Search

This article deals with the development of a probabilistic surrogate model for the uncertainty quantification of the voltage output spectral envelope of a power converter with several stochastic parameters. The proposed approach relies on the combination of the least-squares support vector machine (LS-SVM) regression with the Gaussian process regression (GPR), but it can suitably be applied to any deterministic regression techniques. As a first step, the LS-SVM regression is used to build an accurate and fast-to-evaluate deterministic model of the system responses starting from a limited set of training samples provided by the full-computational model. Then the GPR is used to provide a probabilistic model of the regression error. The resulting LS-SVM+GPR probabilistic model not only approximates the system responses for any configuration of its input parameters, but also provides an estimation of its prediction uncertainty, such as the confidence intervals (CIs). The above technique has been applied to qualify the uncertainty of the spectral envelope of the output voltage of a buck converter with 17 independent Gaussian parameters. The feasibility and the accuracy of the resulting model have been investigated by comparing its predictions and CI with the ones obtained by five different surrogate models based on state-of-the-art techniques and by the reference Monte Carlo results. [ABSTRACT FROM AUTHOR]

This paper investigates the application of support vector machine to the modeling of high-speed interconnects with largely varying and/or highly uncertain design parameters. The proposed method relies on a robust and well-established mathematical framework, yielding accurate surrogates of complex dynamical systems. An identification procedure based on the observation of a small set of system responses allows generating compact parametric relations, which can be used for design optimization and/or stochastic analysis. The feasibility and strength of the method are demonstrated based on a benchmark function and on the statistical assessment of a realistic printed circuit board interconnect, highlighting the main features and benefits of this technique over state-of-the-art solutions. Emphasis is given to the effects of the initial sample size and of input noise on the model estimation. [ABSTRACT FROM AUTHOR]

In this paper, we adopt the so-called sparse polynomial chaos metamodel for the uncertainty quantification in the framework of high-dimensional problems. This metamodel is used to model a realistic electronic bus structure with a large number of uncertain input parameters such as those related to microstrip line geometries. It aims at estimating quantities of interest, such as statistical moments, probability density functions, and provides sensitivity analysis of a response. It drastically reduces the model computational cost with regard to brute force Monte Carlo (MC) simulation. The method presents a good performance and is validated in comparison with MC simulation. [ABSTRACT FROM AUTHOR]

The ever-increasing impact of uncertainties in electronic circuits and systems is requiring the development of robust design tools capable of taking this inherent variability into account. Due to the computational inefficiency of repeated design trials, there has been a growing demand for smart simulation tools that can inherently and effectively capture the results of parameter variations on the system responses. To improve product performance, improve yield and reduce design cost, it is particularly relevant for the designer to be able to estimate worst-case responses. Within this framework, the article addresses the worst-case simulation of lumped and distributed electrical circuits. The application of interval-based methods, like interval analysis, Taylor models and affine arithmetic, is discussed and compared. The article reviews in particular the application of the affine arithmetic to complex algebra and fundamental matrix operations for the numerical frequency-domain simulation. A comprehensive and unambiguous discussion appears in fact to be missing in the available literature. The affine arithmetic turns out to be accurate and more efficient than traditional solutions based on Monte Carlo analysis. A selection of relevant examples, ranging from linear lumped circuits to distributed transmission-line structures, is used to illustrate this technique. [ABSTRACT FROM PUBLISHER]

This paper delivers a considerable improvement in the framework of the statistical simulation of highly nonlinear devices via polynomial chaos-based circuit equivalents. Specifically, a far more efficient and “black-box” approach is proposed that reduces the model complexity for nonlinear components. Based on recent literature, the “stochastic testing” method is used in place of a Galerkin approach to find the pertinent circuit equivalents. The technique is demonstrated via the statistical analysis of a low-noise power amplifier and its features in terms of accuracy and efficiency are highlighted. [ABSTRACT FROM AUTHOR]

This paper presents a numerical procedure for the calculation of the polynomial chaos (PC)-expansion coefficients for the per-unit-length capacitance and inductance matrices of wire structures affected by random parameter variability. According to the recent literature results, these coefficients in turn allow the generation of electrical circuit models for stochastic cables. The procedure is based on the twofold expansion of the nonuniform and stochastic charge distributions on the wire boundaries in terms of both Fourier and PC expansions. A stochastic Galerkin method allows to cast the problem in terms of a deterministic system of equations, whose solution provides the unknown coefficients. The proposed methodology is validated via the generation of statistical models for wire structures with random parameters, whose probabilistic responses are compared against the results of Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

This paper presents an alternative modeling strategy for the stochastic analysis of high-speed interconnects. The proposed approach takes advantage of the polynomial chaos framework and a fully SPICE-compatible formulation to avoid repeated circuit simulations, thereby alleviating the computational burden associated with traditional sampling-based methods such as Monte Carlo. Nonetheless, the technique offers very good accuracy and the opportunity to easily simulate complex interconnect topologies which include lossy and dispersive transmission lines, thus overcoming the limitations of previous formulations. Application examples involving the stochastic analysis of on-chip and on-board interconnects validate the methodology proposed. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic modeling method is presented for the analysis of variability effects, induced by the manufacturing process, on interconnect structures terminated by general nonlinear loads. The technique is based on the solution of the pertinent stochastic Telegrapher's equations in time domain by means of the well-established stochastic Galerkin method, but now allows, for the first time in the literature, the inclusion of loads with arbitrary I-V characteristics at the terminals of the lines. The transient solution is obtained by combining the stochastic Galerkin method with a finite-difference time-domain scheme. The proposed technique is validated and illustrated with a meaningful application example, demonstrating its accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the generation of an enhanced stochastic model of a carbon nanotube interconnect including the effects of process variation. The proposed approach is based on the expansion of the constitutive relations of state-of-the-art deterministic models of nanointerconnects with uncertain parameters in terms of orthogonal polynomials. The method offers comparable accuracy and improved efficiency with respect to conventional methods like Monte Carlo in predicting the statistical behavior of the electrical performance of next-generation data links. An application example involving both the frequency- and time-domain analysis of a realistic nanointerconnect concludes this paper. [ABSTRACT FROM AUTHOR]

This paper focuses on the derivation of an enhanced transmission-line model allowing the stochastic analysis of a realistic multiconductor interconnect. The proposed model, which is based on the expansion of the well-known telegraph equations in terms of orthogonal polynomials, includes the variability of geometrical or material properties of the interconnect due to uncertainties like fabrication process or temperature. A real application example involving the frequency-domain analysis of a coupled microstrip and the computation of the parameters variability effects on the transmission-line response concludes this paper. [ABSTRACT FROM PUBLISHER]

This paper provides an effective solution for the simulation of cables and interconnects with the inclusion of the effects of parameter uncertainties. The problem formulation is based on the telegraphers equations with stochastic coefficients, whose solution requires an expansion of the unknown parameters in terms of orthogonal polynomials of random variables. The proposed method offers accuracy and improved efficiency in computing the parameter variability effects on system responses with respect to the conventional Monte Carlo approach. The approach is validated against results available in the literature, and applied to the stochastic analysis of a commercial multiconductor flat cable. [ABSTRACT FROM AUTHOR]

This letter proposes a general and effective decoupled technique for the stochastic simulation of nonlinear circuits via polynomial chaos. According to the standard framework, stochastic circuit waveforms are still expressed as expansions of orthonormal polynomials. However, by using a point-matching approach instead of the traditional stochastic Galerkin method, a transformation is introduced that renders the polynomial chaos coefficients decoupled and therefore obtainable via repeated non-intrusive simulations and an inverse linear transformation. As discussed throughout the letter, the proposed technique overcomes several limitations of state-of-the-art methods. In particular, the scalability is hugely improved and tens of random parameters can be simultaneously treated within the polynomial chaos framework. Validating application examples are provided that concern the statistical analysis of microwave amplifiers with up to 25 random parameters. [ABSTRACT FROM PUBLISHER]

Recently, an efficient stochastic modeling method for interconnects with inherent variability in their physical parameters was proposed, based on applying the so-called polynomial chaos (PC) approach in conjunction with a Stochastic Galerkin Method (SGM) onto telegrapher's equations. Although this approach was already very successful from a numerical point of view, the novel technique could not be conveniently integrated into SPICE-like solvers, limiting the applicability of the method. In this letter, the PC-SGM scheme for telegrapher's equations is revisited, pinpointing the origin of this inconvenience and immediately allowing to mitigate the issue. By adapting the traditional discretization of the stochastic telegrapher's equations approach, an augmented, yet deterministic, set of ordinary differential equations is obtained that turns out to be of the same type as the telegrapher's equations, and hence, the physical property of reciprocity is preserved. Consequently, it can be directly and more efficiently handled using SPICE-like solvers, which usually assume matrix symmetries. As an application example, the variability analysis of a state-of-the-art on-chip line for millimeter-wave applications is performed in a SPICE solver. [ABSTRACT FROM PUBLISHER]