Achieving high-accuracy and noise-robust process window analysis through stepwise regression

Lithography, one of the key semiconductor manufacturing processes, requires accurate and robust process window analysis to ensure high-yield chip production. Traditional methods often utilize fixed polynomial functions to fit focus-exposure matrix data, which can fail to account for variations across mask patterns and noise in critical dimension measurements, leading to inaccurate evaluation of the process window and best process condition. This paper introduces a stepwise regression approach that iteratively selects statistically significant terms based on p-values. Evaluation through adjusted R2 and window overlapping before and after noise introduction demonstrates the method’s effectiveness in enhancing both accuracy and noise robustness in process window analysis.