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Proceedings Paper

Verification of directed self-assembly (DSA) guide patterns through machine learning
Author(s): Seongbo Shim; Sibo Cai; Jaewon Yang; Seunghune Yang; Byungil Choi; Youngsoo Shin
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Paper Abstract

Verification of full-chip DSA guide patterns (GPs) through simulations is not practical due to long runtime. We develop a decision function (or functions), which receives n geometry parameters of a GP as inputs and predicts whether the GP faithfully produces desired contacts (good) or not (bad). We take a few sample GPs to construct the function; DSA simulations are performed for each GP to decide whether it is good or bad, and the decision is marked in n-dimensional space. The hyper-plane that separates good marks and bad marks in that space is determined through machine learning process, and corresponds to our decision function. We try a single global function that can be applied to any GP types, and a series of functions in which each function is customized for different GP type; they are then compared and assessed in 10nm technology.

Paper Details

Date Published: 19 March 2015
PDF: 8 pages
Proc. SPIE 9423, Alternative Lithographic Technologies VII, 94231E (19 March 2015); doi: 10.1117/12.2085644
Show Author Affiliations
Seongbo Shim, KAIST (Korea, Republic of)
SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Sibo Cai, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Jaewon Yang, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Seunghune Yang, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Byungil Choi, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Youngsoo Shin, KAIST (Korea, Republic of)

Published in SPIE Proceedings Vol. 9423:
Alternative Lithographic Technologies VII
Douglas J. Resnick; Christopher Bencher, Editor(s)

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