A practical and efficient approach to the constrained via minimization problem

Tang, Maolin, Chen, Lihui, & Chua, Hock-Chuan (2001) A practical and efficient approach to the constrained via minimization problem. Computers and Electrical Engineering, 27(2), pp. 201-216.

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This paper presents an efficient and practical approach to the Constrained Via Minimization (CVM) problem, which assigns wire segments to the layers, using the minimum number of vias, given a feasible partial routing. The feasible partial routing is first represented by a directed bipartite graph to reflect the mutual constraints. An energy function is then proposed to turn the problem into a cost optimization problem. An efficient heuristic algorithm, combining hill-climbing and simulated annealing, is developed for the cost optimization. The algorithm has the capability to escape from the local minimums and eventually reaches a near-optimal or optimal solution. The proposed method is practical as it can handle many practical constraints such as a multi-way wire split from a single via, and pre-allocation of power nets and terminals. Experimental results show that our proposed method is efficient in handling complex grid-based routing.

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2 citations in Scopus
2 citations in Web of Science®
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ID Code: 8664
Item Type: Journal Article
Refereed: Yes
Additional Information: For more information, please refer to the journal's website (see hypertext link) or contact the author.
Author contact details: m.tang@qut.edu.au
Keywords: Constrained via minimization, Topological via minimization, Layer assignment problem, Heuristic algorithm, Cost optimization
DOI: 10.1016/S0045-7906(00)00015-X
ISSN: 0045-7906
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Microelectronics and Integrated Circuits (090604)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2001 Elsevier
Deposited On: 16 Jul 2007 00:00
Last Modified: 15 Jan 2009 07:38

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