may test the preset object in the initial environment (e.g., a temperature of 20 to 30°C and a magnetic field of 1 to 2 milligauss) when producing the crystal growth device to determine the first parameter of the preset object under the initial conditions.

[0116] It is understandable that, as the crystal is generated, the environment in which the preset object is located will change relative to the initial environment (for example, the temperature in the initial environment is 20-30°C, and the temperature during crystal growth is 7400-7500°C). In addition, as the crystal growth equipment is used, the internal structure of the preset object may also change, thereby causing the first parameter of the preset object in the crystal growth equipment to continue to change.

[0117] Step 320: Process the initial first parameter based on the reinforcement learning model to determine the target first parameter.

[0118] For each initial first parameter, the parameter determination module 710 may input the initial first parameter into a reinforcement learning model, with the output of the reinforcement learning model being the corresponding target first parameter. The reward value of the reinforcement learning model may be related to the in-furnace information of the crystal growth equipment. The in-furnace information may be relevant information about the crystal growth equipment during the crystal growth process, including but not limited to the furnace temperature, furnace pressure, exhaust rate, etc. The in-furnace information may be determined based on a variety of methods. The in-furnace information may be determined based on detection. For example, the parameter determination module 710 may obtain the in-furnace information through detection using a relevant detection device. The in-furnace information may also be determined based on calculation. For example, the parameter determination module 710 may determine the in-furnace temperature based on the first temperature detection value, using a preset temperature correspondence between the first temperature detection value and the temperature. For another example, the parameter determination module 710 may determine the in-furnace temperature based on the target furnace and the power of the crystal growth equipment through calculation.

[0119] The reinforcement learning model can be used to modify the initial first parameter to obtain the modified target first parameter. The reinforcement learning model 420 includes an adjustment module 421 and an optimal action determination module 422.

[0120] As shown in Figure 4, parameter determination module 710 can input initial first parameter 410 into reinforcement learning model 420, and the output of reinforcement learning model 420 is target first parameter 430. Within reinforcement learning model 420, initial first parameter 410 is input into adjustment module 421, which outputs a set of optional actions. Initial first parameter 410 and the set of optional actions are then input into optimal action determination module 422, which outputs optimal optional action 423. Parameter determination module 710 can use the value corresponding to optimal optional action 423 output by optimal action determination module 422 as the output of reinforcement learning model 420, namely, target first parameter 430.

[0121] The adjustment module 421 may include an optional action determination submodule 421-1, a state determination submodule 421-2, and a reward determination submodule 421-3. During the prediction process of the reinforcement learning model 420, the adjustment module may determine the optional action set through the optional action determination submodule 421-1 based on the initial first parameter 410. It is worth noting that the target first parameter 430 finally determined by adjusting the initial first parameter 410 of the optional action set should not exceed the correction range of the initial first parameter 410. The aforementioned correction range refers to the range for correcting the initial first parameter. It is understandable that because the types of the various initial first parameters are different, the correction range corresponding to each initial first parameter may be different. The aforementioned correction range can be preset based on experience.

[0122] During the training process of the reinforcement learning model 420 , the state determination submodule 421 - 2 and the reward determination submodule 421 - 3 in the adjustment module 421 may be used to determine the first parameter of the next state and the reward value, respectively.

[0123] The optional action determination submodule 421-1 can determine a set of optional actions in the current state based on the first parameter of the current state (for example, during initial execution, the first parameter of the current state can be the initial first parameter 410). The optional action set refers to a set of actions that can be performed in a certain state based on the first parameter of the current state. It is worth noting that the adjusted first parameter should not exceed the correction range corresponding to the initial first parameter.

[0124] The state determination submodule 421 - 2 may determine the first parameter of the next state based on the first parameter of the current state and the optimal optional action 423 output by the optimal action determination module 422 .

[0125] The reward determination submodule 421-3 can be used to determine a reward value. The reward value can be used to determine the accuracy of the adjusted first parameter. For example, for actions with high accuracy, the reward value can be higher; for actions with low accuracy or negative improvement, the reward value can be lower. The reward value can be expressed in a numerical value or other manner. In some embodiments, the reward value of the reinforcement learning model can be related to the in-furnace information of the crystal growth equipment. Taking the first parameter as conductivity and the in-furnace information as the furnace temperature as an example, the parameter determination module 710 can input the adjusted first parameter into a simulation model. The output of the simulation model can be the simulated temperature of the crystal growth equipment corresponding to the current adjusted first parameter. The simulated temperature is compared with the in-furnace temperature of the crystal growth equipment to determine the reward value of the adjusted first parameter. The reward value can be determined based on the temperature difference between the simulated temperature and the in-furnace temperature of the crystal growth equipment and a preset reward rule. The smaller the temperature difference, the greater the reward value. The simulation model can be trained using historical data of the crystal growth equipment.

[0126] The optimal action determination module 422 can determine the optimal optional action 423 based on the first parameter of the current state and the set of optional actions. In some embodiments, the optimal action determination module 422 can be a machine learning model and can be implemented using various methods, such as a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), etc.

[0127] In some embodiments, the optimal action determination module 422 can be trained using a reinforcement learning method to obtain multiple sets of third training samples with third training labels, such as a Deep Q-Learning Network (DQN) or a Double Deep Q-Learning Network (DDQN). The third training samples can be historical first parameters of the crystal growth equipment, and the third training labels can be the optimal optional actions corresponding to the historical first parameters. The third training samples can be obtained based on historical data of the crystal growth equipment, and the third training labels can be obtained using reinforcement learning methods.

[0128] In some embodiments, parameter determination module 710 may periodically execute reinforcement learning model 420 based on a preset trigger condition and output an optimal optional action 423. For example, if the preset trigger condition is that the reinforcement learning model 420 is executed once per heat of the crystal growth equipment, parameter determination module 710 may determine the optimal optional action 423 output by reinforcement learning model 420 for the target heat as the target first parameter for the target heat.

[0129] Some embodiments of this specification process the initial first parameter through a reinforcement learning model, which can more accurately determine the target first parameter and improve the accuracy of the determined simulated crystal surface temperature.

[0130] FIG5 is another exemplary flow chart of determining a target first parameter according to some embodiments of this specification. In some embodiments, process 500 may be performed by parameter determination module 710. As shown in FIG5 , process 500 may include the following steps:

[0131] Step 510 : Obtain historical first parameters of the crystal growth equipment in multiple historical furnaces and historical operating condition parameters corresponding to the historical first parameters.

[0132] The historical heat refers to the crystal growth heat performed by the crystal growth equipment before the target heat. Correspondingly, the historical first parameter refers to the first parameter of the crystal growth equipment in the historical heat.

[0133] Historical operating parameters refer to parameters related to the operating conditions of the crystal growth equipment in historical furnaces.

[0134] In some embodiments, the historical operating condition parameter may include a second temperature measurement value of the crystal growth equipment at a second temperature measurement point during multiple historical furnaces. The second temperature measurement point may refer to a location at which the crystal growth equipment measured the temperature within the furnace during the multiple historical furnaces, and the second temperature measurement value may refer to a result obtained by measuring the temperature at the second temperature measurement point during the multiple historical furnaces. The second temperature measurement point may be located at the same location as or different from the first temperature measurement point.

[0135] In some embodiments, the historical operating condition parameters may further include the historical growth time of the crystal growing equipment in the historical batches of crystal growing. The historical growth time refers to the time taken by the crystal growing equipment to grow crystals in multiple historical batches.

[0136] In some embodiments, the historical operating condition parameters may further include other parameters, such as, but not limited to, the furnace power, furnace pressure, exhaust rate, etc. of historical heats.

[0137] The parameter determination module 710 can obtain the historical first parameters of the crystal growth equipment in multiple historical furnaces and the historical operating parameters corresponding to the historical first parameters from the historical data of the crystal growth equipment. Exemplarily, the crystal growth equipment can also include a storage module. Each time the crystal growth equipment performs crystal growth, the crystal growth equipment can detect relevant data, for example, the historical first parameters and the historical operating parameters, and store them as historical data in the aforementioned storage module. The parameter determination module 710 can obtain the historical first parameters of the crystal growth equipment in multiple historical furnaces and the historical operating parameters corresponding to the historical first parameters from the aforementioned storage module when needed.

[0138] Step 520 : determining a target first parameter based on the historical first parameters and historical operating condition parameters of a plurality of historical heats.

[0139] In some embodiments, when the historical operating condition parameter includes the second temperature detection value of the second temperature measurement point of the crystal growth equipment in multiple historical furnaces, the parameter determination module 710 can perform analysis and processing based on the historical first parameters and the second temperature detection values ​​of the multiple historical furnaces to determine the second correspondence between the temperature detection value of the second temperature measurement point and the first parameter. The aforementioned second correspondence can reflect the correspondence between the temperature detection value of the second temperature measurement point of the crystal growth equipment and the first parameter. The second correspondence can be characterized as a fitting function, a machine learning model, etc. It is understandable that when the first parameter includes multiple types, the second correspondence can include the correspondence between each first parameter and the temperature detection value of the second temperature measurement point. For example, when the first parameter includes the thermal conductivity and electrical conductivity of the crucible, the aforementioned second correspondence can include the correspondence between the temperature detection value of the second temperature measurement point and the thermal conductivity and electrical conductivity of the crucible.

[0140] In some embodiments, the parameter determination module 710 may obtain a third temperature detection value of the crystal growth equipment at the second temperature measurement point in the target heat, and determine the first parameter based on the third temperature detection value and the second corresponding relationship. The third temperature detection value refers to the result obtained by the crystal growth equipment performing temperature detection at the second temperature measurement point in the target heat. It is understood that when the first temperature measurement point and the second temperature measurement point are the same, the third temperature detection value is the first temperature detection value. The parameter determination module 710 may determine the first parameter corresponding to the third temperature detection value based on the second corresponding relationship, and determine it as the target first parameter.

[0141] In some embodiments, when the historical operating condition parameter includes the historical growth time of the crystal growing equipment in the historical furnace growth of the crystal, the parameter determination module 710 can determine the third corresponding relationship based on the historical first parameters of the multiple historical furnaces and based on multiple historical related parameters and multiple historical growth times. Among them, the aforementioned third corresponding relationship can reflect the third corresponding relationship between the growth time of the crystal and the first parameter. The third corresponding relationship can be characterized as a fitting function, a machine learning model, etc. Similar to the second corresponding relationship, when the first parameter includes multiple types, the third corresponding relationship can include the corresponding relationship between each first parameter and the growth time of the crystal.

[0142] In some embodiments, parameter determination module 710 may obtain a target growth time for crystal growth in a target batch of crystals by the crystal growth equipment; and determine a first parameter based on the target crystal growth time and a third correspondence. Parameter determination module 710 may determine the first parameter corresponding to the target growth time based on the third correspondence, and determine the first parameter as the target first parameter.

[0143] In some embodiments, the parameter determination module 710 can also establish a parameter prediction model based on the historical first parameters and historical operating parameters of multiple historical furnaces; determine the preset operating parameters of the crystal growth equipment in the target furnace; and determine the target first parameter based on the preset operating parameters and the parameter prediction model.

[0144] Preset operating parameters may refer to predetermined operating parameters for a target heat. These parameters may include, but are not limited to, predetermined furnace temperature, furnace pressure, exhaust rate, and furnace power for the crystal growth equipment during various time periods of the target heat. Parameter determination module 710 or a user may plan the growth conditions for the target heat based on the crystal growth information, thereby determining the preset operating parameters for the crystal growth equipment for the target heat. For more information about crystal growth information, see FIG2 and its related description.

[0145] In some embodiments, the parameter determination module 710 may input the preset operating parameters of the target heat into a parameter prediction model, and the output of the parameter prediction model is a first parameter under the preset operating parameters. The parameter determination module 710 may determine the first parameter under the preset operating parameters as the target parameter. The parameter prediction model may be a machine learning model. For example, the parameter prediction model may include, but is not limited to, one or more combinations of a convolutional neural network model, a deep learning model, and a random forest model. Some embodiments of this specification can use the parameter prediction model to quickly and accurately determine the first parameter, thereby improving processing efficiency.

[0146] Parameter determination module 710 may train the initial parameter prediction model based on multiple sets of fourth training samples with fourth training labels to obtain a trained parameter prediction model. For each set of fourth training samples with fourth training labels, the fourth training samples may include historical operating parameters of a historical heat, and the fourth training labels may include historical first parameters corresponding to the historical heat.

[0147] Some embodiments of this specification determine the first parameters by analyzing and processing the historical first parameters and historical operating parameters of multiple historical furnaces of the crystal growth equipment, thereby more accurately determining the first parameters of the crystal growth equipment in the target furnace, avoiding direct judgment based on the initial first parameters, which may lead to errors in determining the crystal surface temperature.

[0148] FIG6 is another exemplary flow chart of determining a target first parameter according to some embodiments of this specification. In some embodiments, process 600 may be performed by parameter determination module 710. As shown in FIG6 , process 600 may include the following steps:

[0149] Step 610: Obtain a first corresponding relationship between a furnace of a crystal growth device and a first parameter.

[0150] In some embodiments, for each of a plurality of preset heats of the crystal growth equipment, the parameter determination module 710 may process the initial first parameter based on a reinforcement learning model to determine a reference first parameter corresponding to each preset heat. The plurality of preset heats may refer to a plurality of heats pre-set for the crystal growth equipment. For example, the plurality of preset heats may be the first 20 heats of the crystal growth equipment. The reference first parameter is the first parameter of the crystal growth equipment for that preset heat. For more information about the reinforcement learning model, see FIG. 3 and its related description.

[0151] The parameter determination module 710 can determine the first corresponding relationship between the furnace of the crystal growth equipment and the first parameter based on the reference first parameter corresponding to the multiple preset furnaces. Similar to the second corresponding relationship and the third corresponding relationship, the first corresponding relationship can also be characterized by a fitting function or a machine learning model. For example, the parameter determination module 710 can perform data fitting on the reference first parameters corresponding to the multiple preset furnaces, determine the fitting function between the furnace of the crystal growth equipment and the first parameter, and determine the aforementioned fitting function as the first corresponding relationship. For another example, the parameter determination module 710 determines the preset furnace as the fifth training sample, determines the reference first parameter corresponding to the preset furnace as the fifth training label, performs training based on the aforementioned fifth training sample and the fifth training label, determines the machine learning model, and determines the aforementioned machine learning model as the first corresponding relationship.

[0152] Step 620: Determine a target first parameter based on the target heat and the first corresponding relationship.

[0153] For example, when the first correspondence is a fitting function, the parameter determination module 710 may determine the target first parameter through calculation based on the target heat. For another example, when the first correspondence is a machine learning model, the parameter determination module 710 may input the target heat into the machine learning model, with the output of the machine learning model being the target first parameter.

[0154] Some embodiments of this specification determine the target first parameter through the first corresponding relationship, avoiding the computational complexity caused by continuous processing based on the reinforcement learning model, and improving the computational efficiency of the crystal growth system 700 while ensuring the accuracy of the target first parameter.

[0155] FIG. 7 is an exemplary module diagram of a crystal growth system according to some embodiments of the present specification.

[0156] As shown in FIG. 7 , the crystal growth system 700 may include a parameter determination module 710 , a temperature detection module 720 , and a temperature determination module 730 .

[0157] The parameter determination module 710 can be used to determine a parameter set for a crystal growth apparatus in a target heat, wherein the parameter set includes at least one target parameter, which is a parameter in the crystal growth apparatus that affects crystal growth. In some embodiments, the target parameter includes a target first parameter and a target second parameter, wherein the target first parameter includes a first parameter of a preset object in the crystal growth apparatus in the target heat, the first parameter reflecting a physical property of the preset object related to crystal growth, and the target second parameter includes a second parameter of the crystal growth apparatus in the target heat, the second parameter including parameters related to settings of the crystal growth apparatus.

[0158] In some embodiments, the parameter determination module 710 can be further used to obtain an initial first parameter, wherein the initial first parameter reflects the first parameter of the preset object in the crystal growth equipment in the initial environment; the initial first parameter is processed based on the reinforcement learning model to determine the target first parameter, and the reward value of the reinforcement learning model is related to the furnace information of the crystal growth equipment.

[0159] In some embodiments, the parameter determination module 710 may be further configured to obtain a first correspondence between a charge of the crystal growth device and a first parameter; and determine a target first parameter based on the target charge and the first correspondence.

[0160] In some embodiments, the parameter determination module 710 can also be used to process the initial first parameter based on the reinforcement learning model for each of multiple preset furnaces of the crystal growth equipment, and determine the reference first parameter corresponding to the preset furnace. The initial first parameter reflects the first parameter of the preset object in the crystal growth equipment in the initial environment. The reference first parameter is the first parameter of the crystal growth equipment in the preset furnace. The reward value of the reinforcement learning model is related to the furnace information of the crystal growth equipment; based on the reference first parameters corresponding to multiple preset furnaces, the first corresponding relationship between the furnace of the crystal growth equipment and the first parameter is determined.

[0161] In some embodiments, the parameter determination module 710 may be further configured to obtain historical first parameters of the crystal growth equipment in multiple historical furnaces and historical operating condition parameters corresponding to the historical first parameters, wherein the historical first parameters include first parameters reflecting the preset objects in the crystal growth equipment in the historical furnaces; and determine the target first parameter based on the historical first parameters and historical operating condition parameters of the multiple historical furnaces. In some embodiments, the historical operating condition parameters include second temperature detection values ​​of the crystal growth equipment at a second temperature measurement point in multiple historical furnaces. The parameter determination module 710 may be further configured to determine a second corresponding relationship based on the historical first parameters and second temperature detection values ​​of the multiple historical furnaces, wherein the second corresponding relationship reflects the corresponding relationship between the temperature detection value of the second temperature measurement point of the crystal growth equipment and the first parameter; obtain a third temperature detection value of the crystal growth equipment at the second temperature measurement point in the target furnace; and determine the target first parameter based on the third temperature detection value and the second corresponding relationship. In some embodiments, the historical operating condition parameters include the historical growth time of crystals grown by the crystal growth equipment in historical furnaces. The parameter determination module 710 may be further configured to determine a third corresponding relationship based on the historical first parameters and historical growth times of multiple historical furnaces, the third corresponding relationship reflecting the corresponding relationship between the crystal growth time and the first parameter; obtain a target growth time for the crystal growth equipment to grow crystals in a target furnace; and determine the target first parameter based on the target growth time and the third corresponding relationship. In some embodiments, the parameter determination module 710 may be further configured to establish a parameter prediction model based on the historical first parameters and historical operating condition parameters of multiple historical furnaces; determine the preset operating condition parameters of the crystal growth equipment in the target furnace; and determine the target first parameter based on the preset operating condition parameters and the parameter prediction model.

[0162] In some embodiments, the parameter determination module 710 can also be used to obtain multiple sets of reference data, each set of reference data includes multiple candidate first parameters and verification information corresponding to the multiple candidate first parameters, each candidate first parameter reflects a material physical property of a preset object in the crystal growth equipment; the multiple sets of reference data are processed through an importance analysis model to determine the type of the first parameter, and the importance analysis model is a random forest model.

[0163] The temperature detection module 720 may be configured to obtain a first temperature detection value at a first temperature measurement point of the crystal growth device during a target time period of a target heat.

[0164] The temperature determination module 730 may be configured to determine, based on the target parameter and the first temperature detection value, a simulated crystal surface temperature corresponding to the first temperature detection value through a crystal surface temperature determination model.

[0165] In some embodiments, the crystal growth system 700 shown in FIG7 may further include a temperature control module 740. The aforementioned temperature control module 740 may be used to determine the temperature control parameters of the crystal growth device in the target time period of the target furnace based on the simulated crystal surface temperature. In some embodiments, the temperature control module 740 may be further used to obtain multiple reference crystal surface temperatures and multiple reference control parameters corresponding to the multiple reference crystal surface temperatures for each reference device in a reference furnace, wherein the reference device is a device of the same type as the crystal growth device; and determine the temperature control parameters based on the reference crystal surface temperatures, reference control parameters, and simulated crystal surface temperatures of the multiple reference devices in the reference furnace. In some embodiments, the temperature control module 740 may be further used to obtain crystal detection data of crystals generated by the multiple reference devices in the reference furnace; and determine the temperature control parameters based on the reference crystal surface temperatures, reference control parameters, crystal detection data, and simulated crystal surface temperatures of the multiple reference devices in the reference furnace.

[0166] In some embodiments, the crystal growth system 700 shown in Figure 7 may further include a temperature adjustment module 750. The temperature adjustment module 750 may be configured to automatically send temperature adjustment instructions based on temperature control parameters to adjust the crystal growth temperature of the crystal growth equipment during a target time period of a target batch.

[0167] It should be understood that the crystal growth system 700 and its modules shown in FIG7 can be implemented in various ways. For example, the crystal growth system 700 can be implemented in a processor of a crystal growth apparatus, which can also include a crystal growth device. The processor can control the crystal growth device via the crystal growth system 700 to implement the crystal growth method described in any of the following embodiments of this specification.

[0168] It should be noted that the above description of the crystal growth system 700 and its modules is for convenience of description only and does not limit this specification to the scope of the embodiments cited. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules or form a subsystem connected to other modules without deviating from this principle. In some embodiments, the parameter determination module 710, temperature detection module 720 and temperature determination module 730 disclosed in Figure 7 can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0169] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0170] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0171] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0172] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0173] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0174] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents (currently or subsequently attached to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification will control.

[0175] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A crystal growth method, characterized in that: The method comprises: Determining a parameter set of a crystal growth device in a target heat, wherein the parameter set includes at least one target parameter, and the target parameter is a parameter in the crystal growth device that affects crystal growth; acquiring a first temperature detection value at a first temperature measurement point of the crystal growth device during a target time period of the target heat; Based on the target parameter and the first temperature detection value, a simulated crystal surface temperature corresponding to the first temperature detection value is determined by a crystal surface temperature determination model.

2. The method according to claim 1, wherein The method further comprises: Based on the simulated crystal surface temperature, a temperature control parameter of the crystal growth equipment in a target time period of a target furnace is determined.

3. The method according to claim 2, wherein The method further comprises: Based on the temperature control parameters, a temperature adjustment instruction is automatically sent to adjust the crystal growth temperature of the crystal growth device in the target time period.

4. The method according to claim 1, wherein The target parameters include a first target parameter and a second target parameter. The target first parameter includes a first parameter of a preset object in the crystal growth device at a target heat, wherein the first parameter reflects a physical property of the material in the preset object related to crystal growth. The target second parameter includes a second parameter of the crystal growth equipment in the target heat, and the second parameter includes parameters related to settings of the crystal growth equipment.

5. The method according to claim 4, wherein The physical properties of the material include at least one of thermal conductivity, electrical conductivity, surface emissivity, thermal conductivity, constant pressure heat capacity and relative magnetic permeability.

6. The method according to claim 4, wherein The preset object includes at least one of a crucible, a thermal insulation felt, a raw material melt, and a protective gas.

7. The method according to claim 4, wherein The parameter set for determining the target heat of the crystal growth equipment includes: Acquiring an initial first parameter, wherein the initial first parameter reflects the first parameter of the preset object in the crystal growth device in an initial environment; The initial first parameter is processed based on a reinforcement learning model to determine the target first parameter, and a reward value of the reinforcement learning model is related to the in-furnace information of the crystal growth equipment.

8. The method according to claim 4, wherein The parameter set for determining the target heat of the crystal growth equipment includes: Acquire a first corresponding relationship between the furnace of the crystal growth equipment and the first parameter; The target first parameter is determined based on the target heat and the first corresponding relationship.

9. The method according to claim 8, wherein The first corresponding relationship is determined by the following steps: For each of a plurality of preset furnaces of the crystal growth equipment, processing an initial first parameter based on a reinforcement learning model to determine a reference first parameter corresponding to the preset furnace, wherein the initial first parameter reflects the first parameter of a preset object within the crystal growth equipment in an initial environment, the reference first parameter is the first parameter of the crystal growth equipment in the preset furnace, and a reward value of the reinforcement learning model is related to information within the furnace of the crystal growth equipment; Based on the reference first parameters corresponding to the plurality of preset furnaces, a first corresponding relationship between the furnaces of the crystal growth equipment and the first parameters is determined.

10. The method according to claim 4, wherein The parameter set for determining the target heat of the crystal growth equipment includes: Obtaining historical first parameters of the crystal growth equipment in multiple historical furnaces and historical operating condition parameters corresponding to the historical first parameters, wherein the historical first parameters include the first parameters reflecting the preset object in the crystal growth equipment in the historical furnaces; The target first parameter is determined based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters.

11. The method according to claim 10, wherein The historical operating condition parameters include second temperature detection values of the crystal growth equipment at second temperature measurement points in multiple historical furnaces; The determining of the first parameter based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters includes: Based on the historical first parameters of the plurality of historical heats and the second temperature detection values, a second corresponding relationship is determined, wherein: The second corresponding relationship reflects the corresponding relationship between the temperature detection value of the second temperature measurement point of the crystal growth equipment and the first parameter; Obtaining a third temperature detection value of the crystal growth equipment at the second temperature measurement point of the target heat; The target first parameter is determined based on the third temperature detection value and the second corresponding relationship.

12. The method according to claim 10, wherein The historical operating condition parameters include the historical growth time of the crystal growing equipment in growing crystals in historical furnaces; The determining the target first parameter based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters includes: Determining a third corresponding relationship based on the historical first parameter and the historical growth duration of the plurality of historical furnaces, wherein the third corresponding relationship reflects a corresponding relationship between the growth duration of the crystal and the first parameter; Obtaining a target growth time for the crystal growing device to grow the crystal in the target batch; Based on the target growth duration and the third corresponding relationship, the target first parameter is determined.

13. The method according to claim 10, wherein The determining the target first parameter based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters includes: Establishing a parameter prediction model based on the historical first parameters and the historical operating condition parameters of the plurality of historical heats; Determining preset operating parameters of the crystal growth equipment at the target heat; Based on the preset operating condition parameters and the parameter prediction model, the target first parameter is determined.

14. The method according to claim 4, wherein The type of the first parameter is determined by the following steps: Acquire multiple sets of reference data, each set of the reference data including multiple candidate first parameters and verification information corresponding to the multiple candidate first parameters, each candidate first parameter reflecting a material physical property of the preset object in the crystal growth device; The multiple groups of reference data are processed by an importance analysis model to determine the type of the first parameter, and the importance analysis model is a random forest model.

15. The method according to claim 2, wherein The determining, based on the simulated crystal surface temperature, the temperature control parameter of the crystal growth equipment in the target time period of the target furnace comprises: Obtaining a plurality of reference crystal surface temperatures of each of a plurality of reference devices in a reference furnace and a plurality of reference control parameters corresponding to the plurality of reference crystal surface temperatures, wherein the reference device is a device of the same type as the crystal growth device; The temperature control parameter is determined based on the reference crystal surface temperature of the reference equipment in the reference heat, the reference control parameter, and the simulated crystal surface temperature.

16. The method according to claim 15, wherein The determining of the temperature control parameter based on the plurality of reference crystal surface temperatures, the plurality of reference control parameters and the simulated crystal surface temperature comprises: Acquiring crystal detection data of crystals generated by the reference heats of a plurality of the reference devices; The temperature control parameter is determined based on the reference crystal surface temperature of the reference equipment in the reference heat, the reference control parameter, the crystal detection data, and the simulated crystal surface temperature.

17. A crystal growth system, characterized in that: The system comprises: a parameter determination module, configured to determine a parameter set for a crystal growth device in a target heat, wherein the parameter set includes at least one target parameter, which is a parameter in the crystal growth device that affects crystal growth; A temperature detection module, configured to obtain a first temperature detection value at a first temperature measurement point of the crystal growth device during a target time period of the target heat; The temperature determination module is configured to determine, based on the target parameter and the first temperature detection value, a simulated crystal surface temperature corresponding to the first temperature detection value through a crystal surface temperature determination model.

18. The system according to claim 17, wherein: The system further comprises: A temperature control module is used to determine the temperature control parameters of the crystal growth equipment in a target time period of a target furnace based on the simulated crystal surface temperature.

19. The system of claim 17, wherein: The system further comprises: The temperature adjustment module is used to automatically send a temperature adjustment instruction based on the temperature control parameter to adjust the crystal growth temperature of the crystal growth equipment in the target time period of the target furnace.

20. The system of claim 17, wherein: The target parameters include a first target parameter and a second target parameter. The target first parameter includes a first parameter of a preset object in the crystal growth device at a target heat, wherein the first parameter reflects a physical property of the material in the preset object related to crystal growth. The target second parameter includes a second parameter of the crystal growth equipment in the target heat, and the second parameter includes parameters related to settings of the crystal growth equipment.

21. The system of claim 20, wherein: The parameter determination module is further configured to: Acquiring an initial first parameter, wherein the initial first parameter reflects the first parameter of the preset object in the crystal growth device in an initial environment; The initial first parameter is processed based on a reinforcement learning model to determine the target first parameter, and a reward value of the reinforcement learning model is related to the in-furnace information of the crystal growth equipment.

22. The system of claim 20, wherein: The parameter determination module is further configured to: Acquire a first corresponding relationship between the furnace of the crystal growth equipment and the first parameter; The target first parameter is determined based on the target heat and the first corresponding relationship.

23. The system of claim 22, wherein: The parameter determination module is further configured to: For each of a plurality of preset furnaces of the crystal growth equipment, processing an initial first parameter based on a reinforcement learning model to determine a reference first parameter corresponding to the preset furnace, wherein the initial first parameter reflects the first parameter of a preset object within the crystal growth equipment in an initial environment, the reference first parameter is the first parameter of the crystal growth equipment in the preset furnace, and a reward value of the reinforcement learning model is related to information within the furnace of the crystal growth equipment; Based on the reference first parameters corresponding to the plurality of preset furnaces, a first corresponding relationship between the furnaces of the crystal growth equipment and the first parameters is determined.

24. The system of claim 20, wherein: The parameter determination module is further configured to: Obtaining historical first parameters of the crystal growth equipment in multiple historical furnaces and historical operating condition parameters corresponding to the historical first parameters, wherein the historical first parameters include the first parameters reflecting the preset object in the crystal growth equipment in the historical furnaces; The target first parameter is determined based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters.

25. The system of claim 24, wherein: The historical operating condition parameters include second temperature detection values of the crystal growth equipment at second temperature measurement points in multiple historical furnaces; The parameter determination module is further configured to: Determining a second corresponding relationship based on the historical first parameter and the second temperature detection value of the plurality of historical furnaces, wherein the second corresponding relationship reflects a corresponding relationship between the temperature detection value of the second temperature measurement point of the crystal growth equipment and the first parameter; Obtaining a third temperature detection value of the crystal growth equipment at the second temperature measurement point of the target heat; The target first parameter is determined based on the third temperature detection value and the second corresponding relationship.

26. The system of claim 24, wherein: The historical operating condition parameters include the historical growth time of the crystal growing equipment in growing crystals in historical furnaces; The parameter determination module is further configured to: Determining a third corresponding relationship based on the historical first parameter and the historical growth duration of the plurality of historical furnaces, wherein the third corresponding relationship reflects a corresponding relationship between the growth duration of the crystal and the first parameter; Obtaining a target growth time for the crystal growing device to grow the crystal in the target batch; Based on the target growth duration and the third corresponding relationship, the target first parameter is determined.

27. The system of claim 24, wherein: The parameter determination module is further configured to: Establishing a parameter prediction model based on the historical first parameters and the historical operating condition parameters of the plurality of historical heats; Determining preset operating parameters of the crystal growth equipment at the target heat; Based on the preset operating condition parameters and the parameter prediction model, the target first parameter is determined.

28. The system of claim 20, wherein: The parameter determination module is further configured to: Acquire multiple sets of reference data, each set of reference data includes multiple candidate first parameters and the corresponding verification information, each of the candidate first parameters reflecting a material physical property of the preset object in the crystal growth device; The multiple groups of reference data are processed by an importance analysis model to determine the type of the first parameter, and the importance analysis model is a random forest model.

29. The system of claim 18, wherein: The temperature control module is further configured to: Obtaining a plurality of reference crystal surface temperatures of each of a plurality of reference devices in a reference furnace and a plurality of reference control parameters corresponding to the plurality of reference crystal surface temperatures, wherein the reference device is a device of the same type as the crystal growth device; The temperature control parameter is determined based on the reference crystal surface temperature of the reference equipment in the reference heat, the reference control parameter, and the simulated crystal surface temperature.

30. The system of claim 29, wherein: The temperature control module is further configured to: Acquiring crystal detection data of crystals generated by the reference heats of a plurality of the reference devices; The temperature control parameter is determined based on the reference crystal surface temperature of the reference equipment in the reference heat, the reference control parameter, the crystal detection data, and the simulated crystal surface temperature.

31. A crystal growth apparatus, comprising a crystal growth device and a processor, wherein the processor is configured to execute the crystal growth method according to any one of claims 1 to 16.

32. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the crystal growth method according to any one of claims 1 to 16.

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