A Data-Driven Statistical Approach to Analyzing Process Variation in 65nm SOI Technology

Choongyeun Cho1,  Daeik Kim1,  Jonghae Kim1,  Jean-Olivier Plouchart1,  Daihyun Lim2,  Sangyeun Cho3,  Robert Trzcinski1
1IBM, 2MIT, 3University of Pittsburgh


This paper presents a simple yet effective method to analyze process variations using statistics on the manufacturing in-line data without assuming any explicit underlying model for process variations. Our method is based on a variant of principal component analysis (PCA), and is able to reveal systematic variation patterns existing on a die-to-die and wafer-to-wafer level individually. The separation of die variation from wafer variation can enhance the understanding of a nature of the process uncertainty. Our case study based on the proposed decomposition method shows that the dominating die-to-die variation and wafer-to-wafer variation represent 31% and 25% of the total variance of a large set of in-line parameters in 65nm SOI CMOS technology.