Predictable etch model using machine learning

Youngchang Kim; Sunwook Jung; DooHwan Kwak; Vlad Liubich; Germain Fenger

doi:10.1117/12.2515271

20 March 2019 Predictable etch model using machine learning

Youngchang Kim, Sunwook Jung, DooHwan Kwak, Vlad Liubich, Germain Fenger

Proceedings Volume 10961, Optical Microlithography XXXII; 1096106 (2019) https://doi.org/10.1117/12.2515271
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Etch process is critical to CD control in patterning, but Etch-aware OPC is not as accurate as lithographyaware OPC. Etch process is not understood very well compared to lithography, so empirical etch model like Variable Etch Bias (VEB) has been used for OPC. Although VEB has been quite successful so far, accuracy of etch model needs be improved with below 10 nm node devices. Machine Learning (ML) is applied in this work for VEB model improvement. However, ML is also an extreme empirical model, in fact, so over-fitting is a big problem with machine learning. We demonstrate over-fitting as well as accuracy can be improved in this work as presenting specific methods of ML such as double-stage machine learning, etch-relevant inputs and ensuring sample-coverage.

Conference Presentation

Citation Download Citation

Youngchang Kim, Sunwook Jung, DooHwan Kwak, Vlad Liubich, and Germain Fenger "Predictable etch model using machine learning", Proc. SPIE 10961, Optical Microlithography XXXII, 1096106 (20 March 2019); https://doi.org/10.1117/12.2515271

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