GAN-Dummy Fill: Timing-aware Dummy Fill Method using GAN

Abstract

The chemical mechanical polishing (CMP) dummy fill method is commonly used for the planarization of the CMP process, resulting in the development of many automated methods. We propose a dummy fill method using a generative adversarial network (GAN) that improves the existing dummy fill methods in terms of the uniformity of metal density and timing of critical nets. The dummy patterns created were similar to those of existing methods. However, the GAN dummy fill method applies additional optimizations to make the CMP dummy fill pattern efficient. The method learns by adding density and parasitic capacitance to the loss function of the GAN. Compared to dummy patterns generated from commercial tools, dummy patterns generated from GAN-dummy fill reduced the negative timing slack due to parasitic capacitance by up to 45%.