FUZYE: A Fuzzy -Means Analog IC Yield Optimization Using Evolutionary-Based Algorithms

This paper presents fuzzy c-means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the gene...

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Vydáno v:IEEE transactions on computer-aided design of integrated circuits and systems Ročník 39; číslo 1; s. 1 - 13
Hlavní autoři: Canelas, Antonio, Povoa, Ricardo, Martins, Ricardo, Lourenco, Nuno, Guilherme, Jorge, Carvalho, Joao Paulo, Horta, Nuno
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Analog circuits
analog IC yield estimation
Analog integrated circuit (IC) synthesis
Analytical models
circuit optimization
Clustering
clustering algorithms
Computer simulation
Evolutionary algorithms
expensive simulations
Genetic algorithms
Integrated circuit modeling
Integrated circuits
Monte Carlo (MC) analysis
Optimization
Population
Simulation
Sociology
Statistics
Yield estimation
ISSN:0278-0070, 1937-4151
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Shrnutí:This paper presents fuzzy c-means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the genetic algorithm (GA). MC analysis is the most general and reliable technique for yield estimation, yet the considerable amount of time it requires has discouraged its adoption in population-based optimization tools. The proposed methodology reduces the total number of MC simulations that are required, since, at each GA generation, the population is clustered using a fuzzy c-means (FCMs) technique, and, only the representative individual (RI) from each cluster is subject to MC simulations. This paper shows that the yield for the rest of the population can be estimated based on the membership degree of FCM and RIs yield values alone. This new method was applied on two real circuit-sizing optimization problems and the obtained results were compared to the exhaustive approach, where all individuals of the population are subject to MC analysis. The FCM approach presents a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. Moreover, a k-means-based clustering algorithm was also tested and compared with the proposed FUZYE, with the latest showing an improvement up to 13% in yield estimation accuracy.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2018.2883978