DeePattern: Layout Pattern Generation with Transforming Convolutional Auto-Encoder

VLSI layout patterns provide critic resources in various design for manufacturability researches, from early technology node development to back-end design and sign-off flows. However, a diverse layout pattern library is not always available due to long logic-to-chip design cycle, which slows down the technology node development procedure. To address this issue, in this paper, we explore the capability of generative machine learning models to synthesize layout patterns. A transforming convolutional auto-encoder is developed to learn vector-based instantiations of squish pattern topologies. We show our framework can capture simple design rules and contributes to enlarging the existing squish topology space under certain transformations. Geometry information of each squish topology is obtained from an associated linear system derived from design rule constraints. Experiments on 7nm EUV designs show that our framework can more effectively generate diverse pattern libraries with DRC-clean patterns compared to a state-of-the-art industrial layout pattern generator.