Nanometer-to-millimeter scale computational lithography for the study, detection, classification, repair, and reduction of stochastic defects in EUVL

John J. Biafore; Mark D. Smith; Anatoly Burov; Pradeep Vukkadala; Guy Parsey; Cao Zhang; Craig Higgins; Kunlun Bai; Trey Graves; Yi Liu; Janez Krek; Sergei Bakarian; Kyeongeun Ko; Chi-Ping Liu; Alex Arkhipov; Roel Gronheid; Kaushik Sah; Loemba Bouckou; Andrew Cross; Vikram L. Tolani; Ady Levy

doi:10.1117/12.3034555

13 November 2024 Nanometer-to-millimeter scale computational lithography for the study, detection, classification, repair, and reduction of stochastic defects in EUVL

John J. Biafore, Mark D. Smith, Anatoly Burov, Pradeep Vukkadala, Guy Parsey, Cao Zhang, Craig Higgins, Kunlun Bai, Trey Graves, Yi Liu, Janez Krek, Sergei Bakarian, Kyeongeun Ko, Chi-Ping Liu, Alex Arkhipov, Roel Gronheid, Kaushik Sah, Loemba Bouckou, Andrew Cross, Vikram L. Tolani, Ady Levy

Author Affiliations +

Proceedings Volume PC13215, International Conference on Extreme Ultraviolet Lithography 2024; PC1321509 (2024) https://doi.org/10.1117/12.3034555
Event: SPIE Photomask Technology + EUV Lithography, 2024, Monterey, California, United States

Abstract

Natural physical phenomena occurring at length scales of a few nm in EUV lithography give rise to variation in photoresist images: edge, width, and top roughness, feature-to-feature CD or shape variability, edge placement errors, etc. The most damaging are stochastic printing failures caused by undesirable film thickness loss, admitting etch in line regions, or film thickness gain, preventing etch in space regions. In this work, we begin from analysis of well-calibrated rigorous physical stochastic EUV lithography models to study nanoscale exposure effects affecting stochastic failures. We apply acceleration to the stochastic model and perform computational inspection and classification of hot spots on a large layout area. The agreement between predicted probabilities of occurrence and observed defect frequencies are given for both line and space hot spots. We then perform computational inspection upon a virtual process and select hot spot locations and affect repairs. The actual mask is then fabricated, real wafers are exposed, processed, inspected and measured to compare the predicted reductions in defect probabilities with actual measured defect frequencies on wafer.

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John J. Biafore, Mark D. Smith, Anatoly Burov, Pradeep Vukkadala, Guy Parsey, Cao Zhang, Craig Higgins, Kunlun Bai, Trey Graves, Yi Liu, Janez Krek, Sergei Bakarian, Kyeongeun Ko, Chi-Ping Liu, Alex Arkhipov, Roel Gronheid, Kaushik Sah, Loemba Bouckou, Andrew Cross, Vikram L. Tolani, and Ady Levy "Nanometer-to-millimeter scale computational lithography for the study, detection, classification, repair, and reduction of stochastic defects in EUVL", Proc. SPIE PC13215, International Conference on Extreme Ultraviolet Lithography 2024, PC1321509 (13 November 2024); https://doi.org/10.1117/12.3034555

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KEYWORDS

Extreme ultraviolet lithography

Stochastic processes

Extreme ultraviolet

Inspection

Computational lithography

Photoresist materials

Photoresist processing

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