基于神经网络和遗传算法的铸锭晶体硅质量控制及工艺优化  

Process Control and Optimization of Ingot Crystalline Silicon Growth Using Neural Network and Genetic Algorithm

作　　者：郝佩瑶 朱金伟 廖继龙 郑丽丽[1] 张辉[3] HAO Peiyao;ZHU Jinwei;LIAO Jilong;ZHENG Lili;ZHANG Hui(School of Aerospace Engineering, Tsinghua University, Beijing 100084, China;Jiangsu GCL Silicon Material Technology Development Co., Ltd., Xuzhou 221001, China;Department of Engineering Physics, Tsinghua University, Beijing 100084, China)

机构地区：[1]清华大学航天航空学院,北京100084 [2]江苏协鑫硅材料科技发展有限公司,徐州221001 [3]清华大学工程物理系,北京100084

出　　处：《人工晶体学报》2022年第3期385-397,共13页Journal of Synthetic Crystals

基　　金：国家重点研发计划(2020YFB1506501)。

摘　　要：铸锭晶体硅是太阳能级晶硅材料的重要来源之一,为了进一步降低硅片成本,需要在保证晶体质量的同时发展大尺寸铸锭晶硅。影响铸造晶体硅质量的热场控制核心参数包括晶体生长速度与生长界面温度梯度之比V/G、壁面热流q、生长界面高度差Δh和硅熔体内部温差ΔT等。针对铸锭晶体硅生长过程中的质量控制问题,本研究基于人工神经网络(ANN)模型对晶体生长过程建立了工艺控制优化方法,利用实验测量数据和数值仿真模拟结果构建铸锭晶体硅生长过程的工艺控制数据集,以底部隔热笼开口和侧、顶加热器功率比作为主要工艺控制参数,V/G、|q|、|Δh|和ΔT为优化目标,建立用于研究晶体生长工艺控制参数和热场参数之间映射关系的神经网络模型。使用训练完成的模型分析底部隔热笼开口及侧、顶加热器功率比对晶体生长过程热场的影响规律,并采用遗传算法(GA)对铸锭晶体硅生长过程的工艺控制参数以提高晶体质量为目标进行优化,最后结合实际生产中的检测图像讨论了V/G对晶体质量的影响。研究表明晶体生长中期的V/G沿横向变化较平缓,对应缺陷较少且分布均匀,因此增大V/G在横向上的均匀度也是提高晶体质量的一个重要因素。Ingot crystalline silicon is one of the important sources for solar-grade crystalline silicon materials.For reducing the cost of silicon wafers,it is necessary to develop large-size ingot crystalline silicon while ensuring the crystal quality.The core parameters that affect the quality of cast crystalline silicon include the ratio of crystal growth rate to temperature gradient at the solidification interface V/G,wall heat flow q,height difference of solidification interfaceΔh,and temperature difference of silicon meltΔT.Aiming at the quality control during the growth of ingot crystalline silicon,this study established a process control optimization method based on the artificial neural network(ANN)model for the crystal growth process.A process control data set for the growth process of ingot crystalline silicon was constructed based on the experimental measurement data and numerical simulation results.The opening of the bottom insulation cage and the power ratio of the side and top heaters were used as the main process control parameters,and V/G,q,Δh,ΔT are the optimization goals.A neural network model for mapping the relationship between process control parameters and thermal field characteristics was established.The influence of the bottom heat insulation cage opening and the power ratio of the side to top heaters on the thermal field during the crystal growth process were analyzed using the trained neural network model.The process control parameters for the ingot crystalline silicon growth process are optimized using genetic algorithm(GA),improving the quality of grown crystal and reducing energy consumption.Finally based on the experimental test images,the influences of V/G on the crystal quality were discussed.Research shows that V/G in the middle stage of crystal growth changes smoothly along the horizontal direction,and the corresponding defects are less and evenly distributed.Therefore,increasing the uniformity of V/G in the horizontal direction is also an important factor to improve the quality of cr

关 键 词：铸锭晶体硅 人工神经网络 遗传算法 V/G 定向凝固 晶体质量 

分 类 号：O469[理学—凝聚态物理] TQ127.2[理学—电子物理学]