L2O-ILT: Learning to Optimize Inverse Lithography Techniques

Inverse lithography technique (ILT) is one of the most widely used resolution enhancement techniques (RETs) to compensate for the diffraction effect in the lithography process. However, ILT suffers from runtime overhead issues with the shrinking size of technology nodes. In this article, our proposed L2O-ILT framework unrolls the iterative ILT optimization algorithm into a learnable neural network with high interpretability, which can generate a high-quality initial mask for fast refinement. Experimental results demonstrate that our method achieves better performance on both mask printability and runtime than the previous methods.

I. Introduction

With the continuous scaling-down of technology nodes, the proximity effect and optical diffraction are becoming non-neglectable, which seriously affects the yield of integrated circuits. Resolution enhancement techniques (RETs) are developed to reduce printing errors during the lithography process. Optical proximity correction (OPC) is one of the widely used RETs to compensate for lithography proximity effects by correcting mask pattern shapes and inserting assist features.