Improved yield estimation with efficient decision power for multi-line processes

Abstract

Portable devices with multiple functions are commonly used in modern life, and the dies in these products have become much thinner and slimmer over time. Due to the rapid advancements in manufacturing technology in the semi-conductor industry, the process yield requirements grow increasingly strict. In advanced packaging manufacturing, the process is usually with multiple lines and often requires a very low fraction of defective. To assess the manufacturing yield precisely, the process capability index is widely used. $C_{pk}^M$ is a generalization yield index designated for measuring the yield of multi-line processes. However, the typical existing method for obtaining the lower confidence bound of $C_{pk}^M$ is conservative and it may mislead managers into making incorrect decisions. In this study, the nonparametric and parametric standard bootstrap methods have been implemented to establish a reliable and improved yield assessment. Three methods are investigated and the power comparison is provided. Then, two effective transformation methods to handle non-normal processes are presented and one example with simulation data is given for algorithm demonstration. Finally, an application of yield assessment for underfill processes with two manufacturing lines is presented. The simulation results demonstrate that based on the proposed method, we can reliably evaluate the true manufacturing yield and make a more powerful decision.

Keywords:

• bootstrap methods
• Multi-line processes
• power
• yield assessment

Additional information

Notes on contributors

Chia-Huang Wu

Chia Huang Wu received the PhD degree in Industrial Engineering and Management from National Yang Ming Chiao Tung University, Hsinchu, Taiwan. He is currently an Assistant Professor at National Yang Ming Chiao Tung University. His research interests include process capability analysis, applied statistics, queueing system, and optimization theory. He has published more than 40 papers on various SCI journals such as Quality and Reliability Engineering International, European Journal of Industrial Engineering, Applied Mathematical Modeling, Computers and Operations Research, Computers and Industrial Engineering, etc.

Ya-Chen Hsu

Ya-Chen Hsu received the PhD degree in Industrial Engineering and Management from National Yang Ming Chiao Tung University, Hsinchu, Taiwan. She is currently an Associate Professor in the Department of Food Science of Tunghai University. Her research interests include statistical quality control and process capability analysis. Her publications appeared in various SCI journals such as European Journal of Operational Research, International Journal of Productions Research, Journal of the Operational Research Society, International Journal of the Physical Sciences, Quality Technology & Quantitative Management, etc.

Wen-Lea Pearn

Wen Lea Pearn received the PhD degree in Operations Research from the University of Maryland, College Park, MD, USA. He is a Professor of Operations Research and Quality Assurance with National Yang Ming Chiao Tung University, Hsinchu, Taiwan. He was with Bell Laboratories, Murray Hill, NJ, USA, as a Quality Research Scientist before joining NCTU. His current research interests include process capability, network optimization, and production management. His publications could be found in the IEEE Transactions on Components, Packaging, and Manufacturing Technology, Journal of the Royal Statistical Society, Series C, Journal of Quality Technology, European Journal of Operational Research, Journal of the Operational Research Society, Operations Research Letters, Omega, Networks and International Journal of Productions Research.