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The design of microwave and millimeter wave devices requires more and more accurate synthesis procedures to satisfy the increasingly stringent specifications of modern communication systems [1]. Most of the available synthesis techniques are based on models that do not conveniently describe the physical behavior of circuits. The goal of the present paper is to calculate the scattering matrix for the case of single and cascaded strip gratings.

The strip gratings allow a complete transmission of certain frequencies and a complete reflection at other frequencies and, then, exists a simple type of frequency selective surface [2]. A wide variety of filter characteristics can be obtained by cascading multiple layers of frequencies-selective surfaces. In this paper, a new method for scattered matrix analysis is presented. This method is based on Floquet harmonics analysis.

This paper summarizes the recent circuit-simulation activities [Roo04]–[Sil06] at Helsinki University of Technology (TKK), Circuit Theory Laboratory (CTL). This paper is mostly based on the results of the national projects Advanced Radio Frequency SImulation and Modeling (ARFSIM 2002–2003) [Roo04], MOdeling and Simulation for Advanced Integrated Circuits and Systems (MOSAICS 2004–2005), and Accurate Models Aim for Zero Errors (AMAZE 2006–2008). All these projects have been funded by the National Technology Agency of Finland, Nokia Corporation, and AWR–APLAC Corporation; the annual volume at TKK CTL has been 4.0–5.5 man years. In these projects, APLAC circuit simulation and design tool [A06] has been used as a common platform for the circuit analysis and modeling methods developed.

With roots dating back to many years ago and applications in a wide variety of areas, model order reduction has emerged in the last few decades as a crucial step in the simulation, control, and optimization of complex physical systems. Reducing the order or dimension of models of such systems, is paramount to enabling their simulation and verification. While much progress has been achieved in the last few years regarding the robustness, efficiency and applicability of these techniques, certain problems of relevance still pose difficulties or renewed challenges that are not satisfactorily solved with the existing approaches. Furthermore, new applications for which dimension reduction is crucial, are becoming increasingly relevant, raising new issues in the quest for increased performance.

We extend the positive real balancing procedure for passive linear systems to the nonlinear systems case. We show that, just like in the linear case, model reduction based on this technique preserves passivity.

The modeling of RF/microwave components for computer-aided design is facing new challenges because of increasing operation frequencies, circuit complexity, integration density, and decreasing time to market. Recently, it has been shown that Artificial Neural Networks (ANNs) offer solutions to urgent modeling problems encountered with conventional numerical methods (e.g., 3-D EM simulation) and empirical models. Fast and accurate models based on ANNs have been created for a wide range of components [ZG00k], [PAR01].

In this paper we extend the Trajectory Piecewise Linear (TPWL) model order reduction (MOR) method for nonlinear differential algebraic equations (DAE). The TPWL method is based on combining several linear reduced models at different time points, which are created along a typical trajectory, to approximate the full nonlinear model. We discuss how to select the linearization tuples for linearization and the choice of linear MOR method. Then we study how to combine the local linearized reduced systems to create a global TPWL model. Finally, we show a numerical result.

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 is implemented in C++ and ROMWorkbench is a MATLAB code.We further compare two Arnoldi-based reduction algorithms, which seems to be the most promising for microsystem design applications. The chosen benchmarks are large scale linear ODE systems, which arise from the finite element discretisation of electro-thermal MEMS models.

Sensitivity analysis is an important tool that can be used to assess and improve the design and accuracy of a model describing an electronic circuit. Given a model description in the form of a set of differential-algebraic equations it is possible to observe how a circuit's output reacts to varying input parameters, which are introduced at the requirements stage of design. In this paper we consider the adjoint method more closely. This method is efficient when the number of parameters is large.We extend the transient sensitivity work of Petzold et al., in particular we take into account the parameter dependency of the dynamic term.We also compare the complexity of the direct and adjoint sensitivity and derive some error estimates. Finally we sketch out how Model Order Reduction techniques could be used to improve the efficiency of adjoint sensitivity analysis.

In this paper we will discuss certain aspects of the transient simulation of electrical circuits. It is a well known problem that DAEs in circuit simulation may possess a higher index (e.g. 2) and thus exhibit undesirable numerical behaviour. While methods for the reduction of the higher index exist, they are usually algebraic in nature. The large size of the systems in VLSI circuit simulation prohibits the use of algebraic methods for index reduction. We will present a topological approach to index reduction that changes certain elements of the circuit netlist to obtain a circuit DAE with usually improved numerical behaviour with respect to workload or accuracy.

Characterization of microstrip discontinuities is an important task in computer aided design (CAD) of microstrip circuits. Many methods for modeling the discontinuities have been developed, such as integral equation [1],[2] spectral domain approach [3], finite difference time domain [4], and finite element method [5].