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Proceedings Paper

Machine learning techniques for OPC improvement at the sub-5 nm node
Author(s): Changsoo Kim; Seungjong Lee; Sangwoo Park; No-Young Chung; Jungmin Kim; Narae Bang; Sanghwa Lee; SooRyong Lee; Robert Boone; Pengcheng Li; Jiyoon Chang; Xinxin Zhou; YoungMi Kim; MinSu Oh; Minsung Kim; Rachit Gupta; Jun Ye; Stanislas Baron
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Paper Abstract

With conventional methods, improvements in optical proximity correction (OPC) runtime and accuracy can be challenging. Often improvements in accuracy have limited impact or require longer runtimes. Conversely, improvements in runtime often come at a sacrifice to overall correction quality. OPC industries have been developing and applying machine-learning (ML) methods to address both issues together, such as the Newron® machine learning family of products, which provides for both faster ML-based correction and more accurate resist models. Benchmark testing shows that ML-based correction prediction can yield runtime improvements of 30% or more without sacrificing pattern fidelity. It also shows that a ML resist model can deliver simulation accuracy 15% better than a conventional lithography model. This paper discusses the conversion flow from baseline OPC recipe to ML-accelerated recipe and presents results of a study that applies this technique to a sub-5 nm EUV test case, as well as results of a study that leverages a ML resist model to improve OPC accuracy.

Paper Details

Date Published: 23 March 2020
PDF: 14 pages
Proc. SPIE 11323, Extreme Ultraviolet (EUV) Lithography XI, 1132317 (23 March 2020);
Show Author Affiliations
Changsoo Kim, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Seungjong Lee, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Sangwoo Park, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
No-Young Chung, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Jungmin Kim, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Narae Bang, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Sanghwa Lee, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
SooRyong Lee, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Robert Boone, ASML (United States)
Pengcheng Li, ASML (United States)
Jiyoon Chang, ASML (United States)
Xinxin Zhou, ASML (United States)
YoungMi Kim, ASML (United States)
MinSu Oh, ASML (United States)
Minsung Kim, ASML (United States)
Rachit Gupta, ASML (United States)
Jun Ye, ASML (United States)
Stanislas Baron, ASML (United States)


Published in SPIE Proceedings Vol. 11323:
Extreme Ultraviolet (EUV) Lithography XI
Nelson M. Felix; Anna Lio, Editor(s)

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