Performance-driven semiconductor silicon crystal quality control

Aiming at the online monitoring of key variables during the growth process of Czochralski (Cz) silicon single crystal (SSC), this paper is aimed to propose a performance-driven hierarchical control strategy based on soft-sensing model. To this end, corresponding sub-models based on the mechanism model and industrial data are first established. Then, the two linear forms of them are combined to arrive at the mechanism- and data-driven hybrid variable weighted stacked autoencoder random forest (M-HVW-SAE-RF) soft sensing model. Among them, the stacked autoencoder (SAE) network is utilized to extract the deep features of the data, and the random forest (RF) is employed to realize the regression prediction of the target variable. Secondly, for the problem of model uncertainty, based on the identification for control (I4C) theory, a performance-driven hierarchical control strategy is established on the basis of the model predictive control-adaptive disturbance rejection control (MPC-ADRC). The main goal is to achieve the best output performance of the actual control system and to implement online monitoring of crystal size and V/G. Finally, based on the analysis of the production process data of the semiconductor industry, the effectiveness of the proposed control strategy is verified.