Neural-ILT: Migrating ILT to Neural Networks for Mask Printability and Complexity Co-optimization

Optical proximity correction (OPC) for advanced technology node now has become extremely expensive and challenging. Conventional model-based OPC encounters performance degradation and large process variation, while aggressive approach such as inverse lithography technology (ILT) suffers from large computational overhead for both mask optimization and mask writing processes. In this paper, we developed Neural-ILT, an end-to-end learning-based OPC framework, which literally conducts mask prediction and ILT correction for a given layout in a single neural network, with the objectives of (1) mask printability enhancement, (2) mask complexity optimization and (3) flow acceleration. Quantitative results show that, comparing to the state-of-the-art (SOTA) learning-based OPC solution and conventional ILT flow, Neural-ILT can achieve 30× ~ 70× turn around time (TAT) speedup with lower mask complexity and comparable mask printability. We believe this work could arouse the interests of bridging well-developed deep learning toolkits to GPU-based high-performance lithographic computations to achieve groundbreaking performance boosting on various computational lithography-related tasks.

1 Introduction

Computational lithography models are designed to learn the printing effects of real lithography patterns. Building on top of these delicate lithographic models, advance resolution enhancement techniques (RETs) such as sub-resolution assist feature (SRAF) insertion and optical proximity correction (OPC) help the designers to obtain optimized masks that result in high fidelity printed patterns [1].