Principal component analysis of stochasticity in resist films

Hiroshi Fukuda

doi:10.1117/12.2610998

13 June 2022 Principal component analysis of stochasticity in resist films

Hiroshi Fukuda

Proceedings Volume PC12051, Optical and EUV Nanolithography XXXV; PC120510J (2022) https://doi.org/10.1117/12.2610998
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

Spatial fluctuation in reactions throughout exposed resist films is analyzed by principal component analysis (PCA) or singular value decomposition (SVD), which is known as generalization of Fourie transform. In optical simulations, image is expressed by a wave function obtained from SVD of matrix representation of optical images. We attempt to extend this to density/probability distributions of resist reactions for analyzing probabilistic behavior of resists such as LER/LCDU and stochastic defects. To show its effectiveness, PCA is applied to reaction distributions calculated by the fully coupled Monte-Carlo simulation, which visualizes correlated reaction influences on spatial feature and probability of pattern anomalies.

Conference Presentation

Citation Download Citation

Hiroshi Fukuda "Principal component analysis of stochasticity in resist films", Proc. SPIE PC12051, Optical and EUV Nanolithography XXXV, PC120510J (13 June 2022); https://doi.org/10.1117/12.2610998

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