Advanced simulations using an improved metal oxide photoresist model

Craig D. Needham; Ulrich Welling; Amrit Narasimhan; Peter De Schepper; Lauren McQuade; Michael Kocsis; Lawrence S. Melvin III; Jason Stowers; Stephen T. Meyers

doi:10.1117/12.3010941

10 April 2024 Advanced simulations using an improved metal oxide photoresist model

Craig D. Needham, Ulrich Welling, Amrit Narasimhan, Peter De Schepper, Lauren McQuade, Michael Kocsis, Lawrence S. Melvin III, Jason Stowers, Stephen T. Meyers

Author Affiliations +

Proceedings Volume 12957, Advances in Patterning Materials and Processes XLI; 129571B (2024) https://doi.org/10.1117/12.3010941
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Metal oxide photoresists are recognized as an integral component in the high–numerical aperture future of EUV lithography and the challenging feature sizes required at associated nodes. Many properties of these materials, such as their high EUV absorptivity, etch selectivity, and relatively small molecular size are particularly beneficial for enabling the advancement of lithographic processes. To help ease process development efforts involving these materials, a previously reported rigorous stochastic lithography model has been built using in-depth knowledge of the chemical and material processes that govern the behavior of spin-on metal oxide resists. Experimental data from a series of measurement techniques were used to both define and parametrize the fundamental equations that underlie the lithographic performance of these materials. The resulting parameters were then calibrated to a dataset derived from an extensive series of CD-SEM and open-frame exposures. In this model update, the match between simulated data and experiment has been improved both by more targeted calibration efforts and by the inclusion of more diverse exposure measurements into the calibration dataset. Interrogating the changes necessary to improve performance provides insights into resist behavior and how disparate process steps are interrelated. The updated model is used to simulate a series of exposure conditions outside of the calibration dataset to both validate the model and show its capabilities. Together, the fundamental nature of the model and the insights gained through its calibration provide a powerful tool to drive process optimization for metal oxide materials.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Craig D. Needham, Ulrich Welling, Amrit Narasimhan, Peter De Schepper, Lauren McQuade, Michael Kocsis, Lawrence S. Melvin III, Jason Stowers, and Stephen T. Meyers "Advanced simulations using an improved metal oxide photoresist model", Proc. SPIE 12957, Advances in Patterning Materials and Processes XLI, 129571B (10 April 2024); https://doi.org/10.1117/12.3010941

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