A novel unified dummy fill insertion framework with SQP-based optimization method
Author:
Affiliation:
1. Fudan University, Shanghai, China
2. University of Texas at Austin
3. University of North Carolina at Charlotte
Funder
National Natural Science Foundation of China
NSF Grant
Key Technologies R&D Program
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2966986.2966994
Reference19 articles.
1. Sequential Quadratic Programming
2. Performance-impact limited area fill synthesis
3. Monte-Carlo algorithms for layout density control
4. Practical iterated fill synthesis for CMP uniformity
5. Provably good and practically efficient algorithms for CMP dummy fill
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2. pNeurFill: Enhanced Neural Network Model-Based Dummy Filling Synthesis With Perimeter Adjustment;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-02
3. NeurFill: Migrating Full-Chip CMP Simulators to Neural Networks for Model-Based Dummy Filling Synthesis;2021 58th ACM/IEEE Design Automation Conference (DAC);2021-12-05
4. A Novel and Unified Full-Chip CMP Model Aware Dummy Fill Insertion Framework With SQP-Based Optimization Method;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2021-03
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