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13 June 2022 Improved prediction of assist feature printing with physically-based NTD compact models
Folarin Latinwo, Yulu Chen, Cheng-En Wu, Peter Brooker, Hyesook Hong, Delian Yang, Hua Song, Kevin Lucas
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Proceedings Volume 12052, DTCO and Computational Patterning; 120520P (2022) https://doi.org/10.1117/12.2615990
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States
Abstract
As complex NTD resist behavior significantly impacts AF wafer printing , there is a need to better model NTD AF printing. We present our work to further enhance NTD compact modeling accuracy for AF printing prediction by physics-based and data-based methods. The physics-based enhancements are derived from improvements in behavioral mechanisms in exposed and partially exposed NTD materials. The data-based enhancements are derived from learning methodologies developed for predicting lithography hot-spots at the limits of process control. Both types of enhancements are needed to predict fine changes in imaging and resist behavior where traditional compact models break down.
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Folarin Latinwo, Yulu Chen, Cheng-En Wu, Peter Brooker, Hyesook Hong, Delian Yang, Hua Song, and Kevin Lucas "Improved prediction of assist feature printing with physically-based NTD compact models", Proc. SPIE 12052, DTCO and Computational Patterning, 120520P (13 June 2022); https://doi.org/10.1117/12.2615990
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KEYWORDS
Printing
Data modeling
Data processing
Metrology
Optical lithography
Photoresist developing
Photoresist materials
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