Presentation + Paper
1 May 2023 A novel approach to etch-process-aware intensive layout retarget
Author Affiliations +
Abstract
Patterning, a major process in semiconductor manufacturing, aims to transfer the design layout to the wafer. Accordingly, the "process proximity correction" method was developed to overcome the difference in after-cleaninginspected CD (critical dimension) between patterns of similar shapes. However, its physical model is often limited in the predictive performance. Therefore, recent studies have introduced ML (machine learning) technology to supplement model accuracy, but this approach often has an inherent risk of overfitting depending on the type of sampled pattern. In this study, we present a newly invented flow capable of stable etch-process-aware ML modeling by model reconstruction and large amounts of measurement data. The new modeling flow can also be performed within a reasonable runtime through efficient feature extraction. Based on the new model and its related layout targeting platform, intensive improvements were made to CD targeting and spread; for a given layout, in comparison with delicate rule-based modification, the CD targeting accuracy was improved by 4 times and approaches the limit of metrology error.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeeyong Lee, Yangwoo Heo, Ryanggeun Lee, Sangwook Kim, Jisuk Hong, Kyoil Koo, Changmook Yim, Jungmin Kim, Sooyong Lee, Joonsung Kim, Dongho Kim, Seung-Hune Yang, and Seongtae Jeong "A novel approach to etch-process-aware intensive layout retarget", Proc. SPIE 12499, Advanced Etch Technology and Process Integration for Nanopatterning XII, 124990C (1 May 2023); https://doi.org/10.1117/12.2657880
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KEYWORDS
Etching

Data modeling

Modeling

Machine learning

Design and modelling

Optical proximity correction

Statistical modeling

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