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  5. Path-Based Statistical Static Timing Analysis for Large Integrated Circuits in a Weak Correlation Approximation
 
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Path-Based Statistical Static Timing Analysis for Large Integrated Circuits in a Weak Correlation Approximation

Author(s)
Mishagli, Dmytro  
Koskin, Eugene  
Blokhina, Elena  
Uri
http://hdl.handle.net/10197/9830
Date Issued
2019-05-29
Date Available
2019-04-08T09:09:34Z
Abstract
This work is aimed at the development of a pathbased approach to Statistical Static Timing Analysis. Timing Analysis is an absolutely essential step in the verification of Very Large Scale Integration (VLSI) designs. We propose a novel analytical methodology for the fast calculations of VLSI delay. The problem is stated in such a way that becomes equivalent to finding the maximum of a large set of correlated random variables (RVs). For this purpose, a corresponding extension of extreme value theory of weakly-correlated RVs is developed. Results of simulations showing a comparison of our approach with Monte Carlo simulations are presented. Possible applications, extensions of our methodology and future steps are discussed.
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2019 IEEE
Subjects

Timing graph

Statistical timing al...

Very large scare inte...

Extreme value distrib...

DOI
10.1109/ISCAS.2019.8702198
Web versions
https://www.iscas2019.org/
Language
English
Status of Item
Peer reviewed
Journal
2019 IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings
Conference Details
The 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, 26-29 May 2019
ISBN
978-1-7281-0397-6
ISSN
2158-1525
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

koshagli_iscas19.pdf

Size

613.13 KB

Format

Adobe PDF

Checksum (MD5)

a713b7c92ee975eb87b9d8a09282c0d7

Owning collection
Electrical and Electronic Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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