Stochastic Analysis of Deep-Submicrometer CMOS Process for Reliable Circuits Designs

Zjajo, Amir; Tang, Qin; Berkelaar, M.R.C.M.; Gyvez, José Pineda de; Bucchianico, A.A. Di; Meijs, Nick van der

doi:10.1109/tcsi.2010.2055291

Cited by 23 publications

(6 citation statements)

References 43 publications

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“…It should be mentioned here that (71) and (72) have been derived by keeping in mind that both η(t) and κ(t) employ zero mean. By using (70)-(72), we have found that R I1,I2 (t) = R x1,x2 [7,7] and R I1,I2 (t) = R x1,x2 for the on-chip and off-chip transformer respectively. In addition, all expectation operator termed elements of R x1,x2 can be analytically given in terms of the correlation coefficients.…”

Section: The Vector Sdes Of a Trans-former Their Solutions And The Smentioning

confidence: 99%

“…Since the variance of any random variable can be given by its mean square subtracted by the square of its mean, the variances of I 1 (t) and I 2 (t) of the on-chip transformer are respectively equal to V 1 [7,7] and V 2 [7,7].…”

Section: The Vector Sdes Of a Trans-former Their Solutions And The Smentioning

confidence: 99%

“…As a result, P 1 (t) and P 2 (t) of the for the on-chip transformer can be given by (88) and (89), thus the second moment of I 1 (t) and I 2 (t) of the on-chip transformer are respectively equal to P 1 [7,7] and P 2 [7,7]. In addition, P 1 (t) and P 2 (t) of the off-chip transformer can be given by (90) and (91) therefore the second moment of I 1 (t) and I 2 (t) of the off-chip transformer are respectively equal to P 1 [5,5] and P 2 [5,5].…”

Section: The Vector Sdes Of a Trans-former Their Solutions And The Smentioning

confidence: 99%

“…In electrical and electronic engineering, the SDE covers various stochastic variations which occur in the systems as can be seen from many previous research [4]- [7]. Many previous studies have focused on the eects of stochastic variations in conventional passive electrical circuits which are uncoupled [8]- [13].…”

Section: Introductionmentioning

confidence: 99%

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Vector SDE Based Stochastic Analysis of Transformer

Banchuin

Chaisricharoen

2021

ECTI-CIT

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In this research, the stochastic behaviours oftransformer have been analysed by using the stochasticdifferential equation approach where both noise in thevoltage source applied to the transformer and the randomvariations in elements and parameters of transformers havebeen considered. The resulting vector stochasticdifferential equations of the transformer have been bothanalytically and numerically solved in the Ito sense wherethe Euler-Maruyama scheme has been adopted fordetermining the numerical solutions which have been theirsample means have been used for verification. With theobtained analytical and numerical solutions, the stochasticproperties of the transformer’s electrical quantities havebeen studied and the influences of noise in the voltagesource and random variations in elements and parametersof transformers to those electrical quantities have beenanalysed. The causes of high and low frequency stochasticvariations of such electrical quantities in both transient andsteady state have been pointed out. Moreover, furtherextension of our stochastic differential equations and therelated mathematical formulations has also been given.

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Section: The Vector Sdes Of a Trans-former Their Solutions And The Smentioning

confidence: 99%

Section: The Vector Sdes Of a Trans-former Their Solutions And The Smentioning

confidence: 99%

Section: The Vector Sdes Of a Trans-former Their Solutions And The Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vector SDE Based Stochastic Analysis of Transformer

Banchuin

Chaisricharoen

2021

ECTI-CIT

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“…1–3 As for electrical engineering, the SDE approach has been mostly used to solve noisy issues in electronic circuits with lumped parameters, including monolithic integrated circuits. 4–11 The present paper applies SDE theory to networks with distributed parameters, and transmission lines (TL) in particular, which are very often utilized for high-speed data transmission. 12–14 To catch effects of random variations of system parameters, standard methods like Monte Carlo are commonly utilized.…”

Section: Introductionmentioning

confidence: 99%

Simulation of multiconductor transmission lines with random parameters via stochastic differential equations approach

Brančík

Kolarova

2016

SIMULATION

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This article addresses a method for the simulation of multiconductor transmission lines (MTLs) with fluctuating parameters based on the theory of stochastic differential equations (SDEs). Specifically, confidence intervals of an MTL model's stochastic responses are effectively evaluated. First, the MTL's deterministic model with lumped parameters, based on generalized P sections connected in cascade, is formulated and described through a state variable method, which results in a vector ordinary differential equation (ODE) in the time domain. A vector SDE is then developed by incorporating the respective stochastic processes into its deterministic counterpart. Next, the first two moments of the stochastic processes are calculated via the solution of respective Lyapunov-like ODEs, to assess expectations and the variances of stochastic responses, and also to determine relevant confidence intervals. A statistical processing of individual stochastic trajectories is used to validate the results.

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A Hardware Circuit for Extracting Weak Damage Characteristics of Steel Wire Rope Using an Asymmetric Monostable System

Huang,

Liu,

et al. 2024

Circuit Theory & Apps

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Non‐destructive testing of steel wire rope is significant in lifting machinery. However, it is still challenging to extract weak damage characteristics of steel wire rope under noise caused by harsh working conditions. Currently, extracting damage characteristics mostly involves secondary processing through software, which is cumbersome and inefficient. Stochastic resonance is a typical stochastic dynamic phenomenon of a nonlinear system, and it can extract weak damage characteristics through the cooperation of noise, weak signals, and nonlinear system models. Among different nonlinear system models, the asymmetric monostable system, which has only one fixed point, is advantageous for achieving a more stable response under pulse excitation. In this work, a hardware circuit based on an asymmetric monostable system is constructed to extract weak damage characteristics of steel wire rope. This hardware circuit can efficiently extract damage characteristics without secondary processing. Additionally, the denoising performance of the nonlinear circuit, focusing on weak signals with damage characteristics under a noisy background, is evaluated using the cross‐correlation coefficient and local cross‐correlation coefficient. The results demonstrate that the proposed method provides high recovery in the non‐destructive testing of steel wire rope.

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Stochastic Analysis of Deep-Submicrometer CMOS Process for Reliable Circuits Designs

Cited by 23 publications

References 43 publications

Vector SDE Based Stochastic Analysis of Transformer

Vector SDE Based Stochastic Analysis of Transformer

Simulation of multiconductor transmission lines with random parameters via stochastic differential equations approach

A Hardware Circuit for Extracting Weak Damage Characteristics of Steel Wire Rope Using an Asymmetric Monostable System

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