High-Sigma Performance Analysis using Multi-Objective Evolutionary Algorithms

Martin Albrecht Trefzer; Andy Tyrrell; Simon Jonathan Bale; James Alfred Walker

High-Sigma Performance Analysis using Multi-Objective Evolutionary Algorithms

Martin Albrecht Trefzer, Andy Tyrrell, Simon Jonathan Bale, James Alfred Walker

Electronic Engineering

Research output: Contribution to conference › Poster › peer-review

Abstract

Semiconductor devices have rapidly improved in performance and function density over the past 25 years enabled by the continuous shrinking of technology feature sizes. Fabricating transistors that small, even with advanced processes, results in structural irregularities at the atomic scale, which affect device characteristics in a random manner. To simulate performance of circuits comprising a large number of devices using statistical models and ensuring low failure rates, performance outliers are required to be investigated. Standard Monte Carlo analysis will quickly become intractable because of the large number of circuit simulations required. Cases where the number of samples exceeds are known as “high-sigma problems”. This work proposes a highsigma
sampling methodology based on multi-objective optimisation using evolutionary algorithms. A D-type Flip Flop is presented as a case study and it is shown that higher sigma outliers can be reached using a similar number of SPICE evaluations as Monte Carlo analysis.

Original language	English
Publication status	Published - Jun 2015
Event	Design Automation Conference (DAC 2015) - San Francisco, United States Duration: 7 Jun 2015 → 9 Jul 2015

Conference

Conference	Design Automation Conference (DAC 2015)
Country/Territory	United States
City	San Francisco
Period	7/06/15 → 9/07/15

Access to Document

highsigmaAccepted author manuscript, 401 KB
trefzer-highsigma_DAC-2015Accepted author manuscript, 5.83 MB

http://www.dac.com/

Bio-inspired Adaptive Architectures and Systems
Tyrrell, A. (Principal investigator), Dunn, K. (Co-investigator), Tempesti, G. (Co-investigator), Timmis, J. (Co-investigator), Trefzer, M. A. (Co-investigator) & Turner, A. P. (Co-investigator)
EPSRC
28/02/14 → 31/08/19
Project: Research project (funded) › Research

Cite this

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title = "High-Sigma Performance Analysis using Multi-Objective Evolutionary Algorithms",

abstract = "Semiconductor devices have rapidly improved in performance and function density over the past 25 years enabled by the continuous shrinking of technology feature sizes. Fabricating transistors that small, even with advanced processes, results in structural irregularities at the atomic scale, which affect device characteristics in a random manner. To simulate performance of circuits comprising a large number of devices using statistical models and ensuring low failure rates, performance outliers are required to be investigated. Standard Monte Carlo analysis will quickly become intractable because of the large number of circuit simulations required. Cases where the number of samples exceeds are known as “high-sigma problems”. This work proposes a highsigmasampling methodology based on multi-objective optimisation using evolutionary algorithms. A D-type Flip Flop is presented as a case study and it is shown that higher sigma outliers can be reached using a similar number of SPICE evaluations as Monte Carlo analysis.",

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T1 - High-Sigma Performance Analysis using Multi-Objective Evolutionary Algorithms

AU - Trefzer, Martin Albrecht

AU - Tyrrell, Andy

AU - Bale, Simon Jonathan

AU - Walker, James Alfred

PY - 2015/6

Y1 - 2015/6

N2 - Semiconductor devices have rapidly improved in performance and function density over the past 25 years enabled by the continuous shrinking of technology feature sizes. Fabricating transistors that small, even with advanced processes, results in structural irregularities at the atomic scale, which affect device characteristics in a random manner. To simulate performance of circuits comprising a large number of devices using statistical models and ensuring low failure rates, performance outliers are required to be investigated. Standard Monte Carlo analysis will quickly become intractable because of the large number of circuit simulations required. Cases where the number of samples exceeds are known as “high-sigma problems”. This work proposes a highsigmasampling methodology based on multi-objective optimisation using evolutionary algorithms. A D-type Flip Flop is presented as a case study and it is shown that higher sigma outliers can be reached using a similar number of SPICE evaluations as Monte Carlo analysis.

AB - Semiconductor devices have rapidly improved in performance and function density over the past 25 years enabled by the continuous shrinking of technology feature sizes. Fabricating transistors that small, even with advanced processes, results in structural irregularities at the atomic scale, which affect device characteristics in a random manner. To simulate performance of circuits comprising a large number of devices using statistical models and ensuring low failure rates, performance outliers are required to be investigated. Standard Monte Carlo analysis will quickly become intractable because of the large number of circuit simulations required. Cases where the number of samples exceeds are known as “high-sigma problems”. This work proposes a highsigmasampling methodology based on multi-objective optimisation using evolutionary algorithms. A D-type Flip Flop is presented as a case study and it is shown that higher sigma outliers can be reached using a similar number of SPICE evaluations as Monte Carlo analysis.

M3 - Poster

T2 - Design Automation Conference (DAC 2015)

Y2 - 7 June 2015 through 9 July 2015

ER -

High-Sigma Performance Analysis using Multi-Objective Evolutionary Algorithms

Abstract

Conference

Access to Document

Projects

Bio-inspired Adaptive Architectures and Systems

Cite this