Iterative Learning-Based Predictive Control Method for Electronic Grade Silicon Single Crystal Batch Process

The growth process of silicon single crystal (SSC) is a typical batch production process, which has the characteristics of batch size, variety, complex process, and intensive technology. In order to accurately control the diameter and thermal field temperature of the Czochralski (Cz) SSC batch production process to ensure that the actual production requirements of electronic-grade SSC are met, this paper proposes an iterative learning-based batch process predictive control strategy Model Predictive Control - Iterative Learning Control - Extended State Observer (MPC-ILC-ESO). The control strategy consists of two parts. The time axis is a dual Model Predictive Control (MPC) controller, which is mainly used to deal with disturbance suppression and constraints in the production process of a single batch of SSC. The iterative axis is the Extended State Observer (ESO) based Iterative Learning Control (ILC) control algorithm to deal with uncertainty and disturbance estimation and compensation in the multi-batch production process. The control effects of the time axis and the batch axis are superimposed as the final controlled object input to ensure the crystal diameter and stable thermal field temperature. Finally, the effectiveness of the proposed control strategy is verified based on the data analysis of the semiconductor industry production process.