Computational Prototyping Tools and Techniques

Contains reports on five research projects.Industry Consortium (Mobil, Statoil, DNV Software, Shell, OTRC, Petrobras, NorskHydro, Exxon, Chevron, SAGA, NSWC)U.S. Navy - Office of Naval ResearchAnalog DevicesDefense Advanced Research Projects Agency Contract J-FBI-95-215Cadence Design SystemsHarris SemiconductorMAFET ConsortiumMotorola SemiconductorDefense Advanced Research Projects AgencyMultiuniversity Research InitiativeSemiconductor Research CorporationIBM Corporatio

Custom Integrated Circuits

Contains table of contents for Part III, table of contents for Section 1 and reports on eleven research projects.IBM CorporationMIT School of EngineeringNational Science Foundation Grant MIP 94-23221Defense Advanced Research Projects Agency/U.S. Army Intelligence Center Contract DABT63-94-C-0053Mitsubishi CorporationNational Science Foundation Young Investigator Award Fellowship MIP 92-58376Joint Industry Program on Offshore Structure AnalysisAnalog DevicesDefense Advanced Research Projects AgencyCadence Design SystemsMAFET ConsortiumConsortium for Superconducting ElectronicsNational Defense Science and Engineering Graduate FellowshipDigital Equipment CorporationMIT Lincoln LaboratorySemiconductor Research CorporationMultiuniversity Research IntiativeNational Science Foundatio

Table of Contents

Contains the table of contents

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

This brief proposes an uncertainty quantification method for the periodic steady-state (PSS) analysis with both Gaussian and non-Gaussian variations. Our stochastic testing formulation for the PSS problem provides superior efficiency over both Monte Carlo methods and existing spectral methods. The numerical implementation of a stochastic shooting Newton solver is presented for both forced and autonomous circuits. Simulation results on some analog/RF circuits are reported to show the effectiveness of our proposed algorithms

Weakly nonlinear circuit analysis based on fast multidimensional inverse Laplace transform

There have been continuing thrusts in developing efficient modeling techniques for circuit simulation. However, most circuit simulation methods are time-domain solvers. In this paper we propose a frequency-domain simulation method based on Laguerre function expansion. The proposed method handles both linear and nonlinear circuits. The Laguerre method can invert multidimensional Laplace transform efficiently with a high accuracy, which is a key step of the proposed method. Besides, an adaptive mesh refinement (AMR) technique is developed and its parallel implementation is introduced to speed up the computation. Numerical examples show that our proposed method can accurately simulate large circuits while enjoying low computation complexity. © 2012 IEEE.published_or_final_versio

A novel linear algebra method for the determination of periodic steady states of nonlinear oscillators

Periodic steady-state (PSS) analysis of nonlinear oscillators has always been a challenging task in circuit simulation. We present a new way that uses numerical linear algebra to identify the PSS(s) of nonlinear circuits. The method works for both autonomous and excited systems. Using the harmonic balancing method, the solution of a nonlinear circuit can be represented by a system of multivariate polynomials. Then, a Macaulay matrix based root-finder is used to compute the Fourier series coefficients. The method avoids the difficult initial guess problem of existing numerical approaches. Numerical examples show the accuracy and feasibility over existing methods. © 2014 IEEE.postprin

Uncertainty quantification for integrated circuits: Stochastic spectral methods

Due to significant manufacturing process variations, the performance of integrated circuits (ICs) has become increasingly uncertain. Such uncertainties must be carefully quantified with efficient stochastic circuit simulators. This paper discusses the recent advances of stochastic spectral circuit simulators based on generalized polynomial chaos (gPC). Such techniques can handle both Gaussian and non-Gaussian random parameters, showing remarkable speedup over Monte Carlo for circuits with a small or medium number of parameters. We focus on the recently developed stochastic testing and the application of conventional stochastic Galerkin and stochastic collocation schemes to nonlinear circuit problems. The uncertainty quantification algorithms for static, transient and periodic steady-state simulations are presented along with some practical simulation results. Some open problems in this field are discussed.MIT Masdar Program (196F/002/707/102f/70/9374

Autonomous Volterra algorithm for steady-state analysis of nonlinear circuits

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