A hybrid approach for the sensitivity analysis of integrated inductors

Sallem, Amin; Pereira, Pedro; Fino, M. Helena; Fakhfakh, Mourad

doi:10.1016/j.vlsi.2015.04.006

Cited by 3 publications

(5 citation statements)

References 23 publications

(31 reference statements)

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“…Due to the variability of CMOS technology parameters and tolerance of component values, sensitivity analysis has become unavoidable, as stressed in [15][16][17][18]. The available literature offers a plethora of published papers offering different approaches for sizing analog circuits, while taking into consideration sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The available literature offers a plethora of published papers offering different approaches for sizing analog circuits, while taking into consideration sensitivity analysis. Most of them use the Richardson exploration technique within an in-loop sizing approach; see for instance [17][18][19][20]. However, the already published techniques generally proceed a posteriori for generating low sensitive solutions when handling multi-objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…pb is not subject to the same process, otherwise the program can easily converge prematurely. Richardson extrapolation technique [17][18][19][20] is applied herein within the in-loop algorithm to compute sensitivity values. For fixing gb, different strategies have been considered (see Cases #0-4 below).…”

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confidence: 99%

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Fast and Efficient Sensitivity Aware Multi-Objective Optimization of Analog Circuits

et al. 2019

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This article introduces a novel approach for generating low-sensitive Pareto fronts of analog circuit performances. The main idea consists of taking advantage from the social interaction between particles within a multi-objective particle swarm optimization algorithm by progressively guiding the global leading process towards low sensitive solutions inside the landscape. We show that the proposed approach significantly outperforms already proposed techniques dealing with the generation of sensitivity-aware Pareto fronts, not only in terms of computing time, but also with regards to the number of solutions forming the tradeoff surface. Performances of our approach are highlighted via the design of two analog circuits.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast and Efficient Sensitivity Aware Multi-Objective Optimization of Analog Circuits

et al. 2019

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“…On‐chip spiral inductors have become an essential part of analog and radio frequency (RF)‐integrated circuits, operating at gigahertz frequency levels . Various approaches have been proposed to model, synthesize, and optimize these structures to attain the highest performance.…”

Section: Introductionmentioning

confidence: 99%

“…On-chip spiral inductors have become an essential part of analog and radio frequency (RF)-integrated circuits, operating at gigahertz frequency levels. 1 Various approaches have been proposed to model, synthesize, and optimize these structures to attain the highest performance. [2][3][4][5] The trend of using integrated spiral inductors instead of traditional discrete ones brought about the need for their thorough analysis.…”

Section: Introductionmentioning

confidence: 99%

Surrogate modeling and variability analysis of on‐chip spiral inductors

Akso

Soysal

Yelten

2017

Int J Numerical Modelling

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This paper presents an approach to the analysis of on‐chip integrated spiral inductors in terms of parametric variabilities. An enhanced single‐π model is proposed to compensate for high frequency deviations. In addition to the single‐π model parameters, the skin effect and the proximity effect are included in the surrogate model to reflect high frequency behavior much more accurately while retaining a simple structure. Y‐parameters of the equivalent circuit are derived to extract the frequency‐dependent inductance and quality factor representations using the shunt and differential configurations. This study introduces a novel algorithm able to yield model parameter values through iterations converging on the predetermined inductance curve within an error margin of 5%. High‐Q and low‐Q characteristics of inductors arising from process variations were also captured with the proposed algorithm, along with the failure points of the inductance, the quality factor, and the self‐resonance frequency. Variability analysis results demonstrate that the differential configuration is more robust to inductance and quality factor failures, but also that it is more vulnerable to self‐resonance frequency failures.

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