Crystal growth method

Abstract

The embodiment of the invention provides a crystal growth method which comprises the following steps: determining a parameter set of crystal growth equipment in a target heat, the parameter set comprising at least one target parameter, and the target parameter being a parameter influencing crystal growth in the crystal growth equipment; acquiring a first temperature detection value of a first temperature measurement point of the crystal growth equipment in a target time period of the target heat; based on the target parameter and the first temperature detection value, determining a simulated crystal face temperature corresponding to the first temperature detection value; and determining temperature control parameters of the crystal growth equipment in the target time period of the target heat based on the simulated crystal face temperature.

Description

Description of the case

[0001] This application is a divisional application filed for the Chinese application with the application date of February 6, 2024, application number 202410172434.3, and invention name “A crystal growth method, system, device and storage medium”. Technical Field

[0002] The present invention relates to the field of crystal preparation, and in particular to a crystal growth method. Background Art

[0003] With the development of science and technology, crystals (for example, 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.

[0004] 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.

[0005] Therefore, how to improve the quality of crystal growth is a technical problem that needs to be solved urgently in this field. 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 measurement point of the crystal growth device in a target time period of the target furnace; determining a simulated crystal surface temperature corresponding to the first temperature detection value based on the target parameter and the first temperature detection value; and determining a temperature control parameter of the crystal growth device in the target time period of the target furnace based on the simulated crystal surface temperature.

[0007] 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.

[0008] In some embodiments, the target parameters include a target first parameter and a target second parameter, the target first parameter includes a first parameter of a preset object in the crystal growth equipment in a 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 a second parameter of the crystal growth equipment in the target furnace, and the second parameter includes parameters of related settings of the crystal growth equipment.

[0009] In some embodiments, the physical properties of the material include at least one of thermal conductivity, electrical conductivity, surface emissivity, heat transfer coefficient, constant pressure heat capacity, and relative magnetic permeability; and / or the preset includes at least one of a crucible, a heat insulation felt, a raw material melt, and a protective gas.

[0010] In some embodiments, determining the parameter set of the crystal growth equipment for a target furnace run includes: obtaining an initial first parameter, where the initial first parameter reflects the first parameter of the preset in the crystal growth equipment in the initial environment; processing 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 in-furnace information of the crystal growth equipment.

[0011] In some embodiments, determining the parameter set of the crystal growth equipment for a target furnace run includes: obtaining a first correspondence between the furnace runs of the crystal growth equipment and the first parameter; based on the target furnace run and the first correspondence, determining the target first parameter; where the first correspondence is determined through the following steps: for each of a plurality of preset furnace runs of the crystal growth equipment, processing the initial first parameter based on a reinforcement learning model to determine the reference first parameter corresponding to the preset furnace run, where the initial first parameter reflects the first parameter of the preset 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 run, and the reward value of the reinforcement learning model is related to the in-furnace information of the crystal growth equipment; based on the reference first parameters corresponding to the plurality of preset furnace runs, determining the first correspondence between the furnace runs of the crystal growth equipment and the first parameter.

[0012] In some embodiments, determining the parameter set of the crystal growth equipment for a target furnace run includes: obtaining the historical first parameters of the crystal growth equipment in a plurality of historical furnace runs and the historical working condition parameters corresponding to the historical first parameters, where the historical first parameters include the first parameter reflecting the preset in the crystal growth equipment in the historical furnace run; based on the historical first parameters and the historical working condition parameters of the plurality of historical furnace runs, determining the target first parameter.

[0013] In some embodiments, the historical operating condition parameters include the second temperature detection values at the second temperature measurement points of the crystal growth equipment in multiple historical furnace runs; determining the first parameter based on the historical first parameters and the historical operating condition parameters of multiple historical furnace runs includes: determining a second correspondence relationship based on the historical first parameters and the second temperature detection values of multiple historical furnace runs, where the second correspondence relationship reflects the correspondence relationship between the temperature detection value at the second temperature measurement point of the crystal growth equipment and the first parameter; obtaining the third temperature detection value at the second temperature measurement point of the crystal growth equipment in the target furnace run; and determining the target first parameter based on the third temperature detection value and the second correspondence relationship.

[0014] In some embodiments, the historical operating condition parameters include the historical growth duration of growing crystals in the historical furnace runs of the crystal growth equipment; determining the target first parameter based on the historical first parameters and the historical operating condition parameters of multiple historical furnace runs includes: determining a third correspondence relationship based on the historical first parameters and the historical growth duration of multiple historical furnace runs, where the third correspondence relationship reflects the correspondence relationship between the growth duration of the crystal and the first parameter; obtaining the target growth duration of growing crystals in the target furnace run of the crystal growth equipment; and determining the target first parameter based on the target growth duration and the third correspondence relationship.

[0015] In some embodiments, the type of the first parameter is determined by the following steps: obtaining multiple sets of reference data, each set of reference data including multiple candidate first parameters and the verification information corresponding to the multiple candidate first parameters, and each candidate first parameter reflecting a material physical property of the preset material 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, where the importance analysis model is a random forest model.

[0016] In some embodiments, determining the target first parameter based on the historical first parameters and the historical operating condition parameters of multiple historical furnace runs includes: establishing a parameter prediction model based on the historical first parameters and the historical operating condition parameters of multiple historical furnace runs; determining the preset operating condition parameters of the crystal growth equipment in the target furnace run; and determining the target first parameter based on the preset operating condition parameters and the parameter prediction model.

[0017] In some embodiments, determining the temperature control parameters of the crystal growth equipment during the target time period of the target furnace batch based on the simulated crystal plane temperature includes: obtaining the multiple reference crystal plane temperatures of each of the multiple reference devices during a reference furnace batch and the multiple reference control parameters corresponding to the multiple reference crystal plane temperatures, where the reference devices are devices of the same type as the crystal growth equipment; determining the temperature control parameters based on the reference crystal plane temperatures, the reference control parameters, and the simulated crystal plane temperature of the multiple reference devices during the reference furnace batch.

[0018] In some embodiments, determining the temperature control parameters based on the multiple reference crystal plane temperatures, the multiple reference control parameters, and the simulated crystal plane temperature includes: obtaining the crystal detection data of the crystals generated by the multiple reference devices during the reference furnace batch; determining the temperature control parameters based on the reference crystal plane temperatures, the reference control parameters, the crystal detection data, and the simulated crystal plane temperature of the multiple reference devices during the reference furnace batch. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0020] Figure 1 is a schematic diagram of an application scenario of a crystal growth system shown in some embodiments of this specification;

[0021] Figure 2 is an exemplary flowchart of a crystal growth method shown in some embodiments of this specification;

[0022] Figure 3 is an exemplary flowchart of determining a first parameter shown in some embodiments of this specification;

[0023] Figure 4 is a schematic diagram of a reinforcement learning model shown in some embodiments of this specification;

[0024] Figure 5 is an exemplary flowchart of determining another target first parameter shown in some embodiments of this specification;

[0025] Figure 6 is an exemplary flowchart of determining another target first parameter shown in some embodiments of this specification;

[0026] Figure 7 is an exemplary module diagram of a crystal growth system shown in some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the drawings represent the same structure or operation.

[0028] It should be understood that the "system", "device", "unit" and / or "module" used in this specification are a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0029] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0030] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0031] Figure 1 is a schematic diagram of the application scenario of the crystal growth system shown according to some embodiments of this specification.

[0032] As Figure 1 shown, the application scenario 100 of the 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.

[0033] The crystal growth device 110 is a device for crystal growth. Among them, the aforementioned crystals may include, but are not limited to, silicon carbide, germanium single crystal, etc. The crystal growth device 110 may include, but is not limited to, a physical vapor transport crystal growth device, a liquid phase epitaxy crystal growth device, a Czochralski crystal growth device, etc. The crystal growth device 110 may include structures such as a furnace body, a crucible, a heat insulation layer, and a seed crystal holder. It can be understood that for any of the aforementioned crystal growth methods, the crystal surface temperature during crystal growth can be determined by the crystal growth method shown in any one of the embodiments in this specification to complete crystal growth.

[0034] The temperature detection device 120 is a device for temperature detection. The temperature detection device 120 may include, but is not limited to, an infrared thermal imager, an optical fiber temperature sensor, etc. The temperature detection device 120 may be disposed in the crystal growth device 110 to obtain a first temperature detection value of a first temperature measurement point of the crystal growth device 110 during a target time period of a target furnace batch. For more information about the target furnace batch, the target time period, the first temperature measurement point, and the first temperature detection value, reference can be made to Figure 2 and its related descriptions.

[0035] The network 130 can connect the components of the application scenario 100 of the crystal growth system and / or connect to the external resource part. The network 130 enables communication between the components and with other parts, 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 may include a cable network, an optical fiber 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, etc., or any combination thereof. The network connection between the parts can be in one of the above ways or in multiple ways. The network can be various topological structures such as point-to-point, shared, centralized, etc., or a combination of multiple topological structures.

[0036] The storage device 140 can be used to store data and / or instructions. For example, the storage device 140 can be used to store historical data, device information, etc. of the crystal growth device 110. The storage device 140 can include one or more storage components, and each storage component can be an independent device or a part of other devices. The storage device 140 can include random access memory, read-only memory, mass storage, removable storage, volatile read-write memory, etc. or any combination thereof. Exemplarily, the mass storage can include magnetic disks, optical disks, solid state disks, etc. In some embodiments, the storage device 140 can be implemented on a cloud platform.

[0037] The processor 150 can process data and / or information obtained from external resources or components of the application scenario 100 of the crystal growth system. For example, the processor 150 can determine a parameter set of the crystal growth device 110 in a target furnace run. The processor can execute program instructions based on this 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 plane temperature corresponding to the first temperature detection value through the crystal plane temperature determination model based on the target parameters and the first temperature detection value. Again, for 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 run based on the simulated crystal plane temperature. More content about the foregoing examples can be found in the following text of this specification. The processor 150 can include one or more sub-processing devices (for example, a single-core processing device or a multi-core multi-chip processing device). By way of example only, the processor 150 can include a central processing unit, an application specific integrated circuit, an application specific instruction processor, a graphics processing unit, 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, etc. or any combination of the above.

[0038] 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 of ordinary skill in the art, various modifications or changes can be made according to 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. Again, for example, the application scenario 100 of the crystal growth system can be implemented on other devices to achieve similar or different functions. However, the changes and modifications will not depart from the scope of this specification.

[0039] Figure 2 is an exemplary flowchart of a crystal growth method shown according to some embodiments of this specification. In some embodiments, the process 200 can be executed by the crystal growth system 700. As Figure 2 shown, the process 200 includes the following steps:

[0040] Step 210, determine the parameter set of the crystal growth equipment for the target furnace run. In some embodiments, step 210 may be executed by the parameter determination module 710.

[0041] The target furnace run refers to the furnace run in which the crystal growth equipment (e.g., Figure 1 the crystal growth equipment 110 shown) is currently growing crystals. For example, the target furnace run may be the 20th furnace, indicating that the crystal growth equipment is currently in the process of growing the 20th furnace of crystals.

[0042] The parameter set is a set of target parameters of the crystal growth equipment for the target furnace run, and the parameter set may include at least one target parameter.

[0043] The target parameter is a parameter in the crystal growth equipment that affects crystal growth. Specifically, the target parameter can affect the growth rate of the crystal, the quality of the grown crystal, etc. In some embodiments, the target parameter may include a target first parameter and a target second parameter.

[0044] The target first parameter may include the first parameter of the preset material in the crystal growth equipment for the target furnace run. The aforementioned preset material may include at least one of a crucible, a thermal insulation felt (such as a soft felt and / or a hard felt), a raw material melt, and a protective gas (such as argon). The first parameter can reflect the material physical properties related to crystal growth in the preset material.

[0045] In some embodiments, the parameter determination module 710 may determine the type of the first parameter. It can be understood that the material physical properties of the preset material include various different types of parameters. Exemplarily, the material physical properties of the preset material may include parameters in aspects such as the density, optical properties, mechanical properties, thermal properties, electrical properties, and magnetic properties of the preset material. However, when some of the material physical properties of the preset material (e.g., mechanical properties) change, the impact on the crystal surface temperature is relatively large; when some of the material physical properties of the preset material (e.g., thermal properties) change, the impact on the crystal surface temperature is relatively small. In some embodiments, the parameter determination module 710 may determine the parameters of the material physical properties that have a relatively large impact on the crystal surface temperature when they change in the material physical properties of the preset material as the first parameter, and detect the target first parameter in the target furnace run of the crystal growth equipment through the foregoing multiple embodiments, reducing the calculation amount and avoiding waste of computing resources. In some embodiments, the first parameter may include at least one of parameters in aspects such as the thermal properties, electrical properties, and magnetic properties of the preset material. Exemplarily, the first parameter may include at least one of the thermal conductivity, electrical conductivity, surface emissivity, heat transfer coefficient, constant pressure heat capacity, and relative magnetic permeability.

[0046] In some embodiments, the parameter determination module 710 may obtain multiple sets of reference data. The reference data may be data for determining the type of the first parameter. Each set of reference data may include multiple candidate first parameters and the verification information corresponding to the multiple candidate first parameters. The candidate first parameter may be a parameter to be evaluated for determination of the first parameter, and the candidate first parameter may include all material physical properties of the preset object or some material physical properties specified by the user. Each candidate first parameter may reflect a material physical property of the preset object in the crystal growth device. The verification information is information for verifying the importance of the first parameter. For example, the verification information may include, but is not limited to, the temperature in the furnace, the pressure in the furnace, the crystal growth quality, etc. The reference data may be obtained from the historical data of the crystal growth device. For example, the multiple candidate first parameters and the verification information in a certain set of reference data may be obtained by measuring the preset object of the crystal growth device in a historical furnace run.

[0047] In some embodiments, the parameter determination module 710 may process the multiple sets of reference data through an importance analysis model to determine the type of the first parameter. The importance analysis model is a random forest model. For example, the parameter determination module 710 may process the multiple sets of reference data through the importance analysis model to determine four types of first parameters from the material physical properties of 20 preset objects, namely, surface emissivity, thermal conductivity, constant pressure heat capacity, and relative magnetic permeability.

[0048] The importance analysis model may analyze the input multiple candidate first parameters through the input verification information to determine the importance corresponding to each candidate first parameter, so as to determine the type of the first parameter. The importance corresponding to each of the foregoing candidate first parameters may be represented in various ways, such as a numerical value, a ratio, etc. Exemplarily, the parameter determination module 710 may 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 the multiple candidate first parameters through a random forest model to determine the type of the first parameter, avoiding the interference of the material physical properties that have little influence on crystal growth in the preset object and reducing the computational amount of the crystal growth system 700.

[0049] In some embodiments, the parameter determination module 710 may determine the target first parameter of the crystal growth device in the target furnace run based on the initial first parameter corresponding to the preset object. For example, the parameter determination module 710 may determine the target first parameter of the crystal growth device in the target furnace run based on the device information of the crystal growth device. The foregoing device information may include relevant information of the crystal growth device, and the device information may include, but is not limited to, the type of the crystal growth device, the overall size, the size of the preset object, the initial first parameter, etc. The device information may be provided by the manufacturer of the crystal growth device. For more descriptions of the initial first parameter, see Figure 4and its related descriptions.

[0050] In some embodiments, the parameter determination module 710 may determine the specific value of the target first parameter. It should be noted that when in a normal temperature state, the first parameter is relatively stable. For example, the conductivity can be a certain fixed value. However, when the crystal grows in the crystal growth device, the crystal growth device will inductively heat the crucible through an induction coil. Therefore, the temperature of the crystal growth environment is relatively high (for example, about 900 - 7200 °C), and it is located in an electric field and a magnetic field. At this time, the first parameter may change according to the different furnace environments 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, resulting in a change in the first parameter. Based on this, in order to more accurately determine the crystal plane temperature during the crystal growth process, the parameter determination module 710 may determine the target first parameter of the crystal growth device in the target furnace batch, that is, the specific value of the first parameter in the target furnace batch, through various methods.

[0051] In some embodiments, the parameter determination module 710 may obtain an initial first parameter, where the initial first parameter reflects the first parameter of the preset object in the crystal growth device 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 device. For more descriptions of the foregoing embodiments, reference can be made to Figure 3 and its related descriptions.

[0052] In some embodiments, the parameter determination module 710 may also obtain the first correspondence relationship between the furnace batch of the crystal growth device and the first parameter; determine the target first parameter based on the target furnace batch and the first correspondence relationship. For more descriptions of the foregoing embodiments, reference can be made to Figure 4 and its related descriptions.

[0053] In some embodiments, the parameter determination module 710 may also obtain the historical first parameters of the crystal growth device in multiple historical furnace batches and the historical operating condition parameters corresponding to the historical first parameters, where the historical first parameters include the first parameters of the preset object in the crystal growth device in the historical furnace batches; determine the target first parameter based on the historical first parameters and the historical operating condition parameters of the multiple historical furnace batches. For more descriptions of the foregoing embodiments, reference can be made to Figure 5 and its related descriptions.

[0054] The target second parameter may include the second parameter of the crystal growth device in the target furnace batch. The second parameter may include the parameters related to the settings of the crystal growth device in the target furnace batch.

[0055] In some embodiments, the parameter determination module 710 can determine the type of the second parameter by preset, and further determine the number of the target second parameter in the target furnace. The second parameter may include furnace equipment parameters and furnace growth parameters. The aforementioned furnace equipment parameters may be parameters related to crystal growth equipment. Exemplarily, furnace equipment parameters may include but are not limited to the number of top insulation layers, the distance between the top insulation bottom and the top of the crucible, the number of soft felts, the minimum inner diameter, the maximum outer diameter, the cover thickness, the material distance, the inner diameter of the gasket, the material height, the bottom insulation soft felt, the insulation inner tube hard felt, the insulation inner tube soft felt, the insulation outer tube hard felt, the insulation outer tube soft felt, the outermost layer of soft felt, the ring height, etc. The furnace growth parameters may be parameters related to the crystal growth setting. For example, the furnace growth parameters may include growth pressure, growth power, high temperature temperature, high temperature line, crystal growth time, center thickness, edge thickness, etc. Different furnaces of crystal growth equipment may cause the corresponding second parameters to be different due to differences in the generated crystal type, crystal demand, etc.

[0056] In some embodiments, the target second parameter can be obtained by presetting the target furnace. For example, the target second parameter is determined by inputting furnace equipment parameters and furnace growth parameters of the target furnace by a user (e.g., a manager of a crystal growth device). For another example, the furnace equipment parameters of the crystal growth device in the target furnace can be determined based on a pre-set crystal growth device type. For another example, the furnace growth parameters of the crystal growth device in the target furnace can be determined based on a pre-set crystal type, crystal demand, etc.

[0057] Step 220 , obtaining a first temperature detection value of a first temperature measurement point of the crystal growth device in a target time period of the target furnace. In some embodiments, step 220 may be performed by the temperature detection module 720 .

[0058] The target time period refers to the time period for measuring the temperature of the first temperature measurement point. The target time period can be preset by a user (e.g., an operator of a crystal growth device). The target heat can include multiple time periods, and the target time period can be the current time period.

[0059] The temperature detection module 720 can perform at least one temperature detection within the 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 perform at least one temperature detection within the target time period to obtain a first temperature detection value of a first temperature measurement point of the crystal growth device. Figure 1 The temperature detection device 120 shown is implemented.

[0060] The first temperature measurement point is a temperature detection point in the crystal growth device (for example, in the furnace of the crystal growth device), and the first temperature detection value is the temperature of the first temperature measurement point obtained by detection.

[0061] The crystal growth equipment may include one or more first temperature measurement points. The first temperature measurement points can be determined by the user's pre-setting. The user can set temperature detection devices at one or more positions, and the temperature detection module 720 can obtain the first temperature detection value of the first temperature measurement point through the aforementioned temperature detection devices during the target time period of the target furnace batch.

[0062] Step 230, based on the target parameters and the first temperature detection value, determine the simulated crystal plane temperature corresponding to the first temperature detection value through the crystal plane temperature determination model. In some embodiments, step 230 can be executed by the temperature determination module 730.

[0063] The simulated crystal plane temperature refers to the crystal plane temperature corresponding to the first temperature detection value determined through simulation.

[0064] The crystal plane temperature determination model can be a numerical simulation model. The processor 150 can establish multiple mathematical equations based on the historical target parameters, historical first temperature detection chambers, and historical crystal plane temperatures of multiple historical furnace batches, discretize the multiple mathematical equations, convert them into a discrete form for easy processing, and then use numerical calculation methods such as the finite difference method and the finite element method to solve the aforementioned discretized mathematical equations to obtain the crystal plane temperature determination model. The temperature determination module 730 can input the target parameters and the first temperature detection value into the crystal plane temperature determination model, and the crystal plane temperature determination model performs simulation and emulation based on the input data to determine the simulated crystal plane temperature.

[0065] Some embodiments of this specification can simulate and determine the simulated crystal plane temperature through the crystal plane temperature determination model to solve the situation where the crystal plane temperature is difficult to observe.

[0066] In some embodiments, the crystal plane temperature determination model can also be a machine learning model. For example, the crystal plane 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 plane temperature determination model can include the target parameters and the first temperature detection value, and the output can include the simulated crystal plane temperature corresponding to the first temperature detection value. The aforementioned crystal plane temperature determination model can be obtained by training after modeling the crystal growth equipment through numerical simulation software (such as Virtual reactor, COMSOL, etc.) or a designed heat transfer calculation program to obtain an initial crystal plane temperature determination model. The first training sample can include sample target parameters and sample first temperature detection values, and the first training label can include sample crystal plane temperatures. The aforementioned first training sample and the first training label can be obtained through heating experiments on the crystal growth equipment.

[0067] In some embodiments, the temperature determination module 730 may further determine a corresponding crystal plane temperature determination model based on the crystal growth equipment type. The crystal plane temperature determination model corresponding to each crystal growth equipment type may be obtained by training respectively based on the first training samples and the first training labels corresponding to each crystal growth equipment type.

[0068] In some embodiments, the input of the crystal plane temperature determination model may further include the position information of the first temperature measurement point. Correspondingly, the first training samples during training may further include the sample position information of the sample first temperature measurement point. By using the position information of the first temperature measurement point as the input of the crystal plane temperature determination model, the error caused by the position difference of the first temperature measurement point can be avoided, and the accuracy of the output of the crystal plane temperature determination model can be improved.

[0069] In some embodiments, the crystal plane temperature determination model may further include a numerical simulation model and a machine learning model. For example, the temperature determination module 730 may process the target parameters and the first temperature detection value based on the numerical simulation model and the machine learning model, and perform a weighted sum on the results output by both, and determine the weighted sum result as the simulated crystal plane temperature. Among them, the aforementioned numerical simulation model and machine learning model may be obtained through presetting. The numerical simulation model is usually affected by uncertainties from different sources, such as model parameter uncertainties, noise in input data, etc. The machine learning model can be applied to estimate and model uncertainties to provide more accurate prediction results and effective uncertainty quantification. By integrating the output results of both, a more accurate simulated crystal plane temperature can be obtained.

[0070] Through some embodiments of this specification, the parameter set of the crystal growth equipment in the target furnace can be determined more accurately, thereby more accurately determining the crystal plane temperature during the crystal growth process and improving the growth quality of the crystal. In addition, by using the crystal plane temperature determination model to determine the crystal plane temperature, the judgment efficiency can be improved and the manual judgment cost can be reduced.

[0071] In some embodiments, after determining the simulated crystal plane temperature, process 200 may further determine the 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:

[0072] Step 240, determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace based on the simulated crystal plane temperature. In some embodiments, step 240 may be executed by the temperature control module 740.

[0073] The temperature control parameters are the parameters for the crystal growth equipment to adjust the temperature inside the furnace. The temperature control parameters may include the power of the furnace chamber in the crystal growth equipment. For example, the temperature control parameter may be +5KW, indicating that it is necessary to increase the power of the furnace chamber by 5KW during the target time period based on the current power. The temperature control parameters may also include the adjustment value of the temperature inside the furnace in the crystal growth equipment. For example, the temperature control parameter may be +100°C, indicating that it is necessary to increase the temperature inside the furnace of the furnace chamber by 100°C during the target time period based on the current temperature inside the furnace.

[0074] In some embodiments, the temperature control module 740 may feedback the simulated crystal plane temperature to the operator of the target furnace batch, and the aforementioned operator may determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace batch according to the simulated crystal plane temperature.

[0075] In some embodiments, the temperature control module 740 may determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace batch based on the crystal growth information of the target furnace batch and the simulated crystal plane temperature through a temperature control relationship. The crystal growth information may refer to the relevant information of crystal growth in the target furnace batch. For example, the crystal growth information may include but is not limited to crystal type, current crystal growth situation (such as size), current crystal growth duration, target growth duration, etc. The aforementioned target growth duration refers to the expected duration required for crystal growth in the target furnace batch. The crystal growth information may be directly determined by user input or may be obtained in real time during the crystal growth process. For example, the crystal type, target growth duration, etc. may be directly determined by user input; for another example, the current crystal growth situation (such as crystal size), current crystal growth duration may be obtained in real time during the crystal growth process.

[0076] The temperature control module 740 may determine the required crystal plane temperature of the crystal during the target time period according to the crystal growth information, and combine the simulated crystal plane temperature and a preset control rule relationship to determine the temperature control parameters of the crystal growth equipment during the target time period. For more content about the temperature control relationship, reference may be made to the relevant description below in this specification.

[0077] The temperature control module 740 may obtain multiple reference crystal plane temperatures of each reference device in a reference furnace batch and multiple reference control parameters corresponding to the multiple reference crystal plane temperatures among multiple reference devices. Among them, the reference device is a device of the same type as the crystal growth equipment, and the reference furnace batch is a crystal growth furnace batch carried out by the reference device. The reference crystal plane temperature may refer to the crystal plane temperature of the reference device in the reference furnace batch, and the aforementioned reference crystal plane temperature may be a temperature sequence, representing different crystal plane temperatures of the reference device in multiple time periods of the reference furnace batch. The reference control parameter may refer to the temperature control parameter of the reference device in the reference furnace batch, and the aforementioned reference crystal plane temperature may be a temperature control parameter sequence, representing different temperature control parameters of the reference device in multiple time periods of the reference furnace batch.

[0078] In some embodiments, the temperature control module 740 may determine temperature control parameters based on the reference crystal plane temperature, reference control parameters, and simulated crystal plane temperature of multiple reference devices in a reference furnace run.

[0079] For example, the temperature control module 740 may determine at least one reference furnace run based on the simulated crystal plane temperature and the reference crystal plane temperature, and then determine the temperature control parameters for the target time period of the crystal growth device in the target furnace run based on the reference control parameters corresponding to the foregoing at least one reference furnace run. When the number of reference furnace runs is one, the temperature control module 740 may directly determine the reference control parameter corresponding to this reference furnace run as the temperature control parameter for the target time period of the crystal growth device in the target furnace run. It can be understood that in different reference furnace runs, the reference control parameters corresponding to the same reference crystal plane temperature may be different. Therefore, when the number of reference furnace runs is multiple, the temperature control module 740 may comprehensively evaluate the reference control parameters of multiple reference furnace runs to determine the temperature control parameters for the target time period of the crystal growth device in the target furnace run. For example, determine the average value or median value of the reference control parameters corresponding to multiple reference furnace runs, and determine it as the temperature control parameter for the target time period of the crystal growth device in the target furnace run.

[0080] For example, the temperature control module 740 may determine the temperature control relationship between the temperature control parameters and the crystal plane temperature inside the crystal growth device based on the reference crystal plane temperature and the reference control parameters of multiple reference devices in a reference furnace run. Among them, the temperature control relationship may characterize the influence of the adjustment of the temperature control parameters on the crystal plane temperature.

[0081] In some embodiments, the temperature control relationship may be characterized by a temperature control model. The input of the foregoing temperature control model may include the simulated crystal plane temperature from the start of crystal growth to the target time period, and the output is the temperature control parameter from the target time period to the completion of crystal growth.

[0082] The temperature control model may be obtained by training with the reference crystal plane temperature and the reference control parameters of multiple reference devices in a reference furnace run. The temperature control module 740 may occlude or remove the crystal plane temperature in some time periods of the reference crystal plane temperature to obtain a second training sample. The second training label may be the reference control parameter corresponding to the occluded or removed time period. The temperature control module 740 may train the initial temperature control model based on the second training sample and the second training label to obtain the temperature control model.

[0083] The temperature control module 740 can determine the crystal plane temperature required by the crystal growth equipment during the target time period of the target furnace batch based on the crystal growth information of the target furnace batch, and determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace batch through the temperature control relationship based on the crystal plane temperature required by the crystal growth equipment during the target time period of the target furnace batch and the simulated crystal plane temperature, so that the grown crystal meets the requirements.

[0084] The temperature control module 740 can also obtain the crystal detection data of the crystals generated by multiple reference devices during the reference furnace batch. The crystal detection data can refer to the relevant detection data of the crystals. For example, the crystal detection data can include but is not limited to crystal size, crystal quantity, whether there are crystals, etc.

[0085] In some embodiments, the temperature control module 740 can determine the temperature control parameters based on the reference crystal plane temperature, reference control parameters, crystal detection data, and simulated crystal plane temperature of multiple reference devices during the reference furnace batch. The temperature control module 740 can screen the reference crystal plane temperature and reference control parameters of multiple reference devices during the reference furnace batch based on the crystal growth information of the target furnace batch and the crystal detection data of multiple reference devices during the reference furnace batch, obtain the screened reference crystal plane temperature and reference control parameters, and determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace batch based on the screened reference crystal plane temperature, reference control parameters, and simulated crystal plane temperature. For example, the temperature control module 740 can only retain the reference crystal plane temperature and reference control parameters corresponding to the reference furnace batch in which the crystal size in the crystal detection data is equal to or greater than the preset size threshold. By screening the reference furnace batch of the reference device through the crystal detection data, the data that does not meet the requirements can be screened out, making the temperature control parameters during the target time period of the target furnace batch more in line with the requirements of the crystal growth information.

[0086] It should be noted that if only the simulated crystal plane temperature is fed back to the operator for reference, the operator still needs to set the temperature of the crystal growth furnace based on experience, which makes the monitoring of the crystal plane temperature not comprehensive enough, unable to ensure the constant temperature of the crystal plane during the crystal growth process, and thus may reduce the crystal growth quality. In some embodiments of this specification, the temperature control parameters are determined by the temperature control module 740, which can make the determined temperature control parameters more suitable for crystal growth, realize automatic control, reduce labor costs, and reduce the operation error rate.

[0087] In some embodiments, the crystal growth system 200 can also adjust the crystal growth temperature of the crystal growth equipment during the target time period based on the temperature control parameters. Optionally, the process 200 can further include the following steps:

[0088] Step 250: Automatically send a temperature adjustment instruction based on the temperature control parameter to adjust the crystal growth temperature of the crystal growth equipment during the target time period of the target furnace run. In some embodiments, step 250 may be executed by the temperature adjustment module 750.

[0089] The temperature adjustment instruction is a control instruction for each component in the crystal growth equipment to adjust the temperature. The temperature adjustment module 750 may generate a corresponding temperature adjustment instruction based on the temperature control parameter and send the temperature adjustment instruction to the corresponding component of the crystal growth equipment to adjust the crystal growth temperature of the crystal growth equipment during the target time period of the target furnace run, so that the crystal growth temperature meets the requirements of crystal growth and improves the crystal growth quality.

[0090] Exemplarily, when the temperature control parameter includes an increase of +5KW in the power of the furnace chamber in the crystal growth equipment, the temperature adjustment module 750 may convert it into a corresponding temperature adjustment instruction, and the foregoing temperature adjustment instruction may be sent to the furnace chamber; in response to this temperature adjustment instruction, the furnace chamber may increase the power by 5KW on the current power.

[0091] Some embodiments of this specification can automatically control the temperature adjustment of the crystal growth equipment through temperature control parameters, realize the intelligent control of crystal growth, and reduce the cost brought by manual operation.

[0092] It should be noted that in the target furnace run of the crystal growth equipment, the crystal growth system 700 may continuously execute process 200 to determine the crystal plane temperature of each time period, so as to realize the intelligent control of the crystal growth equipment based on the foregoing crystal plane temperature and improve the crystal growth quality.

[0093] It should be noted that the above description of process 200 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the crystal growth system 700 may first execute step 220 and then execute step 210 to also implement the crystal growth method described in the foregoing embodiments of this specification.

[0094] Figure 3 is an exemplary flowchart for determining the first parameter shown in some embodiments of this specification. In some embodiments, process 300 may be executed by the parameter determination module 710. As Figure 3 shown, process 300 may include the following steps:

[0095] Step 310: Obtain the initial first parameter.

[0096] The initial first parameter can reflect the first parameter of a preset object in the initial environment within the crystal growth equipment. Herein, the initial environment may refer to the environment when the manufacturer of the crystal growth equipment measures the first parameter of the preset object, and at this time, the crystal growth equipment has not undergone crystal growth yet. One or more of temperature, magnetic field, electric field, etc. in the initial environment can be determined through presetting. The initial first parameter can be determined through the equipment information of the crystal growth equipment. For example, the design manufacturer of the crystal growth equipment can test the preset object in the initial environment (such as, the temperature is 20 - 30 °C, the magnetic field is 1 - 2 milligauss) during the production of the crystal growth equipment to determine the first parameter of the preset object under the initial conditions.

[0097] It can be understood that during crystal generation, the environment where the preset object is located changes 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, resulting in continuous changes in the first parameter of the preset object in the crystal growth equipment.

[0098] Step 320: Process the initial first parameter based on the reinforcement learning model to determine the target first parameter.

[0099] For each initial first parameter, the parameter determination module 710 can input the initial first parameter into the reinforcement learning model, and the output of the reinforcement learning model is the corresponding target first parameter. Among them, the reward value of the aforementioned reinforcement learning model can be related to the furnace information of the crystal growth equipment. The aforementioned furnace information can be the relevant information during the crystal growth process of the crystal growth equipment, including but not limited to furnace temperature, furnace pressure, exhaust rate, etc. The aforementioned furnace information can be determined based on multiple methods. The aforementioned furnace information can be determined based on detection. For example, the parameter determination module 710 can detect through relevant detection devices to obtain the furnace information. The aforementioned furnace information can also be determined based on calculation. For example, the parameter determination module 710 can also determine the furnace temperature based on the first temperature detection value through the temperature correspondence relationship between the preset furnace temperature and the first temperature detection value. For another example, the parameter determination module 710 can also determine the furnace temperature based on the target furnace batch and the power of the crystal growth equipment through calculation.

[0100] The reinforcement learning model can be used to correct the initial first parameter to obtain the corrected target first parameter. The reinforcement learning model 420 includes an adjustment module 421 and an optimal action determination module 422.

[0101] Such as Figure 4As shown, the parameter determination module 710 can input the initial first parameter 410 into the reinforcement learning model 420, and the output of the reinforcement learning model 420 is the target first parameter 430. Inside the reinforcement learning model 420, the initial first parameter 410 is input into the adjustment module 421, and the adjustment module 421 outputs a set of optional actions; then the initial first parameter 410 and the set of optional actions are input into the optimal action determination module 422, and the optimal action determination module 422 can output the optimal optional action 423. The parameter determination module 710 can use the value corresponding to the optimal optional action 423 output by the optimal action determination module 422 as the output of the reinforcement learning model 420, that is, the target first parameter 430.

[0102] The adjustment module 421 can include an optional action determination sub-module 421-1, a state determination sub-module 421-2, and a reward determination sub-module 421-3. During the prediction process of the reinforcement learning model 420, the adjustment module can determine a set of optional actions based on the initial first parameter 410 through the optional action determination sub-module 421-1. It should be noted that the target first parameter 430 finally determined by adjusting the initial first parameter 410 through the set of optional actions 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 can be understood that since the types of each initial first parameter are different, the correction range corresponding to each initial first parameter can be different. The aforementioned correction range can be preset based on experience.

[0103] During the training process of the reinforcement learning model 420, the state determination sub-module 421-2 and the reward determination sub-module 421-3 in the adjustment module 421 can be respectively used to determine the first parameter of the next state and the reward value.

[0104] The optional action determination sub-module 421-1 can determine a set of optional actions in the current state based on the first parameter of the current state (for example, at the initial execution, the first parameter of the current state can be the initial first parameter 410). The set of optional actions refers to the set of actions that can be performed on the first parameter of the current state in a certain state. It should be noted that the adjusted first parameter should not exceed the correction range corresponding to the initial first parameter.

[0105] The state determination sub-module 421-2 can 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.

[0106] The reward determination sub-module 421-3 can be used to determine the reward value. The reward value can be used to determine the accuracy of the adjusted first parameter. For example, for an action with high accuracy, the reward value can be higher; for an action with low accuracy or negative improvement, the reward value can be lower. The reward value can be represented by a numerical value or other means. 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 in-furnace temperature as an example, the parameter determination module 710 can input the adjusted first parameter into the simulation model, and the output of the simulation model can be the simulated temperature of the crystal growth equipment corresponding to the currently adjusted first parameter. Comparing the aforementioned simulated temperature with the in-furnace temperature of the crystal growth equipment, the reward value of the adjusted first parameter can be determined. Among them, the aforementioned 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 aforementioned temperature difference, the larger the reward value. The simulation model can be obtained by training with the historical data of the crystal growth equipment.

[0107] 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 by various methods, such as a Deep Neural Network (DNN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), etc.

[0108] In some embodiments, the optimal action determination module 422 can be obtained by training multiple groups of third training samples with third training labels based on the reinforcement learning method. For example, a Deep Q-Learning Network (DQN), a Double Deep Q-Learning Network (DDQN), etc. The third training samples can be the historical first parameters of the crystal growth equipment, and the third training labels are the optimal optional actions corresponding to the historical first parameters. The third training samples can be obtained based on the historical data of the crystal growth equipment, and the third training labels can be obtained through the reinforcement learning method.

[0109] In some embodiments, the parameter determination module 710 can periodically execute the reinforcement learning model 420 based on a preset trigger condition and output the optimal optional action 423. For example, if the preset trigger condition is to execute once per furnace of the crystal growth equipment, the parameter determination module 710 can determine the optimal optional action 423 output by the reinforcement learning model 420 in the target furnace as the target first parameter of the target furnace.

[0110] In some embodiments of this specification, by processing the initial first parameter through a reinforcement learning model, the target first parameter can be determined more accurately, improving the accuracy of the determined simulated crystal plane temperature.

[0111] Figure 5 FIG. 5 is another exemplary flowchart for determining the target first parameter according to some embodiments of this specification. In some embodiments, process 500 may be executed by parameter determination module 710. As Figure 5 shown, process 500 may include the following steps:

[0112] Step 510, obtaining the historical first parameter of the crystal growth equipment in multiple historical furnace runs and the historical operating condition parameter corresponding to the historical first parameter.

[0113] The historical furnace run refers to the furnace run of crystal growth that the crystal growth equipment has carried out before the target furnace run. Correspondingly, the historical first parameter refers to the first parameter of the crystal growth equipment in the historical furnace run.

[0114] The historical operating condition parameter refers to the parameter related to the operating condition of the crystal growth equipment in the historical furnace run.

[0115] In some embodiments, the historical operating condition parameter may include the second temperature detection value at the second temperature measurement point of the crystal growth equipment in multiple historical furnace runs. The second temperature measurement point may refer to the position where the crystal growth equipment detects the temperature inside the furnace in multiple historical furnace runs, and the second temperature detection value may refer to the result obtained by the crystal growth equipment detecting the temperature at the second temperature measurement point in multiple historical furnace runs. The second temperature measurement point may be the same as or different from the position of the aforementioned first temperature measurement point.

[0116] In some embodiments, the historical operating condition parameter may further include the historical growth duration of growing crystals by the crystal growth equipment in the historical furnace run. The historical growth duration refers to the duration used for crystal growth by the crystal growth equipment in multiple historical furnace runs.

[0117] In some embodiments, the historical operating condition parameter may further include other parameters. For example, the historical operating condition parameter may further include, but is not limited to, the in-furnace power, in-furnace air pressure, exhaust rate, etc. of the historical furnace run.

[0118] The parameter determination module 710 may obtain the historical first parameters of the crystal growth equipment in multiple historical furnace runs and the historical operating condition parameters corresponding to the historical first parameters through the historical data of the crystal growth equipment. Exemplarily, the crystal growth equipment may further include a storage module. Each time the crystal growth equipment performs crystal growth, it may detect relevant data. For example, it may detect the historical first parameters and the historical operating condition parameters, and store them as historical data in the aforementioned storage module. When needed, the parameter determination module 710 may obtain the historical first parameters of the crystal growth equipment in multiple historical furnace runs and the historical operating condition parameters corresponding to the historical first parameters from the aforementioned storage module.

[0119] Step 520: Determine the target first parameter based on the historical first parameters and the historical operating condition parameters of multiple historical furnace runs.

[0120] In some embodiments, when the historical operating condition parameters include the second temperature detection values of the second temperature measurement points of the crystal growth equipment in multiple historical furnace runs, the parameter determination module 710 may perform analysis and processing based on the historical first parameters and the second temperature detection values of multiple historical furnace runs to determine the second correspondence between the temperature detection values of the second temperature measurement points and the first parameter. The aforementioned second correspondence may reflect the correspondence between the temperature detection values of the second temperature measurement points and the first parameter of the crystal growth equipment. The second correspondence may be characterized as a fitting function, a machine learning model, etc. It can be understood that when the first parameter includes multiple types, the second correspondence may 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 may include the correspondence between the temperature detection value of the second temperature measurement point and the thermal conductivity and electrical conductivity of the crucible.

[0121] In some embodiments, the parameter determination module 710 may obtain the third temperature detection value of the second temperature measurement point of the crystal growth equipment in the target furnace run, and determine the first parameter based on the third temperature detection value and the second correspondence. The third temperature detection value refers to the result obtained by detecting the temperature of the second temperature measurement point in the target furnace run of the crystal growth equipment. It can be understood that when the first temperature measurement point is the same as the second temperature measurement point, 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 correspondence, and determine it as the target first parameter.

[0122] In some embodiments, when the historical operating condition parameters include the historical growth duration of crystal growth in the historical furnace runs of the crystal growth equipment, the parameter determination module 710 may determine a third corresponding relationship based on the historical first parameters of multiple historical furnace runs, various historical related parameters, and multiple historical growth durations. Among them, the aforementioned third corresponding relationship may reflect the third corresponding relationship between the growth duration of the crystal and the first parameter. The third corresponding relationship may 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 may include the corresponding relationship between each first parameter and the growth duration of the crystal.

[0123] In some embodiments, the parameter determination module 710 may obtain the target growth duration of crystal growth in the target furnace run of the crystal growth equipment; based on the target crystal growth duration and the third corresponding relationship, determine the first parameter. The parameter determination module 710 may determine the first parameter corresponding to the target growth duration based on the third corresponding relationship and determine it as the target first parameter.

[0124] In some embodiments, the parameter determination module 710 may also establish a parameter prediction model based on the historical first parameters and historical operating condition parameters of multiple historical furnace runs; determine the preset operating condition parameters of the crystal growth equipment in the target furnace run; and determine the target first parameter based on the preset operating condition parameters and the parameter prediction model.

[0125] The preset operating condition parameters may refer to the operating condition parameters in the target furnace run determined in advance. The preset operating condition parameters may include, but are not limited to, the furnace temperature, furnace pressure, exhaust rate, and furnace power in each time period of the crystal growth equipment in the target furnace run determined in advance. The parameter determination module 710 or the user may plan the growth situation of the target furnace run based on the crystal growth information, so as to determine the preset operating condition parameters of the crystal growth equipment in the target furnace run. For more content about the crystal growth information, please refer to Figure 2 and its related descriptions.

[0126] In some embodiments, the parameter determination module 710 may input the preset operating condition parameters of the target furnace run into the parameter prediction model, and the output of the parameter prediction model is the first parameter under the preset operating condition parameters. The parameter determination module 710 may determine the first parameter under the preset operating condition 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 a combination of a convolutional neural network model, a deep learning model, and a random forest model. Some embodiments of this specification can quickly and accurately determine the first parameter through the parameter prediction model, improving the processing efficiency.

[0127] The 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 the historical operating condition parameters of a certain historical furnace batch, and the fourth training labels may include the historical first parameter corresponding to the historical furnace batch.

[0128] Some embodiments of this specification analyze and process the historical first parameters and historical operating condition parameters of multiple historical furnace batches of the crystal growth equipment to determine the first parameter, so as to more accurately determine the first parameter of the crystal growth equipment in the target furnace batch, and avoid directly judging based on the initial first parameter, resulting in errors in determining the crystal plane temperature.

[0129] Figure 6 is another exemplary flowchart for determining the target first parameter shown in some embodiments of this specification. In some embodiments, process 600 may be executed by the parameter determination module 710. As Figure 6 shown, process 600 may include the following steps:

[0130] Step 610, obtain the first correspondence relationship between the furnace batches of the crystal growth equipment and the first parameter.

[0131] In some embodiments, for each of the multiple preset furnace batches of the crystal growth equipment, the parameter determination module 710 may process the initial first parameter based on the reinforcement learning model to determine the reference first parameter corresponding to each preset furnace batch. Among them, the multiple preset furnace batches may refer to multiple furnace batches preset by the crystal growth equipment. For example, the multiple preset furnace batches may be the first 20 furnace batches of the crystal growth equipment. The reference first parameter is the first parameter of the crystal growth equipment in the preset furnace batch. For more content about the reinforcement learning model, reference can be made to Figure 3 and its related descriptions.

[0132] The parameter determination module 710 may determine the first correspondence relationship between the furnace batches of the crystal growth equipment and the first parameter based on the reference first parameters corresponding to the multiple preset furnace batches. Similar to the second correspondence relationship and the third correspondence relationship, the first correspondence relationship may also be characterized by a fitting function or a machine learning model, etc. For example, the parameter determination module 710 may perform data fitting on the reference first parameters corresponding to the multiple preset furnace batches to determine the fitting function between the furnace batches of the crystal growth equipment and the first parameter, and determine the foregoing fitting function as the first correspondence relationship. For another example, the parameter determination module 710 determines the preset furnace batch as the fifth training sample, determines the reference first parameter corresponding to the preset furnace batch as the fifth training label, trains based on the foregoing fifth training sample and the fifth training label to determine the machine learning model, and determines the foregoing machine learning model as the first correspondence relationship.

[0133] Step 620: Determine a target first parameter based on a target furnace batch and a first corresponding relationship.

[0134] For example, when the first corresponding relationship is a fitting function, the parameter determination module 710 may calculate based on the target furnace batch to determine the target first parameter. For another example, when the first corresponding relationship is a machine learning model, the parameter determination module 710 may input the target furnace batch into the aforementioned machine learning model, and the output of the aforementioned machine learning model is the target first parameter.

[0135] Some embodiments of this specification determine the target first parameter through the first corresponding relationship, avoiding the computational amount brought by continuously processing based on the reinforcement learning model, and improving the computational efficiency of the crystal growth system 700 on the basis of ensuring the accuracy of the target first parameter.

[0136] Figure 7 is an exemplary module diagram of a crystal growth system shown in some embodiments of this specification.

[0137] As Figure 7 shown, the crystal growth system 700 may include a parameter determination module 710, a temperature detection module 720, and a temperature determination module 730.

[0138] The parameter determination module 710 may be used to determine a parameter set of the crystal growth device in a target furnace batch, where the parameter set includes at least one target parameter, and the target parameter is a parameter that affects crystal growth in the crystal growth device. In some embodiments, the target parameter includes a target first parameter and a target second parameter. The target first parameter includes the first parameter of a preset substance in the crystal growth device in the target furnace batch, and the first parameter reflects the material physical properties related to crystal growth in the preset substance. The target second parameter includes the second parameter of the crystal growth device in the target furnace batch, and the second parameter includes the parameters of the relevant settings of the crystal growth device.

[0139] In some embodiments, the parameter determination module 710 may further be used to obtain an initial first parameter, where the initial first parameter reflects the first parameter of the preset substance in the crystal growth device in an 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 device.

[0140] In some embodiments, the parameter determination module 710 may further be used to obtain the first corresponding relationship between the furnace batch of the crystal growth device and the first parameter; determine the target first parameter based on the target furnace batch and the first corresponding relationship.

[0141] In some embodiments, the parameter determination module 710 may further be configured to, for each of a plurality of preset furnace runs of the crystal growth device, process an initial first parameter based on a reinforcement learning model to determine a reference first parameter corresponding to the preset furnace run, where the initial first parameter reflects the first parameter of a preset material in the crystal growth device in an initial environment, the reference first parameter is the first parameter of the crystal growth device in the preset furnace run, and the reward value of the reinforcement learning model is related to the in-furnace information of the crystal growth device; and determine a first correspondence between the furnace runs of the crystal growth device and the first parameter based on the reference first parameters corresponding to the plurality of preset furnace runs.

[0142] In some embodiments, the parameter determination module 710 may further be configured to obtain historical first parameters of the crystal growth device in a plurality of historical furnace runs and historical operating condition parameters corresponding to the historical first parameters, where the historical first parameters include the first parameters reflecting the preset material in the crystal growth device in the historical furnace runs; and determine a target first parameter based on the historical first parameters and the historical operating condition parameters of the plurality of historical furnace runs. In some embodiments, the historical operating condition parameters include second temperature detection values at a second temperature measurement point of the crystal growth device in a plurality of historical furnace runs, and the parameter determination module 710 may further be configured to determine a second correspondence based on the historical first parameters and the second temperature detection values of the plurality of historical furnace runs, where the second correspondence reflects the correspondence between the temperature detection value at the second temperature measurement point of the crystal growth device and the first parameter; obtain a third temperature detection value at the second temperature measurement point of the crystal growth device in a target furnace run; and determine the target first parameter based on the third temperature detection value and the second correspondence. In some embodiments, the historical operating condition parameters include the historical growth duration of growing crystals in the historical furnace runs of the crystal growth device, and the parameter determination module 710 may further be configured to determine a third correspondence based on the historical first parameters and the historical growth duration of the plurality of historical furnace runs, where the third correspondence reflects the correspondence between the growth duration of the crystals and the first parameter; obtain a target growth duration of growing crystals in the target furnace run of the crystal growth device; and determine the target first parameter based on the target growth duration and the third correspondence. In some embodiments, the parameter determination module 710 may further be configured to establish a parameter prediction model based on the historical first parameters and the historical operating condition parameters of the plurality of historical furnace runs; determine preset operating condition parameters of the crystal growth device in a target furnace run; and determine the target first parameter based on the preset operating condition parameters and the parameter prediction model.

[0143] In some embodiments, the parameter determination module 710 may further be configured to obtain multiple sets of reference data, each set of reference data including a plurality of candidate first parameters and verification information corresponding to the plurality of candidate first parameters, and each candidate first parameter reflecting a material physical property of a preset material in the crystal growth device; and process the multiple sets of reference data through an importance analysis model to determine the type of the first parameter, where the importance analysis model is a random forest model.

[0144] The temperature detection module 720 can be used to obtain the first temperature detection value of the first temperature measurement point of the crystal growth equipment during the target time period of the target furnace batch.

[0145] The temperature determination module 730 can be used to determine the simulated crystal plane temperature corresponding to the first temperature detection value based on the target parameters and the first temperature detection value through the crystal plane temperature determination model.

[0146] In some embodiments, Figure 7 The illustrated crystal growth system 700 may further include a temperature control module 740. The aforementioned temperature control module 740 can be used to determine the temperature control parameters of the crystal growth equipment during the target time period of the target furnace batch based on the simulated crystal plane temperature. In some embodiments, the temperature control module 740 can further be used to obtain the multiple reference crystal plane temperatures of each reference device in the reference furnace batch and the multiple reference control parameters corresponding to the multiple reference crystal plane temperatures among multiple reference devices, where the reference devices are devices of the same type as the crystal growth equipment; determine the temperature control parameters based on the reference crystal plane temperatures, reference control parameters, and simulated crystal plane temperature of the multiple reference devices in the reference furnace batch. In some embodiments, the temperature control module 740 can further be used to obtain the crystal detection data of the crystals generated by the multiple reference devices in the reference furnace batch; determine the temperature control parameters based on the reference crystal plane temperatures, reference control parameters, crystal detection data, and simulated crystal plane temperature of the multiple reference devices in the reference furnace batch.

[0147] In some embodiments, Figure 7 The illustrated crystal growth system 700 may further include a temperature adjustment module 750. The aforementioned temperature adjustment module 750 can be used to automatically send a temperature adjustment instruction based on the temperature control parameters to adjust the crystal growth temperature of the crystal growth equipment during the target time period of the target furnace batch.

[0148] It should be understood that Figure 7 The illustrated crystal growth system 700 and its modules can be implemented in various ways. Exemplarily, the crystal growth system 700 can be applied to the processor of a crystal growth device, and the crystal growth device may further include a crystal growth equipment. The aforementioned processor can control the crystal growth equipment through the aforementioned crystal growth system 700 to implement the crystal growth method described in any of the following embodiments of this specification.

[0149] It should be noted that the above description of the crystal growth system 700 and its modules is only for convenience of description and does not limit this specification to the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of the various modules, or form a subsystem and connect it with other modules. In some embodiments,Figure 7 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 single module can implement the functions of two or more of the above modules. For example, each module can share a storage module, or each module can have its own storage module respectively. Such variations are all within the scope of protection of this specification.

[0150] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0151] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0152] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification are not used to limit the order of the processes and methods of this specification. Although some currently considered useful invention embodiments are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of explanation. 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 conform to the essence 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 through software solutions, such as installing the described system on existing servers or mobile devices.

[0153] Similarly, it should be noted that, in order to simplify the expression of this specification disclosure and thus help the understanding of one or more invention embodiments, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiments disclosed above.

[0154] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of embodiments are, in some examples, modified by the modifiers "about", "approximately" or "substantially". Unless otherwise stated, "about", "approximately" or "substantially" indicate that the said numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0155] For each patent, patent application, patent application publication and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. Except for the application history documents that are inconsistent with or conflict with the content of this specification, and also except for the documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0156] Finally, it should be understood that the embodiments described in this specification are only used 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 to be consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described 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 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; In a target time period of the target furnace, obtaining a first temperature detection value of a first temperature measuring point of the crystal growth equipment; Based on the target parameter and the first temperature detection value, determining a simulated crystal surface temperature corresponding to the first temperature detection value; 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.

2. The method according to claim 1, characterized in that 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.

3. The method according to claim 1, characterized in that 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 furnace, wherein the first parameter reflects a physical property of a 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 furnace, and the second parameter includes a parameter related to the setting of the crystal growth equipment.

4. The method according to claim 3, characterized in that 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; and / or The preset objects include at least one of a crucible, a thermal insulation felt, a raw material melt, and a protective gas.

5. The method according to claim 3, characterized in that 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 furnace information of the crystal growth equipment.

6. The method according to claim 3, characterized in that 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; Based on the target heat and the first corresponding relationship, the target first parameter is determined; wherein the first corresponding relationship is determined by the following steps: For each of a plurality of preset furnaces of the crystal growth device, an initial first parameter is processed 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 the preset object in the crystal growth device in an initial environment, the reference first parameter is the first parameter of the crystal growth device in the preset furnace, and the reward value of the reinforcement learning model is related to the furnace information of the crystal growth device; 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.

7. The method according to claim 3, characterized in that The parameter set for determining the target heat of the crystal growth equipment includes: Acquire 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 furnaces and the historical operating condition parameters.

8. The method according to claim 7, characterized in that The historical operating condition parameters include second temperature detection values ​​of the crystal growth equipment at second temperature measurement points of the plurality of historical furnaces; The determining the first parameter based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters comprises: Determine a second corresponding relationship based on the historical first parameters and the second temperature detection values ​​of the plurality of 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; Acquire a third temperature detection value of the crystal growth equipment at the second temperature measurement point of the target furnace; The target first parameter is determined based on the third temperature detection value and the second corresponding relationship.

9. The method according to claim 7, characterized in that The historical operating condition parameters include the historical growth time of the crystal growth 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 comprises: Based on the historical first parameters of the plurality of historical furnaces and the historical growth durations, determining a third corresponding relationship, 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.

10. The method according to claim 3, characterized in that The type of the first parameter is determined by the following steps: Acquire multiple groups of reference data, each group 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 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.

11. The method according to claim 7, characterized in that The determining the target first parameter based on the historical first parameters of the plurality of historical heats and the historical operating condition parameters comprises: Establishing a parameter prediction model based on the historical first parameters of the plurality of historical furnaces and the historical operating condition parameters; Determining preset operating parameters of the crystal growth equipment at the target furnace; Based on the preset operating condition parameters and the parameter prediction model, the target first parameter is determined.

12. The method according to claim 1, characterized in that The step of determining the temperature control parameter of the crystal growth equipment in the target time period of the target furnace based on the simulated crystal surface temperature includes: Acquire multiple reference crystal surface temperatures of each of the reference devices in a 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; The temperature control parameter is determined based on the reference crystal surface temperature of the reference furnace, the reference control parameter and the simulated crystal surface temperature of the plurality of reference devices.

13. The method according to claim 12, characterized in that The determining 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: Acquire crystal detection data of crystals generated by a plurality of the reference devices in the reference heat; The temperature control parameter is determined based on the reference crystal surface temperature of the reference furnace of a plurality of the reference devices, the reference control parameter, the crystal detection data and the simulated crystal surface temperature.

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