L2O-ILT: Learning to Optimize Inverse Lithography Techniques
The 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.
Abstract
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.