Monitoring Batch Processes with Multiple On–Off Steps in Semiconductor Manufacturing

Abstract

A modern semiconductor manufacturing line is made of hundreds of sequential batch-processing stages. Each of these stages consists of many steps carried out by expensive tools, which are monitored by numerous sensors capable of sampling at intervals of seconds. The sensor readings of each run constitute profiles, which can include extremely drastic changes. The heterogeneous variations at different profile points are mainly due to on—off recipe actions at specific points. In addition, the analysis of these profiles is further complicated by long-term trends due to tool aging and short-term effects specific to the first wafer in a lot cycle. Statistical process control methods that fail to take these effects into consideration will lead to frequent false alarms. A systematic method is proposed to address these challenges. First, a reference profile is determined for each sensor variable that describes the on—off actions. Next, level shifts of these profiles in each step are established to capture and remove intrinsic variations due to long-term aging trends and the short-term first-wafer effects. The residuals are used to formulate a health index, and this index can be used to monitor the health of the equipment and detect faulty wafers efficiently.

• Batch-Processing Stages
• Health Index
• Heterogeneous Variation
• Multivariate Statistical Process Control
• Sensor Data
• Signal Map

Additional information

Notes on contributors

Shui-Pin Lee

Dr. Lee is an Assistant Professor in the Department of Industrial Engineering and Management. His email address is [email protected].

An-Kuo Chao

Mr. Chao is a Graduated Student in the Institute of Statistics. His email address is [email protected].

Fugee Tsung

Dr. Tsung is a Professor in and Head of the Department of Industrial Engineering and Logistics Management. He is a Fellow of ASQ. His email address is [email protected].

David Shan Hill Wong

Dr. Wong is a Professor in the Department of Chemical Engineering. His email address is [email protected].

Sheng-Tsiang Tseng

Dr. Tseng is a Professor in the Institute of Statistics. He is a Member of ASQ. His email address is [email protected].

Shi-Shang Jang

Dr. Jang is a Professor of Chemical Engineering. His email address is [email protected].