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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Abstract 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.
AbstractList 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.
Author Yan, Changhao
Zhou, Dian
Ma, Yuzhe
Yu, Bei
Zeng, Xuan
Cai, Junzhe
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  organization: The Chinese University of Hong Kong,Hong Kong
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  organization: University of Texas at Dallas,USA
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  fullname: Zeng, Xuan
  email: xzeng@fudan.edu.cn
  organization: Fudan University,State Key Lab of ASIC & System,Microelectronics Department,Shanghai,China
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Snippet Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI...
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StartPage 187
SubjectTerms Backpropagation
Design automation
Filling
Manufacturing
Neural networks
Semiconductor device modeling
Very large scale integration
Title NeurFill: Migrating Full-Chip CMP Simulators to Neural Networks for Model-Based Dummy Filling Synthesis
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