Title
Adaptive post-silicon tuning for analog circuits: concept, analysis and optimization
Abstract
The well-known Pelgrom model [14] has demonstrated that the variation between two devices on the same die due to random mismatch is inversely proportional to the square root of the device area: σ ~ 1/sqrt(Area). Based on the Pelgrom model, analog devices are sized to be large enough to average out random variations. Importantly, with CMOS scaling, variations due to random doping fluctuations are making it exceedingly difficult to control device mismatches by sizing alone; namely, the devices have to be made so large that the benefits of CMOS scaling are not realized for analog and RF circuits. In this paper we propose a novel post-silicon tuning methodology to reduce random mismatches for analog circuits in sub-90nm CMOS. A novel dynamic programming algorithm is incorporated into a fast Monte Carlo simulation flow for statistical analysis and optimization of the proposed tunable analog circuits. We apply the proposed post-silicon tuning methodology to several commonly-used analog circuit blocks. We demonstrate that with the post-silicon tuning, device mismatch exponentially decreases as area increases: σ ~ exp(---α·Area).
Year
DOI
Venue
2007
10.1109/ICCAD.2007.4397306
ICCAD
Keywords
Field
DocType
cmos analogue integrated circuits,analog circuits,area increase,cmos scaling,statistical analysis,random doping fluctuation,monte carlo simulation flow,pelgrom model,random mismatch,circuit tuning,silicon,rf circuits,analog device,monte carlo methods,random doping fluctuations,dynamic programming algorithm,adaptive post-silicon tuning,adaptive post-silicon,elemental semiconductors,analog circuit,device area,dynamic programming,random mismatches,commonly-used analog circuit block,proposed tunable analog circuit,ring oscillator,concept analysis,monte carlo simulation
Dynamic programming,Monte Carlo method,Ring oscillator,Analogue electronics,Computer science,Electronic engineering,CMOS,Sizing,Electronic circuit,Square root,Electrical engineering
Conference
ISSN
ISBN
Citations 
1092-3152 E-ISBN : 978-1-4244-1382-9
978-1-4244-1382-9
13
PageRank 
References 
Authors
1.19
16
4
Name
Order
Citations
PageRank
Xin Li170948.36
Brian Taylor2394.11
YuTsun Chien3193.14
Lawrence Pileggi435831.47