NeurFill: Migrating Full-Chip CMP Simulators to Neural Networks for Model-Based Dummy Filling Synthesis

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-seque...

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Veröffentlicht in:2021 58th ACM/IEEE Design Automation Conference (DAC) S. 187 - 192
Hauptverfasser: Cai, Junzhe, Yan, Changhao, Ma, Yuzhe, Yu, Bei, Zhou, Dian, Zeng, Xuan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 05.12.2021
Schlagworte:
Backpropagation
Design automation
Filling
Manufacturing
Neural networks
Semiconductor device modeling
Very large scale integration
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Zusammenfassung: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 \times 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.
DOI:10.1109/DAC18074.2021.9586325