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Proceedings Paper

Machine learning-based 3D resist model
Author(s): Seongbo Shim; Suhyeong Choi; Youngsoo Shin
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Paper Abstract

Accurate prediction of resist profile has become more important as technology node shrinks. Non-ideal resist profiles due to low image contrast and small depth of focus affect etch resistance and post-etch result. Therefore, accurate prediction of resist profile is important in lithographic hotspot verification. Standard approaches based on a single- or multiple-2D image simulation are not accurate, and rigorous resist simulation is too time consuming to apply to full-chip. We propose a new approach of resist profile modeling through machine learning (ML) technique. A position of interest are characterized by some geometric and optical parameters extracted from surroundings near the position. The parameters are then submitted to an artificial neural network (ANN) that outputs predicted value of resist height. The new resist 3D model is implemented in commercial OPC tool and demonstrated using 10nm technology metal layer.

Paper Details

Date Published: 30 March 2017
PDF: 10 pages
Proc. SPIE 10147, Optical Microlithography XXX, 101471D (30 March 2017); doi: 10.1117/12.2257904
Show Author Affiliations
Seongbo Shim, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
KAIST (Korea, Republic of)
Suhyeong Choi, KAIST (Korea, Republic of)
Youngsoo Shin, KAIST (Korea, Republic of)


Published in SPIE Proceedings Vol. 10147:
Optical Microlithography XXX
Andreas Erdmann; Jongwook Kye, Editor(s)

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