Paper Info
Title
FUZYE: A Fuzzy ${c}$ -Means Analog IC Yield Optimization Using Evolutionary-Based Algorithms
Abstract
This paper presents fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula> -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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula> -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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -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.
Year
DOI
Venue
2020
10.1109/TCAD.2018.2883978
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Keywords
DocType
Volume
Optimization,Integrated circuit modeling,Yield estimation,Sociology,Statistics,Analytical models
Journal
39
Issue
ISSN
Citations 
1
0278-0070
4
PageRank 
References 
Authors
0.44
0
7
Name
Order
Citations
PageRank
António Canelas17211.20
Ricardo Povoa25310.57
Ricardo Martins317920.81
Nuno C. Lourenço412218.24
Jorge Guilherme5146.02
João Paulo Carvalho611017.52
Nuno Cavaco Horta731049.65