# Efficient Bilevel Source Mask Optimization

arXiv: https://arxiv.org/abs/2405.09548 · HTML: https://mlsanity.com/paper/2405.09548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation](https://mlsanity.com/paper/2406.05250/) (2024)

## Abstract (arXiv)

Resolution Enhancement Techniques (RETs) are critical to meet the demands of advanced technology nodes. Among RETs, Source Mask Optimization (SMO) is pivotal, concurrently optimizing both the source and the mask to expand the process window. Traditional SMO methods, however, are limited by sequential and alternating optimizations, leading to extended runtimes without performance guarantees. This paper introduces a unified SMO framework utilizing the accelerated Abbe forward imaging to enhance precision and efficiency. Further, we propose the innovative textttBiSMO framework, which reformulates SMO through a bilevel optimization approach, and present three gradient-based methods to tackle the challenges of bilevel SMO. Our experimental results demonstrate that textttBiSMO achieves a remarkable 40% reduction in error metrics and 8× increase in runtime efficiency, signifying a major leap forward in SMO.