DRK methods for time-domain oscillator simulation

This paper presents a new Runge-Kutta type integration method that is well-suited for time-domain simulation of oscillators. A unique property of the new method is that its damping characteristics can be controlled by a continuous parameter

Model order reduction for nonlinear IC models with POD

Due to refined modelling of semiconductor devices and increasing packing densities, reduced order modelling of large nonlinear systems is of great importance in the design of integrated circuits (ICs). Despite the linear case, methodologies for nonlinear problems are only beginning to develop. The most practical approaches rely either on linearisation, making techniques from linear model order reduction applicable, or on proper orthogonal decomposition (POD), preserving the nonlinear characteristic. In this paper we focus on POD. We demonstrate the missing point estimation and propose a new adaption of POD to reduce both dimension of the problemunder consideration and cost for evaluating the full nonlinear system

Sensitivity analysis of linear dynamical systems in uncertainty quantification

We consider linear dynamical systems including random parameters for uncertainty quantification. A sensitivity analysis of the stochastic model is applied to the input-output behaviour of the systems. Thus the parameters that contribute most to the variance are detected. Both intrusive and non-intrusive methods based on the polynomial chaos yield the required sensitivity coefficients. We use this approach to analyse a test example from electrical engineering

Simulation of mutually coupled oscillators using nonlinear phase macromodels

Design of integrated RF circuits requires detailed insight in the behavior of the used components. Unintended coupling and perturbation effects need to be accounted for before production, but full simulation of these effects can be expensive or infeasible. In this paper we present a method to build nonlinear phase macromodels of voltage controlled oscillators. These models can be used to accurately predict the behavior of individual and mutually coupled oscillators under perturbation at a lower cost than full circuit simulations. The approach is illustrated by numerical experiments with realistic designs

Simulation of mutually coupled oscillators using nonlinear phase macromodels

Design of integrated RF circuits requires detailed insight in the behavior of the used components. Unintended coupling and perturbation effects need to be accounted for before production, but full simulation of these effects can be expensive or infeasible. In this paper we present a method to build nonlinear phase macromodels of voltage controlled oscillators. These models can be used to accurately predict the behavior of individual and mutually coupled oscillators under perturbation at a lower cost than full circuit simulations. The approach is illustrated by numerical experiments with realistic designs

Digital linear control theory applied to automatic stepsize control in electrical circuit simulation

Adaptive stepsize control is used to control the local errors of the numerical solution. For optimization purposes smoother stepsize controllers are wanted, such that the errors and stepsizes also behave smoothly. We consider approaches from digital linear control theory applied to multistep BDF-methods

Model order reduction of large scale ODE systems : MOR for ANSYS versus ROM workbench

In this paper we compare the numerical results obtained by different model order reduction software tools, in order to test their scalability for relevant problems of the microelectronic-industry. MOR for ANSYS [2J is implemented in C++ and ROM Workbench [3J is a MATLAB code. The chosen benchmarks are large scale linear ODE systems, which arise from the finite element discretisation of electro-thermal MEMS models

Efficient calculation of uncertainty quantification

We consider Uncertainty Quanti¿cation (UQ) by expanding the solution in so-called generalized Polynomial Chaos expansions. In these expansions the solution is decomposed into a series with orthogonal polynomials in which the parameter dependency becomes an argument of the orthogonal polynomial basis functions. The time and space dependency remains in the coef¿cients. In UQ two main approaches are in use: Stochastic Collocation (SC) and Stochastic Galerkin (SG). Practice shows that in many cases SC is more ef¿cient for similar accuracy as obtained by SG. In SC the coef¿cients in the expansion are approximated by quadrature and thus lead to a large series of deterministic simulations for several parameters. We consider strategies to ef¿ciently perform this series of deterministic simulations within SC