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Fast implicit active contour model for inverse lithography | JoVE Visualize

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Fast implicit active contour model for inverse lithography

Yijiang Shen, Yanzhou Zhou, Zhenrong Zhang

Optics Express|April 6, 2021

View abstract on PubMed

Summary

This study introduces a generalized active contour model for inverse lithography, enhancing pattern contour accuracy using image-driven forces and an efficient numerical scheme. The method improves computation and convergence efficiency for complex layout patterns.

Area of Science:

Computational imaging
Computer vision
Applied mathematics

Background:

Inverse lithography presents challenges in accurately defining desired layout patterns.
Existing methods may lack efficiency or precision in contour generation.
Level-set methods and inverse imaging offer potential for improved solutions.

Purpose of the Study:

To develop a generalized active contour model for inverse lithography problems.
To introduce a cognitive analogy for guiding pattern contour evolution.
To enhance computational and convergence efficiency in numerical schemes.

Main Methods:

Combining level-set methods with inverse imaging principles.
Implementing a locally defined semi-implicit difference scheme.
Utilizing additive operator splitting (AOS) and the Thomas method for solving linear systems.

Main Results:

The generalized active contour model effectively guides pattern contours towards desired layouts.
The semi-implicit numerical scheme demonstrates improved computation and convergence efficiency.
Reduced optimization dimensionality and larger step-sizes contribute to enhanced performance.

Conclusions:

The proposed model offers an efficient and accurate approach for inverse lithography.
The cognitive analogy provides an intuitive mechanism for contour evolution.
The numerical scheme is robust and scalable for complex pattern generation.

Artificial Intelligence

Knowledge Representation And Reasoning

Fast Implicit Active Contour Model For Inverse Lithography