Stochastic thermal simulation considering spatial correlated within-die process variations

Pei Yu Huang; Jia Hong Wu; Yu-Min Lee

doi:10.1109/ASPDAC.2009.4796437

Stochastic thermal simulation considering spatial correlated within-die process variations

Pei Yu Huang^*, Jia Hong Wu, Yu-Min Lee

^*Corresponding author for this work

National Chiao Tung University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

In this work, we develop a statistical thermal simulator including the effect of spatial correlation under within- die process variations. This method utilizes the Karhunen-Loeve(KL) expansion to model the physical parameters, and apply the Polynomial Chaoses(PCs) and the stochastic Galerkin method to tackle stochastic heat transfer equations. We demonstrate the accuracy and efficiency of our simulator by comparing with the Monte Carlo simulation, and point out that the stochastic thermal analysis is essential to provide a robust estimation of temperature distribution for the thermal-aware design flow.

Original language	English
Title of host publication	Proceedings of the ASP-DAC 2009
Subtitle of host publication	Asia and South Pacific Design Automation Conference 2009
Pages	31-36
Number of pages	6
DOIs	https://doi.org/10.1109/ASPDAC.2009.4796437
State	Published - 20 Apr 2009
Event	Asia and South Pacific Design Automation Conference 2009, ASP-DAC 2009 - Yokohama, Japan Duration: 19 Jan 2009 → 22 Jan 2009

Publication series

Name	Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference	Asia and South Pacific Design Automation Conference 2009, ASP-DAC 2009
Country	Japan
City	Yokohama
Period	19/01/09 → 22/01/09

Access to Document

10.1109/ASPDAC.2009.4796437

Link to publication in Scopus

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Cite this

Huang, P. Y., Wu, J. H., & Lee, Y-M. (2009). Stochastic thermal simulation considering spatial correlated within-die process variations. In Proceedings of the ASP-DAC 2009: Asia and South Pacific Design Automation Conference 2009 (pp. 31-36). [4796437] (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). https://doi.org/10.1109/ASPDAC.2009.4796437

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title = "Stochastic thermal simulation considering spatial correlated within-die process variations",

abstract = "In this work, we develop a statistical thermal simulator including the effect of spatial correlation under within- die process variations. This method utilizes the Karhunen-Loeve(KL) expansion to model the physical parameters, and apply the Polynomial Chaoses(PCs) and the stochastic Galerkin method to tackle stochastic heat transfer equations. We demonstrate the accuracy and efficiency of our simulator by comparing with the Monte Carlo simulation, and point out that the stochastic thermal analysis is essential to provide a robust estimation of temperature distribution for the thermal-aware design flow.",

author = "Huang, {Pei Yu} and Wu, {Jia Hong} and Yu-Min Lee",

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Huang, PY, Wu, JH & Lee, Y-M 2009, Stochastic thermal simulation considering spatial correlated within-die process variations. in Proceedings of the ASP-DAC 2009: Asia and South Pacific Design Automation Conference 2009., 4796437, Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, pp. 31-36, Asia and South Pacific Design Automation Conference 2009, ASP-DAC 2009, Yokohama, Japan, 19/01/09. https://doi.org/10.1109/ASPDAC.2009.4796437

Stochastic thermal simulation considering spatial correlated within-die process variations. / Huang, Pei Yu; Wu, Jia Hong; Lee, Yu-Min.

Proceedings of the ASP-DAC 2009: Asia and South Pacific Design Automation Conference 2009. 2009. p. 31-36 4796437 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this work, we develop a statistical thermal simulator including the effect of spatial correlation under within- die process variations. This method utilizes the Karhunen-Loeve(KL) expansion to model the physical parameters, and apply the Polynomial Chaoses(PCs) and the stochastic Galerkin method to tackle stochastic heat transfer equations. We demonstrate the accuracy and efficiency of our simulator by comparing with the Monte Carlo simulation, and point out that the stochastic thermal analysis is essential to provide a robust estimation of temperature distribution for the thermal-aware design flow.

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