NeurFill: Migrating Full-Chip CMP Simulators to Neural Networks for Model-Based Dummy Filling Synthesis - Cai, Junzhe; Yan, Changhao; Ma, Yuzhe; Yu, Bei; Zhou, Dian; Zeng, Xuan (2021 58th ACM/IEEE Design Automation Conference (DAC), 2021)
Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI manufacturing. This paper proposes a novel model-based dummy filling synthesis framework NeurFill, integrated with multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver. Inside this framework, a full-chip CMP simulator is first migrated to the neural network, achieving 8134 × speedup on gradient calculation by backward propagation. Multi-modal starting points search is further applied in the framework to obtain satisfying filling quality optimums. The experimental results show that the proposed NeurFill outperforms existing rule- and model-based methods.