Model order reduction techniques for circuit simulation software

Structure preserving model order reduction of a class of. As such it is closely related to the concept of metamodeling with applications in all areas of mathematical modelling. Model order reduction techniques for circuit simulation. A comparison of some model order reduction methods for fast. Existing model order reduction techniques 1, however, stamp. This increases development time and costs for the design of new circuits. Due to its highly accurate modeling capability, many colleges and universities use this type of software for the teaching of. Model order reduction is a set of techniques which are focused on reducing the number of degrees of freedom. Simulation software allows for modeling of circuit operation and is an invaluable analysis tool.

Model order reduction of nonlinear systems in circuit. Schematic of the pcmfs for dynamic surgery simulation. Preprints publications editorial boards cooperations projects software. Simulation, controller design and design optimization are some applications of such models. Modern model order reduction mor techniques present a way out of this dilemma in providing surrogate models which keep the main characteristics of the.

Large systems of nonlinear daes, one per block spice. Model order reduction o ers new simulation alternatives by circumventing, or at least alleviating, otherwise intractable computational challenges. By a reduction of the models associated state space dimension or degrees of freedom, an approximation to the original model is computed which is commonly referred to as a reduced order model. Model order reduction for electronic circuits mathematics in industry. Finally, the impulse response can be efficiently incorporated into a circuit simulator using recursive convolution. Submitted to the department of electrical engineering and computer science on may 16, 1994, in partial fulfillment of the requirements for the degree of doctor of philosophy. Our research group uses several engineering design applications to drive research in simulation and optimization algorithms and software. The system is represented by its partial elements equivalent. Sparsi cation and model order reduction mor are hence both needed to reduce a large scale rlc circuit. Circuit simulation on a computer must be e cient in terms of cpu time especially for large circuits. Research council of canada discovery grant program and in part by the. Model order reduction allows to speed up simulations in cases where one is. A design automation tool for integrated nanophotonics. A matlab toolbox for teaching model order reduction.

Model order reduction techniques for circuit simulation request. Model order reduction techniques with applications in. Theoretical and practical aspects of model order reduction techniques for use in the context of circuit simulation are investigated, with particular attention to problems involving clocked analog circuitry and to interconnect and packaging applications. Readers will gain indepth knowledge of machine learning and model order reduction concepts, the tradeoffs involved with using various algorithms, and how to apply the techniques presented to circuit simulations and numerical analysis. Free circuit simulatorcircuit design and simulation software. Simplified techniques of model order reduction with. Model order reduction for circuit applications joel phillips cadence design systems. The basic idea of model order reduction of a circuit system is to replace the original system by an approximating system with much smaller statespace dimension. As such they are an essential ingredient for the uncertainty quantification.

Chapter 2 elementbased model reduction in circuit simulation. Despite that, two main different groups can be distinguished. Circuitlab provides online, inbrowser tools for schematic capture and circuit simulation. Enhancing model order reduction for nonlinear analog circuits simulation henda aridhi, mohamed h. Model order reduction techniques for circuit simulation by luis miguel silveira submitted to the department of electrical engineering and computer science on may 16, 1994, in partial fulfillment of the requirements for the degree of doctor of philosophy abstract. This approach, which is also observed in different application areas, is also termed operational model order reduction. To do this, our opensource software mor4ansys performs model order reductions via the block arnoldi algorithm directly to ansys. What is the best free electronicselectrical circuits.

Parametric modeling and model order reduction for electro. The course further covers some of the most recent modeling and simulation work including, but not limited to, model order reduction, harmonic balance methods, nonlinear macromodeling, compact device modeling, and statistical timing analysis. Popular software packages such as matlab, maple, mathematica, and pspice are essential tools for. A fast block structure preserving model order reduction for.

Aug 27, 2008 during his phd studies he worked on eigensolution methods with applications in model order reduction. Electronic circuit simulation uses mathematical models to replicate the behavior of an actual electronic device or circuit. Academics, students and industry specialists around the globe use this free simulation software to learn, teach, and explore the world of simulation. Compared to the dense partial inductance matrix l, l 1 matrix is easier to sparsify 3,4, where l 1 elements are related to the drop of the branch vector potential 4. Model order reduction for nonlinear problems in circuit. We discuss the computational cost with examples related to structural. One of the most used model reduction techniques in circuit simulation is.

Finally, the use of circuit simulation algorithms for non circuit problems will be explored. A trajectory piecewiselinear approach to model order. Passivity assessment and model order reduction for linear. Model order reduction techniques for circuit simulation by luis miguel silveira. Machine learning for model order reduction springer for. Reducedorder modeling rom for simulation and optimization. Novel model order reduction techniques, developed here for the fast repeated simulation of the coupled problems under consideration, are applicable to both coupled systems and parameterized subsystems. In this paper we study the accuracy and efficiency of digital system interconnects analysis using model order reduction mor methods. Ansys mor techniques model order reduction mor for linear problems transfer function based or lti method system matrix based. Elementbased model reduction in circuit simulation university of. Traditional simulation programs, which must accurately solve the associated. Joost rommes currently works at nxp semiconductors on model order reduction. An efficient model order reduction method for threedimensional fem analysis of waveguide structures has been proposed. Interpolation method for adapting reducedorder models and.

To help students better understand and utilize todays circuit modeling and simulation techniques, the topics of this course will include. Model reduction for circuit simulation also reflects and documents the vivid interaction between three active research projects in this area, namely the eumarie curie action tok project omoorenice members in belgium, the netherlands and germany, the eumarie curie action rtnproject comson members in the netherlands, italy, germany, and romania, and the german federal project system. Model reduction for circuit simulation peter benner. In the electronics industry, an increase in complexity at transistor level leads to much large models that can not be simulated without accurate reduction techniques. Model order reduction is an approach used to reduce the computational. Mor techniques for linear systems are now very wellestablished and are widely. Eng in 2008, department of electronic science and technology huazhong university of science and technology, p. Computer methods for circuit analysis and design guide books. Passivity assessment and model order reduction for linear timeinvariant descriptor systems in vlsi circuit simulation by zheng zhang b.

In this work, we discuss the parametric modeling for the electrothermal analysis of components of nanoelectronic structures and automatic model order reduction of the consequent parametric models. During his phd studies he worked on eigensolution methods with applications in model order reduction. Prime members enjoy free twoday delivery and exclusive access to music, movies, tv shows, original audio series, and kindle books. With model reduction for circuit simulation we survey the state of the art in the challenging research field of.

If you want to view papers or slides you must allow popups for this site. It uses physical or other insight to reduce the complexity of models. Time and frequencydomain model order reduction techniques, system identification, parameter estimation, filtering, and control theory will be covered and applied to state of charge, state. Our experimental results show that the reduced models lead to an improvement in simulation speed while providing the same behavior of the original circuit design. An efficient and compact multipleparameterized model order reduction method for physical optimization, ieee transactions on very large scale integration vlsi systems, volume 18, issue 11, jan. Model order reduction techniques for circuits and interconnects simulation em lab technical report no. Review of model order reduction methods for numerical. It is solved in time domain by multirate techniques that efficiently exploit the. Readers will gain indepth knowledge of machine learning and model order reduction concepts, the tradeoffs involved with using various algorithms, and how to apply the techniques presented to circuit simulations and. Design and real time software intheloop simulation of robustoptimal speed controller for vector controlled induction machine. An aggregating based model order reduction method for. Ionutiu 2011 model order reduction and sensitivity analysis.

First, an algorithm for the efficient simulation of clocked. Between 1983 and 2010 he worked at philips and at nxp semiconductors eindhoven, the netherlands on simulation techniques for coupled problems, including circuit simulation and model order reduction. Mecheng 565 battery systems and control isd integrative. White, a linear timeinvariant model for solidphase diffusion in.

Abstractmodel order reduction mor techniques play a crucial role in. This increased the need for circuit simulators to evaluate potential. Nanoelectronic coupled problems solutions nanocops. Sumo lab focuses on the study and development of innovative software tools and techniques for efficient and accurate characterization, modeling, simulation and optimization of complex systems, with applications in different fields of science and engineering.

Digital system interconnects analysis using model order. These tools allow students, hobbyists, and professional engineers to design and analyze analog and digital systems before ever building a prototype. Recent advances in model order reduction mor simulations. One of the most used model reduction techniques in circuit simulation is moment matching approximation. In the present chapter we revisit three of these model reduction techniques. Model order reduction mor techniques, which are able to reduce the size of a dynamical system description, were recently applied to nd smaller and faster models of electronic circuits 2, 3. Comparison of model order reduction methodologies for thermal problems m. Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Lutowska 2012 model order reduction for multiterminal systems with applications to circuit simulation. These tasks become challenging when the dimension of the system is high.

Model order reduction techniques with applications in finite element analysis. Model order reduction for coupled systems using lowrank approximationsa. Recent efforts have focused on the fundamentals of model order reduction, matriximplicit methods, and fast techniques for solving integral equations. Model order reduction mor is a technique for reducing the computational complexity of mathematical models in numerical simulations. Simulation of the resulting partial differential equations using various popular software tools will be introduced with selected topics on numerical issues. Model order reduction methods have been developed for large scale dynamical. Casa model order reduction for multiterminal systems with. Wendem tsegaye beyene engineer in programmable hardware. Due to its highly accurate modeling capability, many colleges and universities use this type of software for the teaching of electronics technician and electronics engineering. Comparison of model order reduction methodologies for. May 02, 2012 model order reduction for dynamic simulation of a flexible multibody system via absolute nodal coordinate formulation computer methods in applied mechanics and engineering, vol. Anylogic ple is a free simulation tool for the purposes of education and selfeducation. Gunupudi p and nakhla m model reduction of nonlinear circuits using krylovspace techniques proceedings of the 36th annual acmieee design automation conference, 16 yang z and zwolinski m fast, robust dc and transient fault simulation for nonlinear analogue circuits proceedings of the conference on design, automation and test in europe. Introduction to model order reduction virginia tech.

Model order reduction techniques have been studied by several authors as these techniques offer a method to reduce the number of degrees of freedom while an accurate description of the dominant dynamic behaviour may be preserved. List of circuit design analysis simulation software. This method is called model order reduction mor, which reduces the complexity of the original large system and generates a reducedorder model rom to represent the original one. Model order reduction for nonlinear problems in circuit simulation a. This paper investigates two alternative, simple model order reduction techniques and argues that the ready availability of digital simulation languages to test the time frequency response characteristics of pro.

Especially when model reduction exploits structure preservation, we show that using the model as a currentdriven element is possible, and allows for synthesis. Model order reduction aims to lower the computational complexity of such problems, for example, in simulations of largescale dynamical systems and control systems. Reduced order model validation even the 3 rd order model gives good accuracy. Indeed, there is a huge variety of techniques and different points of view to face this issue. The implemented algorithms in this tool are bordered blockdiagonal matrix solver and bordered blockdiagonal matrix based block arnoldi method. Efficient model order reduction for fem analysis of waveguide.

The basic goal of this dissertation is to extend the use of order reduction techniques as accurate methods for analyzing complex electronic systems. Modern model order reduction mor techniques present a way out of this dilemma in providing surrogate models which keep the main characteristics of the device while requiring a significantly lower simulation time than the full model. Many different model reduction approaches have been developed in computational. Model order reduction using spice simulation traces. The theoretical and practical aspects of momentmatching, krylov subspacebased methods, and rational approximations techniques are studied. This paper discusses a method to obtain a reduced second order model from a large sparse second order index3 system using the balanced truncation. We also go into somewhat more detail about the question as to what model order reduction is. Enhancing model order reduction for nonlinear analog.

A matlab toolbox for teaching model order reduction techniques authors. Model order reduction and controller design techniques. Consisting of the linear reduced order subcircuits and the rest nonlinear components, the overall reduced order circuit models can then be reused in multimode or multicorner context to accelerate simulation. Citeseerx model order reduction techniques for circuit. Model order reduction of nonlinear systems in circuit simulation. Model reduction for circuit simulation peter benner springer. This leads to very high dimensional problems which nowadays require simulation times too. Introduction the modeling of complex dynamic systems is one of the most important subjects in engineering. A framework for model reduction and synthesis is presented, which greatly enlarges the options for the reuse of reduced order models in circuit simulation by simulators of choice.

Given the system matrices at different values of the parameters, we introduce a simple method of extracting system matrices which are independent of the parameters, so that. Given the system matrices at different values of the parameters, we introduce a simple method of extracting system matrices which are independent of the parameters, so that parametric models of a. We present the software mor4ansys that allows engineers to employ modern model reduction techniques to finite element models developed in ansys. We study and develop efficient, numerically stable, fully automated model order reduction mor and parameterized model order reduction pmor techniques, which ensure good approximation of the original system, and preserve as much as possible the physical properties of the underlying system such as causality, stability and passivity.

Efficient model order reduction of electrical networks with. In this report, we focus on the development of mathematical and physical model order reduction techniques and corresponding theory for the simulation of pcbs and interconnects. What is the best online circuit simulator for a beginner. Some of his developed methods are now used in software for circuit simulation and power system analysis. Model order reduction mor techniques can efficiently deal with the aforementioned challenges by reducing the size of linear subcircuits. New techniques are needed to shorten timetomarket and to reduce the cost of producing a correct analog integrated circuit. The computational domain is divided into cascaded subdomains, in which model order reduction is performed locally by means of the enor algorithm. Traditional simulation programs, which must ac curately solve the associated differential equations with a time discretization method become extraordinarily. Thanks to model order reduction, you can now deploy models beyond simulation specialists to the engineering and manufacturing departments, and following the product inservice.

Model reduction for circuit simulation springerlink. Model order reduction numerische mathematik university of. Hello friends, i hope you all got benefited with our previous article on electronic circuit drawing softwares today we are bringing you a great collection of circuit simulators which are at the same time can be used for circuit drawing, circuit design and analysis as well. Efficient optimization of transient dynamic problems in mems. Model order reduction for large scale engineering models. Reduced models will allow you to create a new simulation experience through the coupling of system models with finite element models, but also multiscale and multi. Once the reduced order model is derived, an impulse response can easily be generated. We discuss the computational cost with examples related to structural mechanics.