The growth process of Czochralski (Cz) silicon single crystal is a dynamic time-varying system with nonlinearity, strong coupling, large hysteresis, and uncertain model. Traditional model-based control methods are difficult to achieve satisfactory crystal growth control effects, and it is difficult to ensure that the crystal quality meets the actual process requirements. Therefore, from the perspective of data-driven modeling and control, this paper proposes a model predictive control method for the crystal growth process with a V/G soft-sensing model for measuring crystal quality. First, because the V/G value to measure crystal quality is difficult to obtain directly, a hybrid variable weighted stacked autoencoder random forest (HVW-SAE-RF) soft-sensing model based on data-driven V/G value is established. Here, the HVW-SAE is used to extract the deep quality-related features of the measurable process data, and the RF is used for the regression prediction of the SAE output layer; Second, using the dual closed-loop control strategy, the inner loop is based on the HVW-SAE-RF soft-sensing model of the V/G value, and the predictive PI control method is used to control the V/G value closely related to the crystal quality within a reasonable range, and the outer loop based on real-time estimation of V/G values to achieve nonlinear model predictive control (NMPC) of crystal diameter; finally, the effectiveness of the proposed method is verified based on the industrial production process data of silicon single crystal.

中文翻译：

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V/G值软测量模型对Cz硅单晶生长过程的数据驱动模型预测控制

直拉（Cz）硅单晶的生长过程是一个动态时变系统，具有非线性、强耦合、大滞后和不确定模型。传统的基于模型的控制方法难以达到令人满意的晶体生长控制效果，难以保证晶体质量满足实际工艺要求。因此，本文从数据驱动建模与控制的角度，提出了一种基于V/G软测量晶体质量测量模型的晶体生长过程模型预测控制方法。首先，由于测量晶体质量的 V/G 值难以直接获得，因此建立了基于数据驱动的 V/G 值的混合可变加权堆叠自编码器随机森林（HVW-SAE-RF）软测量模型。这里，HVW-SAE用于提取可测量过程数据的深层质量相关特征，RF用于SAE输出层的回归预测；二、采用双闭环控制策略，内环基于V/G值的HVW-SAE-RF软测量模型，采用预测PI控制方法对V/G值进行严密控制与晶体质量在合理范围内相关，外环基于V/G值的实时估计，实现晶体直径的非线性模型预测控制（NMPC）；最后，基于单晶硅的工业生产过程数据验证了所提方法的有效性。采用双闭环控制策略，内环基于V/G值的HVW-SAE-RF软传感模型，采用预测PI控制方法控制与V/G值密切相关的V/G值晶体质量在合理范围内，外环基于V/G值的实时估计，实现晶体直径的非线性模型预测控制（NMPC）；最后，基于单晶硅的工业生产过程数据验证了所提方法的有效性。采用双闭环控制策略，内环基于V/G值的HVW-SAE-RF软传感模型，采用预测PI控制方法控制与V/G值密切相关的V/G值晶体质量在合理范围内，外环基于V/G值的实时估计，实现晶体直径的非线性模型预测控制（NMPC）；最后，基于单晶硅的工业生产过程数据验证了所提方法的有效性。以及基于V/G值实时估计的外环实现晶体直径的非线性模型预测控制（NMPC）；最后，基于单晶硅的工业生产过程数据验证了所提方法的有效性。以及基于V/G值实时估计的外环实现晶体直径的非线性模型预测控制（NMPC）；最后，基于单晶硅的工业生产过程数据验证了所提方法的有效性。