Efficient Bilevel Source Mask Optimization

TL;DR

This paper presents a novel bilevel source mask optimization framework called BiSMO, which significantly improves efficiency and accuracy in resolution enhancement techniques by reformulating the problem and applying gradient-based methods.

Contribution

It introduces the BiSMO framework that reformulates SMO as a bilevel optimization problem and develops gradient-based methods for enhanced performance.

Findings

Achieves 40% reduction in error metrics.

Provides 8x faster runtime efficiency.

Demonstrates major improvements over traditional SMO methods.

Abstract

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 \texttt{BiSMO} 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 \texttt{BiSMO} achieves a remarkable 40\% reduction in error metrics and 8$\times$ increase in runtime efficiency, signifying a major leap forward in SMO.