A novel unified dummy fill insertion framework with SQP-based optimization method

Dummy fill insertion is widely applied to significantly improve the planarity of topographic patterns for chemical mechanical polishing process in VLSI manufacture. However, these dummies will lead to additional parasitic capacitance and deteriorate the circuit performance. The main challenge of dum...

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Vydané v:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 8
Hlavní autori:	Yudong Tao, Changhao Yan, Yibo Lin, Sheng-Guo Wang, Pan, David Z., Xuan Zeng
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 01.11.2016
Predmet:	Benchmark testing Degradation Dummy Fill Insertion Layout Linear programming Multi-Starting Point Technique (MSP) Optimization Parasitic capacitance Sequential Quadratic Programming Method (SQP) Solid modeling
ISSN:	1558-2434
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Popis
Shrnutí:	Dummy fill insertion is widely applied to significantly improve the planarity of topographic patterns for chemical mechanical polishing process in VLSI manufacture. However, these dummies will lead to additional parasitic capacitance and deteriorate the circuit performance. The main challenge of dummy filling algorithms is how to balance multiple objectives, such as fill amount, density variation, parasitic capacitance, etc. which is the aim of ICCAD 2014 DFM contest. Traditional dummy fill insertion methods are no longer applicable because they generate large amount of fills or take unaffordable time. In this paper, we propose a unified dummy fill insertion optimization framework based on multi-starting points and sequential quadratic programming optimization solver, where all objectives are considered simultaneously without approximation. Selecting the initial points smartly with prior knowledge, the proposed method can be effectively accelerated. Even without any prior knowledge, it can also reach high fill quality by random initial points with high scalability. The proposed algorithm is verified by ICCAD 2014 DFM contest benchmark, which shows better quality of dummy filling over the state-of-the-art algorithms.
ISSN:	1558-2434
DOI:	10.1145/2966986.2966994