Decomposition and analysis of process variability using constrained principal component analysis

Process-induced variability has become a predominant limiter of performance and yield of IC products especially in a deep submicron technology. However, it is difficult to accurately model systematic process variability due to the complicated and interrelated nature of physical mechanisms of variation. In this paper, a simple and practical method is presented to decompose process variability using statistics of the measurements from manufacturing inline test structures without assuming any underlying model for process variation. The decomposition method utilizes a variant of principal component analysis and is able to reveal systematic variation signatures existing on a die-to-die and wafer-to-wafer scale individually. Experimental results show that the most dominant die-to-die variation and wafer-to-wafer variation represent 31% and 25% of the total variance of a large set of manufacturing inline parameters in 65-nm SOI CMOS technology. The process variation in RF circuit performance is also analyzed and shown to contain 66% of process variation obtained with manufacturing inline parameters.