A5081766741- Probabilistic and Robust Engineering Design
- Electromagnetic Compatibility and Noise Suppression
- Lightning and Electromagnetic Phenomena
- Low-power high-performance VLSI design
- Advanced Multi-Objective Optimization Algorithms
- Particle Detector Development and Performance
- Greenhouse Technology and Climate Control
- Structural Health Monitoring Techniques
- Diamond and Carbon-based Materials Research
- Advancements in Semiconductor Devices and Circuit Design
- Optimal Experimental Design Methods
- stochastic dynamics and bifurcation
- Wind and Air Flow Studies
- Analog and Mixed-Signal Circuit Design
- Acoustic Wave Phenomena Research
- Photonic and Optical Devices
- Radiation Effects in Electronics
- Control Systems and Identification
- Soil Carbon and Nitrogen Dynamics
- Gaussian Processes and Bayesian Inference
- Electromagnetic Simulation and Numerical Methods
- Botany and Plant Ecology Studies
- CCD and CMOS Imaging Sensors
- Scientific Research and Discoveries
- Semiconductor Lasers and Optical Devices
Polytechnic University of Turin
2015-2025
Ghent University
2015-2018
Cadence Design Systems (United States)
2018
Intel (United Kingdom)
2018
iMinds
2015-2017
Istituto Nazionale di Fisica Nucleare, Sezione di Milano
1981-2003
University of Pavia
1990-2001
Lawrence Berkeley National Laboratory
2001
Istituto Nazionale di Fisica Nucleare, Sezione di Pavia
1997
University of Milan
1988
This letter proposes a general and effective decoupled technique for the stochastic simulation of nonlinear circuits via polynomial chaos. According to standard framework, circuit waveforms are still expressed as expansions orthonormal polynomials. However, by using point-matching approach instead traditional Galerkin method, transformation is introduced that renders chaos coefficients therefore obtainable repeated non-intrusive simulations an inverse linear transformation. As discussed...
This paper provides an effective solution for the simulation of cables and interconnects with inclusion effects parameter uncertainties. The problem formulation is based on telegraphers equations stochastic coefficients, whose requires expansion unknown parameters in terms orthogonal polynomials random variables. proposed method offers accuracy improved efficiency computing variability system responses respect to conventional Monte Carlo approach. approach validated against results available...
In this paper, a novel stochastic modeling strategy is constructed that allows assessment of the parameter variability effects induced by manufacturing process on-chip interconnects. The adopts three-step approach. First, very accurate electromagnetic technique yields per unit length (p.u.l.) transmission line parameters interconnect structures. Second, parameterized macromodels these p.u.l. are constructed. Third, Galerkin method implemented to solve pertinent telegrapher's equations. new...
This paper focuses on the derivation of an enhanced transmission-line model allowing stochastic analysis a realistic multiconductor interconnect. The proposed model, which is based expansion well-known telegraph equations in terms orthogonal polynomials, includes variability geometrical or material properties interconnect due to uncertainties like fabrication process temperature. A real application example involving frequency-domain coupled microstrip and computation parameters effects...
The aim of this article is to provide an overview polynomial chaos (PC) based methods for the statistical analysis transmission lines. underlying idea PC represent stochastic line voltages and currents as expansions predefined orthogonal polynomials. determination expansion coefficients allows obtaining pertinent information generally much faster than running, e.g., a Monte Carlo (MC) analysis. There exist several approaches calculate coefficients. briefly reviews virtually all existing...
This paper investigates the application of support vector machine to modeling 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 complex dynamical systems. An identification procedure based observation small set system responses allows generating compact parametric relations, which can be used for optimization stochastic analysis. feasibility...
This paper presents a systematic approach for the statistical simulation of nonlinear networks with uncertain circuit elements. The proposed technique is based on spectral expansions elements' constitutive equations (I-V characteristics) into polynomial chaos series and applies to arbitrary components, both linear nonlinear. By application stochastic Galerkin method, problem cast in terms an augmented set deterministic relating voltage current coefficients. These new are given interpretation...
A thorough investigation has been done about the behavior of natural diamond as a radiation detector material for wide temperature range. Drift velocities and mean free drift time have determined at temperatures ranging between 85 K 700 with electric fields up to 60 KV/cm. Average energy required create an electron-hole pair resolution measured in 100 - 400 interval. The spectroscopic features detectors also analyzed on broad It was found that T approaches 500 polarization phenomena begin,...
This paper presents an alternative modeling strategy for the stochastic analysis of high-speed interconnects. The proposed approach takes advantage polynomial chaos framework and a fully SPICE-compatible formulation to avoid repeated circuit simulations, thereby alleviating computational burden associated with traditional sampling-based methods such as Monte Carlo. Nonetheless, technique offers very good accuracy opportunity easily simulate complex interconnect topologies which include lossy...
This letter presents a novel methodology for the stochastic simulation of interconnects illuminated by random external fields. The proposed strategy is based on polynomial expansion classical forcing terms describing coupling fields onto transmission lines. method turns out to be accurate and much faster than traditional solutions like Monte Carlo (MC) in determining statistical parameters interest. advantages approach are demonstrated means comparisons with MC simulations case incident...
This paper delivers a considerable improvement in the framework of statistical simulation highly nonlinear devices via polynomial chaos-based circuit equivalents. Specifically, far more efficient and "black-box" approach is proposed that reduces model complexity for components. Based on recent literature, "stochastic testing" method used place Galerkin to find pertinent The technique demonstrated analysis low-noise power amplifier its features terms accuracy efficiency are highlighted.
A stochastic modeling method is presented for the analysis of variability effects, induced by manufacturing process, on interconnect structures terminated general nonlinear loads. The technique based solution pertinent Telegrapher's equations in time domain means well-established Galerkin method, but now allows, first literature, inclusion loads with arbitrary I-V characteristics at terminals lines. transient obtained combining a finite-difference time-domain scheme. proposed validated and...
This paper presents an iterative and adaptive perturbation technique for the analysis of nonuniform transmission lines. Place-dependent variations per-unit-length parameters are interpreted as perturbations with respect to their average values along line. allows casting governing equations corresponding voltages currents those a uniform line distributed sources. Therefore, standard theory is used calculate these terms. Specifically, increasing order computed iteratively starting from...
This paper introduces an innovative data-driven approach to uncertainty quantification (UQ) in complex engineering designs based on polynomial chaos expansion (PCE) and least-square support-vector machine (LSSVM). While PCE is a prevalent UQ method, its reliance predefined model form poses limitations the complexity, training accuracy, efficiency high-dimensional settings. To overcome this, we propose nonparametric reformulation that draws equivalence with kernel-based LSSVM. Leveraging...
This paper addresses the generation of an enhanced stochastic model a carbon nanotube interconnect including effects process variation. The proposed approach is based on expansion constitutive relations state-of-the-art deterministic models nanointerconnects with uncertain parameters in terms orthogonal polynomials. method offers comparable accuracy and improved efficiency respect to conventional methods like Monte Carlo predicting statistical behavior electrical performance next-generation...
In this article, a probabilistic machine learning framework based on Gaussian process regression (GPR) and principal component analysis (PCA) is proposed for the uncertainty quantification (UQ) of microwave circuits. As opposed to most surrogate modeling techniques, GPR models inherently carry information model prediction due unseen data. This article shows how inherent pointwise predictions can be combined with design parameters provide global statistical device performance inclusion...