Open AccessFast lithographic source optimization method of certain contour sampling-Bayesian compressive sensing for high fidelity patterning
Author(s) -
Yiyu Sun,
Yanqiu Li,
Tie Li,
Yuelei Xu,
Enze Li,
Pengzhi Wei
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.032733
Subject(s) - fidelity , compressed sensing , high fidelity , sampling (signal processing) , computer science , lithography , bayesian optimization , algorithm , bayesian probability , pixel , optics , artificial intelligence , computer vision , physics , telecommunications , acoustics , filter (signal processing)
Fast source optimization (SO) is in demand urgently for holistic lithography on-line at 14-5 nm nodes. Our earlier works of fast compressive sensing (CS) SO methods adopted randomly sampling monitoring pixels on layout patterns, consequently resulting in failure of SO sometimes and poor image fidelity compared to gradient-based SO with complete sampling (SD-SO). This paper proposes a novel certain contour sampling-Bayesian compressive sensing SO (CCS-BCS-SO) method to achieve the goals of fast SO and high fidelity patterns simultaneously. The CCS assures the optimized source uniquely and reduces the computational complexity significantly. The BCS theory, to our best knowledge, is for the first time applied to resolution enhancement techniques (RETs) in lithography systems to ensure high fidelity patterns. The results demonstrate that CCS-BCS-SO simultaneously achieves fast SO like CS-SO and high fidelity patterns like SD-SO.