Adaptive Reinforcement Learning-Driven Digital Twin for Multiobjective and Multimachine Process Optimization

Achieving efficient and scalable multi-objective process optimization in the semiconductor industry poses significant challenges due to the complexity of multi-machine and multi-product scenarios, high-dimensional parameter spaces, and limited availability of real manufacturing data—especially in cutting-edge applications involving wide-bandgap (WBG) materials such as SiC and GaN. To address these issues, we introduce ADEPT (Adaptive Deep fEature-based Policy opTimization), an integrated Digital Twin (DT) framework that fuses multi-stage learning, Reinforcement Learning (RL), Gaussian Processes (GP), and dynamic deep feature extraction. Validated on real-world epitaxial (Epi) process data, ADEPT demonstrates robust performance under strict data constraints by leveraging GP-based uncertainty to guide RL exploration. Furthermore, by incorporating advanced meta-learning and transfer-learning (MLTL) strategies, ADEPT efficiently adapts to newly introduced machines and WBG-oriented product lines, expediting deployment and minimizing data requirements. We also provide rigorous mathematical convergence analysis, proving a high-probability guarantee of near-optimal solutions under appropriate assumptions, thus paving the way for future advancements in smart manufacturing.