Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits

Zhang, Zheng; El-Moselhy, Tarek; Maffezzoni, P.; Elfadel, Ibrahim M.; Daniel, Luca

doi:10.1109/tcsii.2013.2278110

Cited by 19 publications

(35 citation statements)

References 15 publications

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“…In the following, an overview of the characteristics of some popular PC-based techniques is given, including spectral projection [13,23], linear regression [23,54,55], Galerkin projection-based approaches [24,56,57], and stochastic testing [38,58,59]. …”

Section: Pc-based Applications In Electronicsmentioning

confidence: 99%

“…• Stochastic Testing in [38,58], an efficient and accurate algorithm, was introduced for the UQ of transistor-level circuits, which has been implemented in a SPICE-type stochastic simulator.…”

mentioning

confidence: 99%

“…Indeed, this method employs an efficient testing node scheme that significantly reduces the required number of samples in comparison to the traditional tensor-product and sparse grid-based methods [22,60]): K = M + 1. Moreover, a decoupling procedure applied inside the intrusive solver makes the stochastic testing method computationally more efficient than Galerkin-based approaches [38,58]. A nonintrusive formulation of the stochastic testing approach has been presented in [59], while in [78], the stochastic testing method is applied to evaluate the performance of a wireless links under the influence of a large number of random variables in a nonintrusive manner.…”

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confidence: 99%

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Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits

2018

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Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standard Monte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developments and challenges in the application of polynomial chaos-based techniques for uncertainty quantification in integrated circuits, with particular focus on high-dimensional problems.

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Section: Pc-based Applications In Electronicsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits

2018

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“…There is a lack of uncertainty quantification techniques for silicon photonics, however some results have been reported recently for nanometer integrated circuits (IC) [9][10][11][12] and microelectromechanical systems (MEMS) [13,14]. The mainstream stochastic simulation technique in commercial design software is Monte Carlo [15].…”

Section: Introductionmentioning

confidence: 99%

“…Based on such expansions, fast stochastic spectral methods have been developed [19][20][21]. Standard spectral methods include an intrusive method we refer to as stochastic Galerkin (SG) [19], a nonintrusive method called stochastic collocation (SC) [20,21], and their hybrid variant called stochastic testing [9][10][11]. In most existing publications [18,[22][23][24][25], the input parameters are assumed to be mutually independent, or Gaussian-correlated, in which case one can convert the random variables to a set of independent ones by a linear transform.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters

Weng¹,

Zhang²,

Su³

et al. 2015

Opt. Express

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Abstract:Process variations can significantly degrade device performance and chip yield in silicon photonics. In order to reduce the design and production costs, it is highly desirable to predict the statistical behavior of a device before the final fabrication. Monte Carlo is the mainstream computational technique used to estimate the uncertainties caused by process variations. However, it is very often too expensive due to its slow convergence rate. Recently, stochastic spectral methods based on polynomial chaos expansions have emerged as a promising alternative, and they have shown significant speedup over Monte Carlo in many engineering problems. The existing literature mostly assumes that the random parameters are mutually independent. However, in practical applications such assumption may not be necessarily accurate. In this paper, we develop an efficient numerical technique based on stochastic collocation to simulate silicon photonics with correlated and non-Gaussian random parameters. The effectiveness of our proposed technique is demonstrated by the simulation results of a silicon-on-insulator based directional coupler example. Since the mathematic formulation in this paper is very generic, our proposed algorithm can be applied to a large class of photonic design cases as well as to many other engineering problems.

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Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles

et al. 2019

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As new services and business models are being associated with the power distribution network, it becomes of great importance to include load uncertainty in predictive computational tools. In this paper, an efficient uncertainty-aware load flow analysis is described which relies on generalized polynomial chaos and stochastic testing methods. It is described how the method can be implemented in order to account for real data-based load profiles due to two different usage models: residential loads and electrical vehicle charging profiles. Hence, it is shown how some relevant information affecting the quality of service can be deduced by means of non-elementary post-processing computations. The proposed technique is tested by using a benchmark scenario for typical European low voltage networks, considering the variation of both residential loads and EV charging profiles. The results are compared with the same simulation done by means of the Monte Carlo methodology. The consideration done during the analysis will be useful to clarify the application of the methodology but also to understand the effect of load variations on the grid characteristic quantities. INDEX TERMS Distribution network, load uncertainty, variability analysis, load forecasting, EV charging profiles, electrical vehicles.

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Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits

Cited by 19 publications

References 15 publications

Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits

Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits

Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters

Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles

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