Stochastic testing method for transistor-level uncertainty quantification based on generalized polynomial chaos
Uncertainties have become a major concern in integrated circuit design. In order to avoid the
huge number of repeated simulations in conventional Monte Carlo flows, this paper presents …
huge number of repeated simulations in conventional Monte Carlo flows, this paper presents …
Enabling high-dimensional hierarchical uncertainty quantification by ANOVA and tensor-train decomposition
Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit
simulation by employing spectral methods at different levels. This paper presents an efficient …
simulation by employing spectral methods at different levels. This paper presents an efficient …
Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters
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 …
photonics. In order to reduce the design and production costs, it is highly desirable to predict …
Calculation of generalized polynomial-chaos basis functions and Gauss quadrature rules in hierarchical uncertainty quantification
Stochastic spectral methods are efficient techniques for uncertainty quantification. Recently
they have shown excellent performance in the statistical analysis of integrated circuits. In …
they have shown excellent performance in the statistical analysis of integrated circuits. In …
Accelerated probabilistic power flow in electrical distribution networks via model order reduction and Neumann series expansion
S Chevalier, L Schenato… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This paper develops a computationally efficient algorithm which speeds up the probabilistic
power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile …
power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile …
Stochastic testing simulator for integrated circuits and MEMS: Hierarchical and sparse techniques
Process variations are a major concern in today's chip design since they can significantly
degrade chip performance. To predict such degradation, existing circuit and MEMS …
degrade chip performance. To predict such degradation, existing circuit and MEMS …
Scattering from two-dimensional objects of varying shape combining the method of moments with the stochastic Galerkin method
In this communication, the combined field integral equation for perfect electrically conducting
scatterers is combined with the stochastic Galerkin method (SGM) to model the impact of …
scatterers is combined with the stochastic Galerkin method (SGM) to model the impact of …
Scattering from two-dimensional objects of varying shape combining the multilevel fast multipole method (MLFMM) with the stochastic Galerkin method (SGM)
In this letter, the Multilevel Fast Multipole Method (MLFMM) is combined with the polynomial
chaos expansion (PCE) approach to model the stochastic variations of a scatterer. In …
chaos expansion (PCE) approach to model the stochastic variations of a scatterer. In …
High-accuracy parasitic extraction
M Kamon, R Iverson - EDA for IC implementation, circuit design …, 2018 - taylorfrancis.com
In this chapter, we describe high-accuracy parasitic extraction by both fast integral equation
methods as well as random-walk-based methods. For any extraction application, the …
methods as well as random-walk-based methods. For any extraction application, the …
[图书][B] Variational formulations and functional approximation algorithms in stochastic plasticity of materials
B Rosić - 2013 - search.proquest.com
A class of abstract stochastic variational inequalities of the second kind described by
uncertain parameters is considered within the framework of infinitesimal and large …
uncertain parameters is considered within the framework of infinitesimal and large …