Speeding up OPC by leveraging existing designs with machine learning

Abstract

We propose a machine-learning-based mechanism to perform OPC, which is much more efficient than traditional OPC processes in terms of compute resources. Building a physical model for OPC takes a lot of labor and computational time, for example, model calibration requires thousands of cores for up to ten hours, and, OPC data prepare needs thousands of cores for a couple of days. We present a way to use learning to produce OPC mask designs from a large amount of lithography target data with a computationally cheap approach. Our technique uses learning based on pairs of lithography target data and OPCed mask. The impact of different learning algorithm on the quality and performance of mask prediction has been studied. We have tested multiple learning algorithm, such as PyTorch, Multilayer perceptron on IBM cloud. Preliminary evaluation of our technique on a standard contact EUV testsite shows accuracy similar to the standard processes using much less compute power.