GAN-OPC: Mask Optimization With Lithography-Guided Generative Adversarial Nets

Mask optimization has been a critical problem in the VLSI design flow due to the mismatch between the lithography system and the continuously shrinking feature sizes. Optical proximity correction (OPC) is one of the prevailing resolution enhancement techniques (RETs) that can significantly improve mask printability. However, in advanced technology nodes, the mask optimization process consumes more and more computational resources. In this article, we develop a generative adversarial network (GAN) model to achieve better mask optimization performance. We first develop an OPC-oriented GAN flow that can learn target-mask mapping from the improved architecture and objectives, which leads to satisfactory mask optimization results. To facilitate the training process and ensure better convergence, we propose a pretraining scheme that jointly trains the neural network with inverse lithography technique (ILT). We also propose an enhanced generator design with a U-Net architecture and a subpixel super-resolution structure that promise a better convergence and a better mask quality, respectively. At convergence, the generative network is able to create quasi-optimal masks for given target circuit patterns and fewer normal OPC steps are required to generate high quality masks. The experimental results show that our flow can facilitate the mask optimization process as well as ensure a better printability.