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A Swarm Intelligence based Automated Framework for Variability Analysis
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TLDR
In this paper, an automated framework is proposed that uses swarm intelligence based optimization technique, namely particle swarm optimization algorithm, to estimate the worst-case variability bounds of the system response.Abstract:
In this paper, a novel methodology for variability analysis of CMOS circuits is presented. An automated framework is proposed that uses swarm intelligence based optimization technique, namely particle swarm optimization algorithm, to estimate the worst-case variability bounds of the system response. The efficacy of the proposed method is illustrated by performing the variability in phase noise of a 2.4 GHz CMOS LC tank RF oscillator. The proposed methodology is investigated and validated by comparing it with the conventional Monte Carlo simulations technique. For this case study, the proposed method is found to be significantly time-efficient.read more
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Journal ArticleDOI
Bridging the Gap between Physical and Circuit Analysis for Variability-Aware Microwave Design: Modeling Approaches
TL;DR: This paper presents a flexible procedure to extract black-box models from accurate physics-based simulations, namely TCAD analysis of the active devices and EM simulations for the passive structures, incorporating the dependence on the most relevant fabrication process parameters.
References
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Book
Nature-Inspired Metaheuristic Algorithms
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
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Tracking and optimizing dynamic systems with particle swarms
Russell C. Eberhart,Yuhui Shi +1 more
TL;DR: Three kinds of dynamic systems are defined for the purposes of this paper and one of them is chosen for preliminary analysis using the particle swarm on the parabolic benchmark function.
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Oscillator phase noise: a tutorial
Thomas H. Lee,Ali Hajimiri +1 more
TL;DR: The time-varying phase noise model presented in this tutorial identifies the importance of symmetry in suppressing the upconversion of 1/f noise into close-in phase noise, and provides an explicit appreciation of cyclostationary effects and AM-PM conversion.
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Understanding MOSFET mismatch for analog design
P.G. Drennan,Colin C. McAndrew +1 more
TL;DR: In this article, a physically based mismatch model was used to obtain dramatic improvements in prediction of MOSFET mismatch for analog design, and the model was applied to current mirrors to show some nonobvious effects over bias, geometry, and multiple unit devices.
Proceedings ArticleDOI
Challenge: variability characterization and modeling for 65- to 90-nm processes
TL;DR: A new test-structure is developed to precisely measure the on-chip variation of key LSI components (MOST, R, C, and circuit-delay) and it is found that variation can be suppressed due to its randomness features in multi-stage circuitry and high-performance, large-gate-area driver CMOS devices.
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