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Model Reduction for Circuit Simulation

Simulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. This leads to very high dimensional problems which nowadays require simulation times too large for the short time-to-market demands in industry. 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.With Model Reduction for Circuit Simulation we survey the state of the art in the challenging research field of MOR for ICs, and also address its future research directions. Special emphasis is taken on aspects stemming from miniturisations to the nano scale. Contributions cover complexity reduction using e.g., balanced truncation, Krylov-techniques or POD approaches. For semiconductor applications a focus is on generalising current techniques to differential-algebraic equations, on including design parameters, on preserving stability, and on including nonlinearity by means of piecewise linearisations along solution trajectories (TPWL) and interpolation techniques for nonlinear parts. Furthermore the influence of interconnects and power grids on the physical properties of the device is considered, and also top-down system design approaches in which detailed block descriptions are combined with behavioral models. Further topics consider MOR and the combination of approaches from optimisation and statistics, and the inclusion of PDE models with emphasis on MOR for the resulting partial differential algebraic systems. The methods which currently are being developed have also relevance in other application areas such as mechanical multibody systems, and systems arising in chemistry and to biology.The current number of books in the area of MOR for ICs is very limited, so that this volume helps to fill a gap in providing the state of the art material, and to stimulate further research in this area of MOR. Model Reduction for Circuit Simulation also reflects and documents the vivid interaction between three active research projects in this area, namely the EU-Marie Curie Action ToK project O-MOORE-NICE (members in Belgium, The Netherlands and Germany), the EU-Marie Curie Action RTN-project COMSON (members in The Netherlands, Italy, Germany, and Romania), and the German federal project System reduction in nano-electronics (SyreNe).

Informations détaillées sur le livre - Model Reduction for Circuit Simulation


EAN (ISBN-13): 9789400700888
ISBN (ISBN-10): 9400700881
Version reliée
Date de parution: 2011
Editeur: Springer
315 Pages
Poids: 0,627 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2009-09-01T10:39:33+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-02-22T00:02:14+01:00 (Zurich)
ISBN/EAN: 9789400700888

ISBN - Autres types d'écriture:
94-007-0088-1, 978-94-007-0088-8
Autres types d'écriture et termes associés:
Auteur du livre: peter hinze, benner, michael hinz, michael jan, modern project limited, ter
Titre du livre: around the circuit


Données de l'éditeur

Auteur: Peter Benner; Michael Hinze; E. Jan W. ter Maten
Titre: Lecture Notes in Electrical Engineering; Model Reduction for Circuit Simulation
Editeur: Springer; Springer Netherland
315 Pages
Date de parution: 2011-03-26
Dordrecht; NL
Imprimé / Fabriqué en
Poids: 0,664 kg
Langue: Anglais
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
POD
XIII, 315 p.

BB; Circuits and Systems; Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik; Schaltkreise und Komponenten (Bauteile); Verstehen; Krylov subspace methods; Models-Order-Reduction (MOR); integrated circuit design; model order reduction; nanotechnology; proper orthogonal decomposition; Mathematical and Computational Engineering; Systems Theory, Control; Electronic Circuits and Systems; Mathematical and Computational Engineering Applications; Systems Theory, Control; Mathematik für Ingenieure; Kybernetik und Systemtheorie; BC; EA

Simulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. This leads to very high dimensional problems which nowadays require simulation times too large for the short time-to-market demands in industry. 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.

we survey the state of the art in the challenging research field of MOR for ICs, and also address its future research directions. Special emphasis is taken on aspects stemming from miniturisations to the nano scale. Contributions cover complexity reduction using e.g., balanced truncation, Krylov-techniques or POD approaches. For semiconductor applications a focus is on generalising current techniques to differential-algebraic equations, on including design parameters, on preserving stability, and on including nonlinearity by means of piecewise linearisations along solution trajectories (TPWL) and interpolation techniques for nonlinear parts. Furthermore the influence of interconnects and power grids on the physical properties of the device is considered, and also top-down system design approaches in which detailed block descriptions are combined with behavioral models. Further topics consider MOR and the combination of approaches from optimisation and statistics, and the inclusion of PDE models with emphasis on MOR for the resulting partial differential algebraic systems. The methods which currently are being developed have also relevance in other application areas such as mechanical multibody systems, and systems arising in chemistry and to biology.

also reflects and documents the vivid interaction between three active research projects in this area, namely the EU-Marie Curie Action ToK project O-MOORE-NICE (members in Belgium, The Netherlands and Germany), the EU-Marie Curie Action RTN-project COMSON (members in The Netherlands, Italy, Germany, and Romania), and the German federal project System reduction in nano-electronics (SyreNe).

Model Reduction for Circuit Simulation Model Reduction for Circuit Simulation

1.1 Introduction. 1.2 Mathematical problems in the electronics industry. 1.3 Passivity and realizability. 1.4 Structure preservation. 1.5 Reduction of MIMO networks. 1.6 MOR for delay equations. 1.7 Parameterized and nonlinear MOR. 1.8 Summary: present and future needs of the electronics industry. References.

2.1 Introduction. 2.2 RCL Circuit Equations. 2.3 Projection-Based Order Reduction. 2.4 The SPRIM Algorithm. 2.5 Treatment of Voltage Sources. 2.6 Numerical Examples. 2.7 Concluding Remarks. References.

3.1 Introduction. 3.2 Circuit equations. 3.3 Balancing-related model reduction. 3.4 Numerical methods for matrix equations. 3.5 Numerical examples. 3.6 Conclusions and open problems. References.

4.1 Introduction and Motivation. 4.2 Background. 4.3 Theoretical Aspects. 4.4 Tangential interpolation for modeling Y-parameters. 4.5 Numerical Results. 4.6 Conclusion. References.

5.1 Introduction. 5.2 Forward and reverse modeling: problem descriptions. 5.3 Forward Modeling. 5.3.1 Performance Figures via Surrogate Models. 5.4 Reverse Modeling with the NBI method. 5.5 Reverse modeling using transistor level simulations. 5.6 Discussion and conclusions. References.

6.1 Introduction. 6.2 Methods Based on Recycling Krylov Subspaces. 6.3 Application to Model Order Reduction. 6.4 Simulation Results. 6.5 Conclusions. References.

7.1 Introduction. 7.2 Background. 7.3 Parametric Macromodeling. 7.4 Choice of basis functions. 7.5 Example: Double folded stub microstrip bandstop filter. 7.6 Conclusions. References.

8.1 Introduction. 8.2 Elimination of RC-node by TICER. 8.3 Inductance Elimination. 8.4 Elimination of Coupled Inductances. 8.5 Eliminations under LC Couplings. 8.6 Algorithmic Aspects. 8.7 Numerical Examples. 8.8 Conclusion. References.

9.1 Introduction. 9.2 Phase noise analysis of oscillators. 9.3 Oscillator coupled to a balun. 9.4 Oscillator coupling to a transmission line. 9.5 Model order reduction. 9.6 Numerical experiments. 9.7 Conclusion. References.

10.1 Introduction. 10.2 Complete coupled system. 10.3 Simulation of the full system. 10.4 Model reduction. 10.5 Numerical investigation. Appendix: Proper Orthogonal Decomposition. References.

11.1 Introduction. 11.2 Periodic Descriptor Systems. 11.3 Periodic Gramians and Matrix Equations. 11.4 Balanced Truncation Model Reduction. 11.5 Example. 11.6 Conclusion. References.

12.1 Introduction. 12.2 Foster synthesis of rational transfer functions. 12.3 Structure preservation and synthesis by unstamping. 12.4 Numerical examples. 12.5 Conclusions and outlook. References.

13.1 Introduction. 13.2 Background for Model Reduction of Linear Networks. 13.3 Description of distributed sources. 13.4 Examples. 13.5 Conclusion. References.

14.1 Introduction. 14.2 Linear port-Hamiltonian systems. 14.3 The Kalman decomposition of port-Hamiltonian systems. 14.4 The co-energy variable representation. 14.5 Balancing for port-Hamiltonian systems. 14.6 Reduction of port-Hamiltonian systems in the general case. 14.7 Example. 14.8 Conclusions. Appendix. References.

15.1 Motivation. 15.2 Symbolic Techniques. 15.3 Hierarchical systems. 15.4 Workflow for the exploitation of the hierarchy. 15.5 Comparison to other approaches. 15.6 Summary and future work. References.

16.1 Introduction. 16.2 The Extended SVDMOR Approach. 16.3 Stability, Passivity, and Reciprocity. 16.4 Remarks and Outlook. References.

17.1 Introduction. 17.2 Linear versus nonlinear model order reduction. 17.3 Some nonlinear MOR techniques. 17.4 TPWL and POD. 17.5 Numerical examples. 17.6 Discussion and outlook. References.

18.1 Static versus Dynamic Approximation. 18.2 Gramians for Linear Systems and Applications. 18.3 Metric Properties of Balanced Truncation. 18.4 Nonlinear Model Reduction. References.

Part I Invited Papers. 1 The need for novel model order reduction techniques in the electronics industry; .W.H.A. Schilders. 2 The SPRIM Algorithm for Structure-Preserving Order Reduction of General RCL Circuits; Roland W. Freund. 3 Balancing-Related Model Reduction of Circuit Equations Using Topological Structure; Tatjana Stykel. 4 Topics in Model Order Reduction with Applications to Circuit Simulation; Sanda Lefteriu and Athanasios C. Antoulas. Part II Contributed Papers. 5 Forward and Reverse Modeling of Low Noise Amplifiers based on L. De Tommasi, J. Rommes, T. Beelen, M. Sevat, J. A. Croon and T. Dhaene. Peter Benner and Lihong Feng. 7 Data-driven Parameterized Model Order Reduction Using z-domain Multivariate Orthonormal Vector Fitting Technique; Francesco Ferranti, Dirk Deschrijver, Luc Knockaert and Tom Dhaene. 8 Network Reduction by Inductance Elimination; M.M. Gourary, S.G.Rusakov, S.L.Ulyanov, and M.M.Zharov. 9 Simulation of coupled oscillators using nonlinear phase macromodels and model order reduction; Davit Harutyunyan and Joost Rommes. 10 POD Model Order Reduction of Drift-Diffusion Equations in Electrical Networks; Michael Hinze, Martin Kunkel and Morten Vierling. 11 Model Reduction of Periodic Descriptor Systems Using Balanced Truncation; Peter Benner, Mohammad-Sahadet Hossain and Tatjana Stykel. 12 On synthesis of reduced order models; Roxana Ionutiu and Joost Rommes. Stefan Ludwig and Wolfgang Mathis. 14 Structure preserving port-Hamiltonian model reduction of electrical circuits; R.V. Polyuga and A.J. van der Schaft. 15 Coupling of numerical and symbolic techniques for model order reduction in circuit design; Oliver Schmidt Thomas Halfmann Patrick Lang. 16 On Stability, Passivity and Reciprocity Preservation of ESVDMOR; Peter Benner and André Schneider. 17 Model order reduction of nonlinear systems in circuit simulation: status and applications; Michael Striebel and Joost Rommes. 18 An Approach to Nonlinear Balancing and MOR ; Erik I. Verriest.

Simulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. This leads to very high dimensional problems which nowadays require simulation times too large for the short time-to-market demands in industry. 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.

we survey the state of the art in the challenging research field of MOR for ICs, and also address its future research directions. Special emphasis is taken on aspects stemming from miniturisations to the nano scale. Contributions cover complexity reduction using e.g., balanced truncation, Krylov-techniques or POD approaches. For semiconductor applications a focus is on generalising current techniques to differential-algebraic equations, on including design parameters, on preserving stability, and on including nonlinearity by means of piecewise linearisations along solution trajectories (TPWL) and interpolation techniques for nonlinear parts. Furthermore the influence of interconnects and power grids on the physical properties of the device is considered, and also top-down system design approaches in which detailed block descriptions are combined with behavioral models. Further topics consider MOR and the combination of approaches from optimisation and statistics, and the inclusion of PDE models with emphasis on MOR for the resulting partial differential algebraic systems. The methods which currently are being developed have also relevance in other application areas such as mechanical multibody systems, and systems arising in chemistry and to biology.

also reflects and documents the vivid interaction between three active research projects in this area, namely the EU-Marie Curie Action ToK project O-MOORE-NICE (members in Belgium, The Netherlands and Germany), the EU-Marie Curie Action RTN-project COMSON (members in The Netherlands, Italy, Germany, and Romania), and the German federal project System reduction in nano-electronics (SyreNe).

Model Reduction for Circuit Simulation Model Reduction for Circuit Simulation

Future direction in MOR for circuit simulations

Aspects of MOR related to miniaturization to nano-scale

Includes supplementary material: sn.pub/extras



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