Recursive Bayesian state estimation method for run‐to‐run control in high‐mixed semiconductor manufacturing process. Issue 3 (26th November 2018)

• Record Type: Journal Article
• Authors: Tan, Fei; Pan, Tianhong; Bian, Jun; Wang, Haiyan; Wang, Weiran
• Is Part Of: Asian journal of control. Volume 22:Issue 3(2020)
• Page Start: 1177
• Page End: 1187
• Publication Date: 2018-11-26
• Subjects: Bayesian estimation theory -- high‐mix semiconductor manufacturing process -- run‐to‐run control -- state estimation; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1977
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: One of the challenges in semiconductor manufacturing processes is the state estimation of a high‐mix production system. The traditional algorithm consists of constructing a context matrix based on the product fabricating thread. The state of the context matrix is estimated using the Moore‐Penrose pseudo‐inverse method. Although the method works well, the context matrix is often singular. Taking an integrated moving average disturbance into consideration, a novel state estimation method is proposed in a high‐mix manufacturing scenario. Furthermore, the recursive Bayesian estimation is presented to obtain the estimations of states combined with a moving window and an analysis of variance model. As a result, the calculation of the inverse of the context matrix is avoided and the unobservability problem is addressed. Both simulated and industrial cases are presented to demonstrate the effectiveness of the proposed algorithm.