CN-121091933-B - Intelligent temperature control system and method for crystal growth furnace

CN121091933BCN 121091933 BCN121091933 BCN 121091933BCN-121091933-B

Abstract

The application relates to the technical field of temperature control of crystal growth equipment, in particular to an intelligent temperature control system and method of a crystal growth furnace. The system comprises a data acquisition module, a calculation module, a control module and an optimization module, wherein the data acquisition module acquires physical parameters and crystal quality parameters in a furnace in real time, the calculation module dynamically determines a temperature control precision grade and a temperature deviation threshold value by calculating the thermal resistance value of non-uniform grid nodes and outputs a control instruction, the control module responds to the control instruction to carry out temperature adjustment, and the optimization module establishes a correlation model of defect density and temperature fluctuation and corrects the thermal resistance value of the grid nodes. The application realizes the accurate temperature control of the whole period of crystal growth, improves the temperature regulation precision and the control strategy adaptability, and improves the crystal quality and the yield.

Inventors

ZHEN XIHE
Peng Minglin
ZHOU FANG
ZHOU SONGSONG
HAN SHICHANG
ZHOU CHAO

Assignees

河南微米光学科技有限公司

Dates

Publication Date: 20260508
Application Date: 20250901

Claims (8)

1. An intelligent temperature control system of a crystal growth furnace comprises a data acquisition module, a calculation module, a control module and an optimization module; the data acquisition module is used for acquiring physical parameters and crystal quality parameters in the crystal growth furnace in real time, wherein the crystal quality parameters comprise crystal growth parameters and crystal defect detection data, the physical parameters comprise temperature data, thermal image data, motion parameters and gas parameters, the motion parameters comprise crucible rotation speed and lifting speed, and the gas parameters comprise air inlet flow and furnace pressure; the calculation module comprises a thermal resistance network unit and a control precision unit; The thermal resistance network unit divides the furnace space into non-uniform grid nodes and calculates thermal resistance values of the non-uniform grid nodes based on the physical parameters; the control precision unit dynamically determines the temperature control precision grade and the temperature deviation threshold value of the crystal growth interface based on the temperature gradient distribution and the crystal growth parameters, and outputs a control instruction when the absolute value of the difference value between the actual temperature and the target temperature of the crystal growth interface is larger than the temperature deviation threshold value; the control module is used for responding to the control instruction, driving the executing mechanism to carry out temperature adjustment according to an adjustment mechanism corresponding to the temperature control precision level; The optimization module is used for establishing a correlation model of defect density and temperature fluctuation based on the crystal defect detection data and the historical temperature fluctuation data, and correcting the thermal resistance value of the non-uniform grid node based on the crystal defect data after the growth of each batch of crystals is finished; The specific mode for outputting the temperature gradient distribution of the crystal growth interface based on the thermal resistance values among the non-uniform grid nodes and the boundary conditions is as follows: Establishing an unsteady heat conduction equation based on the thermal resistance value and the energy conservation law, and solving the heat conduction equation by combining boundary conditions, wherein the boundary conditions comprise a temperature boundary condition, a heat flow boundary condition, a convection boundary condition and a radiation boundary condition; discretizing a heat conduction equation, converting the discretized heat conduction equation into a linear equation set, and solving the equation set through a numerical algorithm to obtain temperature values of all non-uniform grid nodes, thereby obtaining the approximate distribution of a three-dimensional temperature field; spatial derivation is carried out on the three-dimensional temperature field at the position of the crystal growth interface, so that the temperature gradient distribution of the crystal growth interface is obtained; the step of correcting the thermal resistance value of the non-uniform grid node based on the crystal defect data is specifically as follows: Identifying a defect dense region on a crystal according to offline defect detection data, and positioning a non-uniform grid node set corresponding to the defect dense region through a coordinate mapping relation; Calling a correlation model of temperature fluctuation and defect density, judging whether the defect dense area is obviously and positively correlated with the temperature fluctuation, if the correlation model shows that the defect dense area is obviously and positively correlated with the defect density, marking the corresponding grid node as a preferential correction node, otherwise, recording the corresponding node as a node to be observed; Calculating the ratio of the defect density of the crystal area corresponding to the priority correction node to the average defect density of the whole crystal to obtain a defect influence coefficient; calculating the difference value between the temperature of the priority correction node and the actually measured temperature output by the thermal resistance network unit to obtain a temperature prediction error, and taking the correlation coefficient of the crystal area corresponding to the priority correction node as the correlation intensity coefficient of the temperature fluctuation and the defect density in the crystal area corresponding to the priority correction node; And according to the correction amount of the thermal resistance value of the priority correction node, updating the thermal resistance value of the priority correction node.
2. The intelligent temperature control system of a crystal growing furnace according to claim 1, wherein the thermal resistance network unit is configured with a grid division strategy for dividing a furnace space into non-uniform grid nodes, and the grid division strategy specifically comprises: dividing a basic grid covering the whole hearth based on a furnace body three-dimensional structure, and setting a minimum grid size; The method comprises the steps of establishing a mapping relation between a crystal growth stage and grid density, wherein the highest grid density is adopted in a seeding stage, and the node density of a seed crystal tip, a melt surface, a solid-liquid interface area and a neighborhood thereof is increased; And judging the current growth stage of the crystal according to the crystal growth parameters, and adjusting the grid density according to the mapping relation between the crystal growth stage and the grid density.
3. The intelligent temperature control system of a crystal growth furnace according to claim 2, wherein the control precision unit is configured with a control precision grade classification strategy for determining a temperature control precision grade of a crystal growth interface and a temperature deviation threshold under the temperature control precision grade, the control precision grade classification strategy specifically comprising: Calculating a total risk value based on the thermal stress factor, the growth stability factor, and the interfacial curvature factor; determining the temperature control precision grade of a crystal growth interface according to the total risk value, and setting a basic temperature deviation threshold value for each temperature control precision grade, wherein the temperature control precision grade comprises an ultra-precision mode, a high-precision mode, a standard mode and a loose mode; dynamically correcting the temperature deviation threshold value by an adjustment factor on the basis of the basic temperature deviation threshold value; the adjustment factors comprise a crystal length factor, a crystal gradient factor and a crystal defect factor, and the temperature deviation threshold is obtained by multiplying the basic temperature deviation threshold by the adjustment factors.
4. The intelligent temperature control system of a crystal growing furnace according to claim 3, wherein the control module is configured with an actuator for performing temperature adjustment according to an adjustment mechanism corresponding to the temperature control precision level, the actuator comprises a multi-zone heater, a gas flow valve and a crucible lifting mechanism, and the adjustment mechanism specifically comprises: After receiving an instruction of an ultra-precise mode, the control module starts a part of multi-zone heater to perform preset minimum power step progressive adjustment, and when the accumulated power adjustment quantity of the heater reaches a preset maximum range, starts a gas flow valve to perform flow adjustment in a preset minimum flow step, and adjusts a stable radial gradient through a preset minimum amplitude of the crucible rotating speed and the pulling speed; When the control module receives an instruction of a high-precision mode, the multi-zone heater of the part is started to perform step-type power step adjustment, if the absolute value of the difference value between the actual temperature and the target temperature value is not smaller than the temperature deviation threshold value range after the preset step number is continuously adjusted, the gas flow valve is started to perform cooperative adjustment according to the preset basic flow step length, and meanwhile the crucible lifting mechanism adjusts the rotating speed once per fixed time to control temperature gradient fluctuation.
5. The intelligent temperature control system of a crystal growing furnace of claim 4 wherein the adjustment mechanism further comprises: when the control module receives a standard mode instruction, driving all the multi-zone heaters to synchronously adjust power according to a preset proportion, simultaneously enabling the gas flow valves to cooperatively adjust in a mode that the gas flow valve is larger than a preset basic flow step length and smaller than a preset maximum flow step length, and starting a crystal lifting speed of the crucible lifting mechanism to adjust when the crystal diameter fluctuation is larger than a preset fluctuation first threshold value; if the control module receives a loose mode instruction, starting all heaters to perform preset maximum power step adjustment, setting a gas flow valve in a preset flow interval, and starting a crucible lifting mechanism to perform preset maximum amplitude adjustment when the fluctuation of the crystal diameter is larger than a preset fluctuation second threshold value.
6. The intelligent temperature control system of a crystal growth furnace according to claim 5, wherein the optimization module is configured with a correlation model construction strategy for constructing a correlation model of defect density and temperature fluctuation, and the correlation model construction strategy specifically comprises: time alignment is carried out on the crystal defect detection data and the temperature fluctuation data of each batch of the same growth stage by taking the time progress of crystal growth as a basic axis; Dividing a crystal into different space regions, establishing a coordinate mapping relation between each space region of the crystal and a three-dimensional temperature field non-uniform grid, recording a correlation result of data corresponding to the space region, classifying the correlation result according to the space region of the crystal and a growth stage to form data subsets, and calculating a defect characteristic value and a temperature fluctuation characteristic value of each subset, wherein the defect characteristic value comprises average defect density and maximum defect density of the corresponding space region; Drawing a scatter diagram by taking the absolute value of the difference between the actual temperature and the target temperature as a horizontal axis and taking the average defect density as a vertical axis, and calculating the correlation coefficient of the temperature fluctuation and the defect density by analyzing the data distribution of the scatter diagram so as to establish the mapping relation between the defect characteristic value and the temperature fluctuation characteristic value; and generating a temperature fluctuation and defect density correlation table for each region according to the mapping relation to form a temperature fluctuation and defect density correlation model.
7. The intelligent temperature control system of a crystal growing furnace of claim 6, wherein, The data acquisition module is configured with a thermocouple and acquires the temperature data through the thermocouple; the data acquisition module is also provided with an infrared thermal imager and acquires the thermal image data through the infrared thermal imager; the crystal growth parameters include crystal diameter and crystal growth rate; The crystal diameter is obtained by scanning the outer diameter of the crystal by a laser beam, outputting a crystal diameter value in real time, calculating the crystal growth speed by pulling displacement and time difference, and detecting the crystal defect by on-line detection data and off-line detection data.
8. An intelligent temperature control method of a crystal growth furnace, which is realized based on an intelligent temperature control system of the crystal growth furnace according to any one of claims 1 to 7, and is characterized by comprising the following steps: collecting physical parameters and crystal quality parameters in a crystal growth furnace in real time, wherein the crystal quality parameters comprise crystal growth parameters and crystal defect detection data; dividing the hearth space of the crystal growth furnace into non-uniform grid nodes, and calculating the thermal resistance values of the non-uniform grid nodes based on the physical parameters; dynamically determining a temperature control precision grade and a temperature deviation threshold value of a crystal growth interface based on the temperature gradient distribution and the crystal growth parameters, and performing temperature adjustment according to an adjustment mechanism corresponding to the temperature control precision grade when the absolute value of the difference between the actual temperature of the crystal growth interface and the target temperature is larger than the temperature deviation threshold value; And establishing a correlation model of defect density and temperature fluctuation based on the crystal defect detection data and the historical temperature fluctuation data, and correcting the thermal resistance value of the non-uniform grid node based on the crystal defect data after the growth of each batch of crystals is finished.

Description

Intelligent temperature control system and method for crystal growth furnace Technical Field The application relates to the technical field of temperature control of crystal growth equipment, in particular to an intelligent temperature control system and method of a crystal growth furnace. Background The crystal material is used as a core base material in the fields of modern electronic information technology, photoelectric communication, aerospace and the like, and the quality of the crystal material is a key factor for determining the performance and reliability of downstream devices. In the crystal growth process, temperature control is a decisive link influencing the final quality of the crystal, and accurate thermal field regulation is important to obtain high-quality crystals. The conventional temperature control technical means commonly used in the current crystal growth field mainly comprises a PID control method for collecting temperature data through a few thermocouples and carrying out feedback adjustment by adopting a proportional-integral-derivative algorithm, and an empirical test method for presetting fixed temperature parameters and adjustment step length at different stages of crystal growth, switching the sectional fixed parameters of a control mode according to time nodes and manually adjusting parameters such as heating power, gas flow and the like according to past experience by an operator. However, the conventional technical means generally have the following problems that on one hand, the conventional method mainly relies on limited local point measurement, complex three-dimensional temperature field distribution in a furnace is difficult to comprehensively and accurately reflect, particularly dynamic temperature change of a key area of a crystal growth interface cannot be effectively captured, so that a regulation decision lacks sufficient global information basis, and on the other hand, the prior art is difficult to dynamically adapt to the differential requirements of the crystal on temperature precision, gradient distribution and change rate in different growth stages no matter that a single PID parameter is adopted to penetrate the whole growth process or only simple preset parameter stage switching is carried out, so that the quality of the crystal is influenced. For example, china patent with the publication number CN119292384B discloses a method and a system for optimizing heat management in a crystal growth process. The method comprises the steps of constructing a plurality of heating subareas and a plurality of monitoring points according to crystal processing equipment parameters, generating a crystal expected growth curve according to the crystal parameters to be processed, constructing a heating period, a growing period and a cooling period according to the crystal expected growth curve, sequentially setting control strategies of the heating period, the growing period and the cooling period, constructing a plurality of heating subareas in the crystal processing equipment, controlling the temperature of the subareas in a subarea mode in the crystal growth process, ensuring that a solid-liquid interface of the crystal is in a stable temperature field, ensuring the growth efficiency and quality of the crystal, and simultaneously carrying out targeted adjustment on the rest heating subareas, so that the overall heat loss is reduced. And the control strategies in each period are respectively set according to the expected growth curve of the crystal, so that the temperature control of the inside of the crystal processing equipment is improved, and the overall heat loss is reduced. The patent application with the publication number of CN119336101A discloses a crystal growth process optimization control system and method based on machine learning, comprising a difference detection module, a model building module, a morphology prediction module and a temperature setting module, wherein the difference detection module is used for acquiring the crystal outline of a current crystal growth image to obtain the crystal growth difference degree, the model building module is used for acquiring historical crystal growth data and building a growth morphology parameter prediction model according to the historical crystal growth data, the morphology prediction module is used for inputting the current crystal growth difference degree into the growth morphology parameter prediction model to obtain the predicted growth morphology parameter of the current crystal, and the temperature setting module is used for determining the growth required temperature according to the predicted growth morphology parameter of the current crystal and correcting the growth required temperature according to the historical crystal growth data. And predicting the crystal growth morphology by establishing a growth morphology parameter prediction model, so that the optimal temperature required by crystal growth is obt