Practical iterated fill synthesis for CMP uniformity

We propose practical iterated methods for layout density control for CMP uniformity, based on linear programming, Monte-Carlo and greedy algorithms. We experimentally study the tradeoffs between two main filling objectives: minimizing density variation, and minimizing the total amount of inserted fi...

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Veröffentlicht in:37th Design Automation Conference, 2000 S. 671 - 674
Hauptverfasser: Chen, Yu, Kahng, Andrew B., Robins, Gabriel, Zelikovsky, Alexander
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 01.01.2000
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Applied computing > Physical sciences and engineering > Electronics
Applied computing > Physical sciences and engineering > Engineering
Chemical processes
Computational geometry
Computer science
Filling
Greedy algorithms
Hardware > Electronic design automation > Physical design (EDA)
Hardware > Emerging technologies
Hardware > Robustness
Hardware > Very large scale integration design
Linear programming
Manufacturing processes
Mathematics of computing > Mathematical analysis > Mathematical optimization > Continuous optimization > Linear programming
Permission
Semiconductor device modeling
Solid modeling
Theory of computation > Design and analysis of algorithms > Mathematical optimization > Continuous optimization > Linear programming
ISBN:9781581131871, 1581131879
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:We propose practical iterated methods for layout density control for CMP uniformity, based on linear programming, Monte-Carlo and greedy algorithms. We experimentally study the tradeoffs between two main filling objectives: minimizing density variation, and minimizing the total amount of inserted fill. Comparisons with previous filling methods show the advantages of our new iterated Monte-Carlo and iterated greedy methods. We achieve near-optimal filling with respect to each of the objectives and for both density models (spatial density [3] and effective density [8]). Our new methods are more efficient in practice than linear programming [3] and more accurate than non-iterated Monte-Carlo approaches [1].
Bibliographie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781581131871
1581131879
DOI:10.1145/337292.337610