Monocrystalline silicon heat treatment method customized according to different application requirements

Abstract

The invention discloses a monocrystalline silicon heat treatment method customized for different application requirements, and relates to the technical field of monocrystalline silicon heat treatment, and the method comprises the following steps: determining the application field and specific requirements of monocrystalline silicon, and determining the performance index of a monocrystalline silicon material after heat treatment; carrying out heat treatment on the monocrystalline silicon according to the performance index of the monocrystalline silicon; and carrying out quality detection and evaluation on the monocrystalline silicon subjected to heat treatment. According to the invention, through application fields of monocrystalline silicon, performance requirements of each field for monocrystalline silicon are determined, accurate identification of different application requirements is realized, a relation model between monocrystalline silicon heat treatment parameters and material performance is established based on a deep learning algorithm, intelligent optimization of the heat treatment parameters is realized, and the accuracy of the heat treatment parameters is improved. The heat treatment parameter optimization efficiency and accuracy are improved, the heat treatment parameters are adjusted in time by monitoring the temperature, atmosphere, surface change and other parameters in real time, and it is ensured that the heat treatment process is always in the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of single crystal silicon heat treatment, and in particular to a single crystal silicon heat treatment method customized for different application requirements. Background Art

[0002] In recent years, single crystal silicon has been widely used in many fields such as solar cells, integrated circuits, optoelectronic devices, etc., promoting the continuous progress of related technologies. In order to meet the strict requirements of single crystal silicon performance in different application scenarios, heat treatment technology is widely used as an effective means to improve and regulate the microstructure and electrical properties of single crystal silicon. Traditional single crystal silicon heat treatment methods, such as annealing, diffusion, oxidation, etc., have improved the quality of single crystal silicon materials to a certain extent and promoted the improvement of electronic device performance.

[0003] However, with the increasing diversification and complexity of market demand, traditional heat treatment methods have gradually exposed some shortcomings, limiting their further expansion in high-end application fields. There is a lack of customized treatment solutions for specific application needs. Traditional methods often use universal heat treatment parameters, which are difficult to meet the personalized requirements for single crystal silicon performance in different application scenarios, resulting in a large gap between the performance of the treated material and the actual application needs. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for heat treatment of single crystal silicon customized for different application requirements to solve the problem of personalized requirements for single crystal silicon performance in different application scenarios.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for heat treating single crystal silicon customized for different application requirements, which comprises:

[0008] Determine the application areas and specific needs of single crystal silicon, and clarify the performance indicators of single crystal silicon materials after heat treatment;

[0009] According to the performance index of the single crystal silicon, heat treatment is performed on the single crystal silicon;

[0010] Perform quality inspection and evaluation on single crystal silicon that has completed heat treatment.

[0011] As a preferred solution of the method for heat treatment of single crystal silicon customized for different application requirements of the present invention, wherein: the application field and specific requirements of single crystal silicon are determined, and the performance index of the single crystal silicon material after heat treatment is clarified, the specific steps are as follows:

[0012] Based on the application fields of monocrystalline silicon and its role in these fields, determine the performance requirements of monocrystalline silicon in various fields;

[0013] Based on performance requirements, clarify the various performance indicators of single crystal silicon materials.

[0014] As a preferred solution of the method for heat treating single crystal silicon customized for different application requirements of the present invention, wherein: the heat treatment of single crystal silicon is performed according to the performance index of single crystal silicon, and the specific steps are as follows:

[0015] Physically treat the single crystal silicon according to performance indicators;

[0016] Based on deep learning algorithms, a relationship model between single crystal silicon heat treatment parameters and material properties was established;

[0017] Through a multi-step progressive heat treatment method, heat treatment is performed on the single crystal silicon according to the heat treatment parameters output by the relational model;

[0018] Install sensors on the heat treatment equipment to monitor the material status during the heat treatment process in real time and automatically adjust the heat treatment parameters.

[0019] As a preferred solution of the method for heat treatment of single crystal silicon customized for different application requirements described in the present invention, wherein: the single crystal silicon is physically treated according to the performance index, and the specific steps are as follows:

[0020] Based on the application requirements and performance indicators of monocrystalline silicon, the monocrystalline silicon raw materials are physically treated, and the monocrystalline silicon wafers are placed in an ultrasonic cleaning tank and cleaned with deionized water and acetone in sequence to remove surface pollutants;

[0021] Use a diamond saw blade to cut the cleaned single crystal silicon wafer into standard sizes;

[0022] The surface of single crystal silicon wafer is polished using chemical mechanical polishing technology.

[0023] As a preferred solution of the method for heat treatment of single crystal silicon customized for different application requirements described in the present invention, wherein: based on the deep learning algorithm, a relationship model between heat treatment parameters of single crystal silicon and material properties is established, and the specific steps are as follows:

[0024] Based on the performance requirements of different application fields, single crystal silicon heat treatment equipment is used to conduct heat treatment experiments on single crystal silicon;

[0025] After each experiment, the corresponding performance indicators of single crystal silicon materials for different applications are measured using testing instruments;

[0026] Record the performance index data of each experiment to form a performance index feature vector and the corresponding heat treatment parameters of the single crystal silicon application, and perform cleaning and normalization processing, and divide them into training set and verification set respectively according to proportion;

[0027] Based on the deep neural network, a relationship model between the performance index of single crystal silicon material and the heat treatment parameters is constructed. By inputting the performance index of single crystal silicon, the model outputs targeted heat treatment parameters of single crystal silicon;

[0028] Use the data loader to batch read and load the training set and validation set data, and set the model training rounds;

[0029] For each training round, forward propagation is performed, the performance indicator feature vector is input, the predicted value of the heat treatment parameter is calculated, and the output loss value is calculated;

[0030] Back propagation, introduce mean square error as the loss function, calculate the gradient of model parameters, and use Adam optimization algorithm to update model parameters;

[0031] During the model training process, the feature enhancement function is introduced, and the expression is as follows:

[0032]

[0033]

[0034] Among them, f(x) is the input vector after feature enhancement, x is the original input vector, α is the parameter of the control function slope, g(x) is the feature enhancement function, and w i is the weight of the i-th feature, β i is the frequency parameter of the i-th feature, x i is the i-th eigenvalue, n is the number of features, and i is the index variable of the number of features;

[0035] After each training round, the model performance is evaluated on the validation set, and the performance indicators and loss values ​​of the training set and validation set output in each round are compared;

[0036] If the performance of the current model on the validation set is better than that of the previous model, the current model is saved; otherwise, the previous model continues to be used.

[0037] As a preferred solution of the method for heat treatment of single crystal silicon customized for different application requirements described in the present invention, wherein: the heat treatment of single crystal silicon is performed by a multi-step progressive heat treatment method according to the heat treatment parameters output by the relationship model, and the specific steps are as follows:

[0038] Calibrate single crystal silicon heat treatment equipment;

[0039] The required single crystal silicon performance indicators are input into the trained model, and the model outputs a set of heat treatment parameters. The initial parameters of the heat treatment equipment are set, and the single crystal silicon pretreated according to the application requirements is placed into the heat treatment equipment to start the heat treatment;

[0040] The heat treatment process is divided into three stages: the preheating stage, from room temperature to half of the maximum temperature, slow heating, the main treatment stage, heating to the maximum temperature, rapid heating, and the cooling stage, the temperature in the heat treatment equipment is reduced to room temperature.

[0041] As a preferred solution of the method for heat treatment of single crystal silicon customized for different application requirements described in the present invention, wherein: a sensor is installed on the heat treatment equipment to monitor the material state during the heat treatment process in real time and automatically adjust the heat treatment parameters. The specific steps are as follows:

[0042] In the multi-step progressive heat treatment process, an embedded controller is used as a central processor to receive the data monitored by the sensor in real time;

[0043] Integrate the PID control algorithm into the central controller to adjust the heating parameters of the heat treatment equipment in real time according to the multi-step progressive heating scheme and data feedback;

[0044] According to actual needs, set the initial PID coefficient and set value, read the parameters inside the heat treatment equipment in real time through the sensor interface, and calculate the error between the set value and the actual value;

[0045] Based on the error value, the proportional term, integral term and differential term are calculated and summed up as the total PID output;

[0046] The total PID output is sent to each parameter control unit of the heat treatment equipment in the form of a voltage signal to automatically adjust the heat treatment parameters;

[0047] At the same time, users are allowed to manually adjust PID parameters through the user interface, observe the control effect and optimize it;

[0048] Run the PID control algorithm on actual heat treatment equipment, monitor the response and stability of the system, and verify the effectiveness and reliability of the PID control algorithm;

[0049] According to the test results, gradually adjust the PID parameters until the system achieves the best control effect. During the optimization process, the system performance before and after each adjustment should be recorded for comparison and analysis;

[0050] During the entire heat treatment process, multiple high-temperature resistant temperature sensors are installed inside the heat treatment equipment to monitor the temperature changes inside the equipment in real time, and the central controller automatically adjusts the heating rate according to the temperature curve;

[0051] The atmosphere sensor monitors the atmosphere type, concentration and pressure changes in the equipment in real time, and the central controller automatically adjusts the gas replenishment and exhaust;

[0052] An optical microscope is installed during the cooling stage to monitor the surface changes of the single crystal silicon heated sample in real time, and the central controller adjusts the cooling rate.

[0053] As a preferred solution of the method for heat treatment of single crystal silicon customized for different application requirements described in the present invention, wherein: the quality inspection and evaluation of the single crystal silicon that has completed the heat treatment is carried out, and the specific steps are as follows:

[0054] After the temperature inside the heat treatment equipment drops to room temperature, the single crystal silicon sample that has completed the heat treatment is taken out, and the surface of the single crystal silicon sample is cleaned using ultrasonic waves;

[0055] Use atomic force microscopy to detect the surface roughness of single crystal silicon samples, and use X-ray diffractometer to detect the crystal structure of single crystal silicon wafers;

[0056] Use a photoelectric conversion efficiency tester to test the photoelectric conversion efficiency of single crystal silicon wafers;

[0057] Use minority carrier lifetime tester to detect minority carrier lifetime of single crystal silicon wafer;

[0058] Use minority carrier diffusion length tester to test minority carrier diffusion length of single crystal silicon wafers, as well as other performance indicators required by the application;

[0059] Record various test data, compare them with the performance indicators of application requirements, and generate an evaluation report.

[0060] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for heat treatment of single crystal silicon customized for different application requirements as described in the first aspect of the present invention is implemented.

[0061] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for heat treatment of single crystal silicon customized for different application requirements as described in the first aspect of the present invention is implemented.

[0062] The beneficial effects of the present invention are as follows: the present invention determines the performance requirements of single crystal silicon in various fields based on the application fields of single crystal silicon, thereby realizing accurate identification of different application requirements; based on a deep learning algorithm, a relationship model between single crystal silicon heat treatment parameters and material properties is established, thereby realizing intelligent optimization of heat treatment parameters, improving the optimization efficiency and accuracy of heat treatment parameters; through a multi-step progressive heat treatment method, the temperature and other parameters are gradually adjusted to ensure that the single crystal silicon material can achieve the best heat treatment effect at each stage, improve the heat treatment consistency and stability of the single crystal silicon material; through real-time monitoring of parameters such as temperature, atmosphere and surface changes, the heat treatment parameters are adjusted in time to ensure that the heat treatment process is always in the best state. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0064] Figure 1 This is a flow chart of the method for heat treating single crystal silicon customized for different application requirements in Example 1.

[0065] Figure 2 This is a flow chart of heat treatment of single crystal silicon according to the performance indicators of single crystal silicon in Example 1. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0069] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for heat treatment of single crystal silicon customized for different application requirements, comprising the following steps:

[0070] S1. Determine the application areas and specific needs of single crystal silicon, and clarify the performance indicators of single crystal silicon materials after heat treatment.

[0071] Furthermore, based on the application fields of monocrystalline silicon and its role in these fields, the performance requirements of monocrystalline silicon in various fields are determined;

[0072] Based on performance requirements, clarify the various performance indicators of single crystal silicon materials.

[0073] S2. heat-treating the single crystal silicon according to its performance indexes.

[0074] Physically treat the single crystal silicon according to performance indicators;

[0075] Based on deep learning algorithms, a relationship model between single crystal silicon heat treatment parameters and material properties was established;

[0076] Through a multi-step progressive heat treatment method, heat treatment is performed on the single crystal silicon according to the heat treatment parameters output by the relational model;

[0077] Install sensors on the heat treatment equipment to monitor the material status during the heat treatment process in real time and automatically adjust the heat treatment parameters.

[0078] S2.1. Physically process the single crystal silicon according to the performance indexes.

[0079] Furthermore, based on the application requirements and performance indicators of monocrystalline silicon, the purity of monocrystalline silicon raw materials with a purity of ≥ 99.99%

[0080] Physical treatment is performed by placing the single crystal silicon wafer in an ultrasonic cleaning tank and cleaning it with deionized water and acetone in sequence to remove surface contaminants;

[0081] Use a diamond saw blade to cut the cleaned single crystal silicon wafer into standard sizes;

[0082] The surface of the single crystal silicon wafer is polished using chemical mechanical polishing (CMP).

[0083] S2.2. Based on the deep learning algorithm, a relationship model between single crystal silicon heat treatment parameters and material properties is established.

[0084] Furthermore, based on the performance requirements of different application fields, single crystal silicon heat treatment equipment is used to conduct heat treatment experiments on single crystal silicon;

[0085] After each experiment, the corresponding performance indicators of single crystal silicon materials for different applications are measured using testing instruments;

[0086] Record the performance indicator data of each experiment and the corresponding heat treatment parameters of the single crystal silicon application according to different application requirements, and clean them to remove outliers and missing values ​​to ensure the quality and reliability of the data;

[0087] The cleaned performance index data are combined into a performance index feature vector, combined with the corresponding heat treatment parameters, and divided into 80% as a training set and 20% as a validation set to ensure that the distribution of the data set is representative;

[0088] Furthermore, a relationship model between the performance index of single crystal silicon material and the heat treatment parameters is constructed based on a deep neural network. The model structure includes an input layer, multiple hidden layers and an output layer. By inputting the performance index of single crystal silicon, the model outputs targeted heat treatment parameters of single crystal silicon.

[0089] Use the data loader to batch read and load the training set and validation set data. The input layer receives the performance indicator feature vector and sets the model training rounds to 100.

[0090] In the hidden layer, for each training round, forward propagation is performed, the performance indicator feature vector is input, the predicted value of the heat treatment parameter is calculated, and the output loss value is calculated;

[0091] Back propagation, introduces mean square error as the loss function to measure the difference between the model prediction value and the actual value. The expression is as follows:

[0092]

[0093] Among them, m is the number of samples, y j is the actual value of the jth sample, is the predicted value of the jth sample, where j is the index variable of the number of samples;

[0094] Calculate the gradient of model parameters, use the Adam optimization algorithm to minimize the loss function, and update the model parameters;

[0095] During the model training process, a feature enhancement function is introduced to enhance the expressiveness of the input features. The expression is as follows:

[0096]

[0097]

[0098] Among them, f(x) is the input vector after feature enhancement, x is the original input vector, α is the parameter of the control function slope, g(x) is the feature enhancement function, and w i is the weight of the i-th feature, β i is the frequency parameter of the i-th feature, x iis the i-th eigenvalue, n is the number of features, and i is the index variable of the number of features;

[0099] Furthermore, after each training round, the model performance is evaluated on the validation set, and the performance indicators and loss values ​​output by the training set and validation set in each round are compared;

[0100] If the performance of the current model on the validation set is better than that of the previous model, save the current model; otherwise, continue to use the previous model;

[0101] When the number of training rounds exceeds 50, after each evaluation, compare the loss values ​​of the validation set for the last 10 consecutive times. If the change in the loss value does not exceed 1%, the training is terminated early. Otherwise, the training is continued until the last round.

[0102] It should be noted that a relationship model between single crystal silicon heat treatment parameters and material properties was established through a deep learning algorithm, which provides precise control for obtaining single crystal silicon performance parameters for different application requirements and improves the customized production efficiency of single crystal silicon for different application requirements.

[0103] S2.3. Heat-treating the single crystal silicon by a multi-step progressive heat treatment method according to the heat treatment parameters output by the relationship model.

[0104] Furthermore, the single crystal silicon heat treatment equipment is calibrated;

[0105] Furthermore, the required single crystal silicon performance indicators are input into the trained model, and the model outputs a set of heat treatment parameters, and the initial parameters of the heat treatment equipment are set. The single crystal silicon pretreated according to the application requirements is placed in the heat treatment equipment to start the heat treatment;

[0106] The heat treatment process was divided into three stages: the preheating stage, from room temperature to half of the maximum temperature at a rate of 10°C per minute;

[0107] In the main treatment stage, the temperature is rapidly increased at a rate of 20°C per minute to the highest temperature, and maintained for 1 hour;

[0108] During the cooling stage, the heat treatment equipment is cooled naturally and the temperature drops to room temperature.

[0109] S2.4. Install sensors on the heat treatment equipment to monitor the material status during the heat treatment process in real time and automatically adjust the heat treatment parameters.

[0110] Furthermore, during the multi-step progressive heat treatment process, an embedded controller is used as a central processor to receive data monitored in real time by sensors;

[0111] Integrate the PID control algorithm into the central controller to adjust the heating parameters of the heat treatment equipment in real time according to the multi-step progressive heating scheme and data feedback;

[0112] According to actual needs, set the initial PID coefficient and set value, read the parameters inside the heat treatment equipment in real time through the sensor interface, and calculate the error between the set value and the actual value;

[0113] Based on the error value, the proportional term, integral term and calculus are calculated and summed as the total PID output. The expression is as follows:

[0114]

[0115] Among them, u(t) is the total output of PID, e(t) is the error value, K p is the proportional gain of the controller, which determines the influence of the error on the output. j is the integral gain of the controller, which determines the influence of error accumulation on the output. d is the differential gain of the controller, which determines the influence of the error change rate on the output. (t)dt is the integral of the error e(t) over time from time 0 to the current time t, and de(t) / dt is the rate of change of the error;

[0116] Furthermore, the total PID output is sent to each parameter control unit of the heat treatment equipment in the form of a voltage signal to automatically adjust the heat treatment parameters;

[0117] At the same time, users are allowed to manually adjust PID parameters through the user interface, observe the control effect and optimize it;

[0118] Test and run the PID control algorithm on actual heat treatment equipment to monitor the response and stability of the system. The effectiveness and reliability of the PID control algorithm can be verified through testing;

[0119] Furthermore, according to the test results, the PID parameters are gradually adjusted until the system achieves the best control effect. During the optimization process, the system performance before and after each adjustment should be recorded for comparison and analysis;

[0120] During the entire heat treatment process, multiple high-temperature resistant temperature sensors are installed inside the heat treatment equipment to monitor the temperature changes inside the equipment in real time, and the central controller automatically adjusts the heating rate according to the temperature curve;

[0121] The atmosphere sensor monitors the atmosphere type, concentration and pressure changes in the equipment in real time, and the central controller automatically adjusts the gas replenishment and exhaust;

[0122] An optical microscope is installed during the cooling stage to monitor the surface changes of the heated single crystal silicon sample in real time, and the central controller adjusts the cooling rate;

[0123] It should be noted that by adjusting the heat treatment parameters in real time, precise control of material properties can be achieved, thereby improving product quality and production efficiency.

[0124] S3. Conduct quality inspection and evaluation on the single crystal silicon that has completed the heat treatment.

[0125] Furthermore, after the temperature inside the heat treatment equipment drops to room temperature, the single crystal silicon sample that has completed the heat treatment is taken out, and the surface of the single crystal silicon sample is cleaned using ultrasonic waves;

[0126] Use atomic force microscopy to detect the surface roughness of single crystal silicon samples, and use X-ray diffractometer to detect the crystal structure of single crystal silicon wafers;

[0127] Use a photoelectric conversion efficiency tester to test the photoelectric conversion efficiency of single crystal silicon wafers;

[0128] Use minority carrier lifetime tester to detect minority carrier lifetime of single crystal silicon wafer;

[0129] Use minority carrier diffusion length tester to test minority carrier diffusion length of single crystal silicon wafers, as well as other performance indicators required by the application;

[0130] Record various test data, compare them with the performance indicators of application requirements, and generate an evaluation report.

[0131] This embodiment also provides a computer device, which is suitable for the method of single crystal silicon heat treatment customized for different application requirements, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of single crystal silicon heat treatment customized for different application requirements as proposed in the above embodiment.

[0132] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0133] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing customized single crystal silicon heat treatment for different application requirements as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0134] In summary, the present invention achieves accurate identification of different application requirements by: determining the performance requirements of single crystal silicon in various fields based on the application fields of single crystal silicon; establishing a relationship model between single crystal silicon heat treatment parameters and material properties based on a deep learning algorithm; achieving intelligent optimization of heat treatment parameters; improving the efficiency and accuracy of heat treatment parameter optimization; gradually adjusting the temperature and other parameters through a multi-step progressive heat treatment method to ensure that the single crystal silicon material can achieve the best heat treatment effect at each stage, thereby improving the consistency and stability of the heat treatment of the single crystal silicon material; and timely adjusting the heat treatment parameters through real-time monitoring of parameters such as temperature, atmosphere and surface changes to ensure that the heat treatment process is always in the best state.

[0135] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a method for heat treatment of single crystal silicon customized for different application requirements are provided.

[0136] The application field of monocrystalline silicon is determined to be the photovoltaic industry, and the specific performance requirements are high photoelectric conversion efficiency, low surface roughness, and long minority carrier lifetime.

[0137] Performance indicators: Photoelectric conversion efficiency: ≥21%, surface roughness: ≤0.5nm, minority carrier lifetime: ≥1ms.

[0138] The single crystal silicon samples are physically processed by cleaning, cutting, polishing, etc.

[0139] The specific performance indicators of monocrystalline silicon in the photovoltaic industry are input into the relationship model between the performance indicators of monocrystalline silicon materials and the heat treatment parameters built based on a deep neural network, and the corresponding single crystal silicon heat treatment parameters are output.

[0140] The single crystal silicon heat treatment equipment is calibrated to ensure the accuracy of temperature control and time control, and the pre-treated single crystal silicon is placed into the equipment to start heat treatment.

[0141] Through real-time monitoring of temperature sensors, atmosphere sensors and optical sensors on the heat treatment equipment, combined with real-time adjustment of heat treatment parameters during the heat treatment process by the central processor.

[0142] After the internal temperature of the heat treatment equipment drops to room temperature, an atomic force microscope is used to detect the surface roughness of the single crystal silicon sample, an X-ray diffractometer is used to detect the crystal structure of the single crystal silicon wafer, a photoelectric conversion efficiency tester is used to detect the photoelectric conversion efficiency of the single crystal silicon wafer, a minority carrier lifetime tester is used to detect the minority carrier lifetime of the single crystal silicon wafer, and a minority carrier diffusion length tester is used to detect the minority carrier diffusion length of the single crystal silicon wafer.

[0143] Record various test data, compare them with the performance indicators of application requirements, and generate an evaluation report.

[0144] The details are shown in Table 1 below:

[0145] Table 1 Comparison of single crystal silicon heat treatment methods

[0146]

[0147] The analysis of the above table data clearly demonstrates the advantages of the method of the present invention over the traditional method in the heat treatment of single crystal silicon.

[0148] The photoelectric conversion efficiency of the traditional method is 20.5%, while the photoelectric conversion efficiency of the method of the present invention reaches 21.2%, which means that the method of the present invention can significantly improve the photoelectric conversion efficiency of single crystal silicon materials, which is an important improvement for the photovoltaic industry.

[0149] By establishing a relationship model between single crystal silicon heat treatment parameters and material properties, it is possible to more accurately predict and control heat treatment parameters, thereby optimizing the performance of single crystal silicon materials.

[0150] Through the embedded controller and PID control algorithm, the heat treatment parameters are adjusted in real time to ensure the stability and accuracy of the heat treatment process.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for heat treatment of single crystal silicon customized for different application requirements, characterized in that: include, Determine the application areas and specific needs of single crystal silicon, and clarify the performance indicators of single crystal silicon materials after heat treatment; According to the performance index of the single crystal silicon, heat treatment is performed on the single crystal silicon; Perform quality inspection and evaluation on single crystal silicon that has completed heat treatment.

2. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 1, characterized in that: The specific steps of determining the application fields and specific needs of single crystal silicon and clarifying the performance indicators of single crystal silicon materials after heat treatment are as follows: Based on the application fields of monocrystalline silicon and its role in these fields, determine the performance requirements of monocrystalline silicon in various fields; Based on performance requirements, clarify the various performance indicators of single crystal silicon materials.

3. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 2, characterized in that: According to the performance index of the single crystal silicon, the single crystal silicon is heat treated, and the specific steps are as follows: Physically treat the single crystal silicon according to performance indicators; Based on deep learning algorithms, a relationship model between single crystal silicon heat treatment parameters and material properties was established; Through a multi-step progressive heat treatment method, heat treatment is performed on the single crystal silicon according to the heat treatment parameters output by the relational model; Install sensors on the heat treatment equipment to monitor the material status during the heat treatment process in real time and automatically adjust the heat treatment parameters.

4. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 3, characterized in that: The physical treatment of the single crystal silicon is carried out according to the performance index, and the specific steps are as follows: Based on the application requirements and performance indicators of monocrystalline silicon, the monocrystalline silicon raw materials are physically treated, and the monocrystalline silicon wafers are placed in an ultrasonic cleaning tank and cleaned with deionized water and acetone in sequence to remove surface pollutants; Use a diamond saw blade to cut the cleaned single crystal silicon wafer into standard sizes; The surface of single crystal silicon wafer is polished using chemical mechanical polishing technology.

5. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 3, characterized in that: The relationship model between single crystal silicon heat treatment parameters and material properties is established based on the deep learning algorithm. The specific steps are as follows: Based on the performance requirements of different application fields, single crystal silicon heat treatment equipment is used to conduct heat treatment experiments on single crystal silicon; After each experiment, the corresponding performance indicators of single crystal silicon materials for different applications are measured using testing instruments; Record the performance index data of each experiment to form a performance index feature vector and the corresponding heat treatment parameters of the single crystal silicon application, and perform cleaning and normalization processing, and divide them into training set and verification set respectively according to proportion; Based on the deep neural network, a relationship model between the performance index of single crystal silicon material and the heat treatment parameters is constructed. By inputting the performance index of single crystal silicon, the model outputs targeted heat treatment parameters of single crystal silicon; Use the data loader to batch read and load the training set and validation set data, and set the model training rounds; For each training round, forward propagation is performed, the performance indicator feature vector is input, the predicted value of the heat treatment parameter is calculated, and the output loss value is calculated; Back propagation, introduce mean square error as the loss function, calculate the gradient of model parameters, and use Adam optimization algorithm to update model parameters; During the model training process, the feature enhancement function is introduced, and the expression is as follows: Among them, f(x) is the input vector after feature enhancement, x is the original input vector, α is the parameter of the control function slope, g(x) is the feature enhancement function, and w i is the weight of the i-th feature, β i is the frequency parameter of the i-th feature, x i is the i-th eigenvalue, n is the number of features, and i is the index variable of the number of features; After each training round, the model performance is evaluated on the validation set, and the performance indicators and loss values ​​of the training set and validation set output in each round are compared; If the performance of the current model on the validation set is better than that of the previous model, the current model is saved; otherwise, the previous model continues to be used.

6. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 4, characterized in that: The method of heat treatment of the single crystal silicon is carried out by a multi-step progressive heat treatment method according to the heat treatment parameters output by the relationship model. The specific steps are as follows: Calibrate single crystal silicon heat treatment equipment; The required single crystal silicon performance indicators are input into the trained model, and the model outputs a set of heat treatment parameters. The initial parameters of the heat treatment equipment are set, and the single crystal silicon pretreated according to the application requirements is placed into the heat treatment equipment to start the heat treatment; The heat treatment process is divided into three stages: the preheating stage, from room temperature to half of the maximum temperature, slow heating, the main treatment stage, heating to the maximum temperature, rapid heating, and the cooling stage, the temperature in the heat treatment equipment is reduced to room temperature.

7. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 5, characterized in that: The sensors are installed on the heat treatment equipment to monitor the material status in real time during the heat treatment process and automatically adjust the heat treatment parameters. The specific steps are as follows: In the multi-step progressive heat treatment process, an embedded controller is used as a central processor to receive the data monitored by the sensor in real time; Integrate the PID control algorithm into the central controller to adjust the heating parameters of the heat treatment equipment in real time according to the multi-step progressive heating scheme and data feedback; According to actual needs, set the initial PID coefficient and set value, read the parameters inside the heat treatment equipment in real time through the sensor interface, and calculate the error value between the set value and the actual value; Based on the error value, the proportional term, integral term and differential term are calculated and summed up as the total PID output; The total PID output is sent to each parameter control unit of the heat treatment equipment in the form of a voltage signal to automatically adjust the heat treatment parameters; At the same time, users are allowed to manually adjust PID parameters through the user interface, observe the control effect and optimize it; Run the PID control algorithm on actual heat treatment equipment, monitor the response and stability of the system, and verify the effectiveness and reliability of the PID control algorithm; According to the test results, gradually adjust the PID parameters until the system achieves the best control effect. During the optimization process, the system performance before and after each adjustment should be recorded for comparison and analysis; During the entire heat treatment process, multiple high-temperature resistant temperature sensors are installed inside the heat treatment equipment to monitor the temperature changes inside the equipment in real time, and the central controller automatically adjusts the heating rate according to the temperature curve; The atmosphere sensor monitors the atmosphere type F, concentration and pressure changes in the equipment in real time, and the central controller automatically adjusts the gas replenishment and exhaust; An optical microscope is installed during the cooling stage to monitor the surface changes of the single crystal silicon heated sample in real time, and the central controller adjusts the cooling rate.

8. The method for heat treating single crystal silicon customized for different application requirements as claimed in claim 7, characterized in that: The specific steps of quality inspection and evaluation of the heat-treated single crystal silicon are as follows: After the temperature inside the heat treatment equipment drops to room temperature, the single crystal silicon sample that has completed the heat treatment is taken out, and the surface of the single crystal silicon sample is cleaned using ultrasonic waves; Use atomic force microscopy to detect the surface roughness of single crystal silicon samples, and use X-ray diffractometer to detect the crystal structure of single crystal silicon wafers; Use a photoelectric conversion efficiency tester to test the photoelectric conversion efficiency of single crystal silicon wafers; Use minority carrier lifetime tester to detect minority carrier lifetime of single crystal silicon wafer; Use minority carrier diffusion length tester to test minority carrier diffusion length of single crystal silicon wafers, as well as other performance indicators required by the application; Record various test data, compare them with the performance indicators of application requirements, and generate an evaluation report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for heat treatment of single crystal silicon customized for different application requirements as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for heat treatment of single crystal silicon customized for different application requirements as claimed in any one of claims 1 to 8 are implemented.

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