Data-Driven Predictive Control Strategy for Rapid Thermal Processing Systems

Abstract

Rapid Thermal Processing (RTP) is a key technology for semiconductor manufacturing. However, the strong coupling, nonlinearities, and multiple disturbances inherent in RTP render accurate physical modeling extremely challenging. This paper studies the application of subspace predictive control (SPC) to a wafer RTP system driven by standard tungsten halogen lamps. This control algorithm does not require an exact RTP physical model and employs a data-driven method for temperature control. The subspace identification algorithm is employed to construct a subspace-based predictor for the RTP system. A pseudo-random binary series (PRBS) is designed as the excitation signal for the RTP system to obtain the input and output data. The designed PRBS excitation signal produces a response that effectively captures the system dynamics. Finally, temperature control of RTP system is achieved through SPC. Experimental results show that the achieved wafer temperature uniformity and control accuracy satisfy the required RTP performance metrics. The SPC algorithm provides a feasible alternative to model-based strategies in complex industrial environments.