Hybrid deep learning OPC framework with generative adversarial network

Abstract

OPC is a key step to improve design fidelity when people transfer patterns from the photomask to the wafer. However, to complete a traditional OPC job in advanced technology node, a huge number of CPU cores and above several days are required. In this article, we proposed a pixel based OPC and deep learning OPC hybrid optimization framework. First, the pixOPC is done with the raw training clips. The pairs of the raw training clip and post OPC clip form the training data set. The training clip pairs are fed into GAN (Generative Adversarial Network) OPC architecture and the GAN network is trained. The GAN OPC generator is then validated to ensure that it has enough accuracy and does not overfit the data. The validated GAN OPC generator is then applied to generate OPC masks for the new design clips and the generated masks are refined with traditional OPC to exclude some unexpected outliers generated by the GAN method. We design the reversed high discretion pix2pix GAN to generate OPC masks. Its runtime and performance are compared with the model based OPC, pixOPC and U-Net. The generated OPC masks, simulated lithographic contours, EPE, PVBAND and NILS are compared. We find the GAN generative models have better performance compared with the traditional OPC, and the runtime are also much shorter.