Advances in OPC etch modeling

Germain L. Fenger; Youngchang Kim; Ignat Moskalenko; Johnny Herrera; Alexander Drutsa

doi:10.1117/12.2614280

13 June 2022 Advances in OPC etch modeling

Germain L. Fenger, Youngchang Kim, Ignat Moskalenko, Johnny Herrera, Alexander Drutsa

Author Affiliations +

Proceedings Volume PC12056, Advanced Etch Technology and Process Integration for Nanopatterning XI; PC1205605 (2022) https://doi.org/10.1117/12.2614280
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

Tighter edge placement requirements for advanced nodes has driven model accuracy requirements, especially in the area of etch modeling. Etch model errors are becoming a larger part of the total model error and effects that could previously be ignored, now have to be addressed. To address systematic errors in etch modeling, new modeling techniques for etch modeling are presented, namely, Variable Edge Bias (VEB) model, the Reactive Ion Etch Variable Bias Model (RIE VEB), and finally, neural network assisted dual stage etch (N2E) model. The VEB RIE model enables the ability to represent trends relating to physical parameters, such as time and temperature into the model. To further improve model accuracy, a machine learning solution is introduced, which operates on the etch model’s residual error.

Conference Presentation

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Germain L. Fenger, Youngchang Kim, Ignat Moskalenko, Johnny Herrera, and Alexander Drutsa "Advances in OPC etch modeling", Proc. SPIE PC12056, Advanced Etch Technology and Process Integration for Nanopatterning XI, PC1205605 (13 June 2022); https://doi.org/10.1117/12.2614280

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KEYWORDS

Etching

Reactive ion etching

Optical proximity correction

Ions

Machine learning

Neural networks

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