Details

Passive Macromodeling

Theory and Applications
Wiley Series in Microwave and Optical Engineering 1. Aufl.

von: Stefano Grivet-Talocia, Bjorn Gustavsen
144,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	19.10.2015
ISBN/EAN:	9781119140979
Sprache:	englisch
Anzahl Seiten:	904

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Titelbeschreibung

Offers an overview of state of the art passive macromodeling techniques with an emphasis on black-box approaches This book offers coverage of developments in linear macromodeling, with a focus on effective, proven methods. After starting with a definition of the fundamental properties that must characterize models of physical systems, the authors discuss several prominent passive macromodeling algorithms for lumped and distributed systems and compare them under accuracy, efficiency, and robustness standpoints. The book includes chapters with standard background material (such as linear time-invariant circuits and systems, basic discretization of field equations, state-space systems), as well as appendices collecting basic facts from linear algebra, optimization templates, and signals and transforms. The text also covers more technical and advanced topics, intended for the specialist, which may be skipped at first reading. <ul> <li>Provides coverage of black-box passive macromodeling, an approach developed by the authors</li> <li>Elaborates on main concepts and results in a mathematically precise way using easy-to-understand language</li> <li>Illustrates macromodeling concepts through dedicated examples</li> <li>Includes a comprehensive set of end-of-chapter problems and exercises</li> </ul> Passive Macromodeling: Theory and Applications serves as a reference for senior or graduate level courses in electrical engineering programs, and to engineers in the fields of numerical modeling, simulation, design, and optimization of electrical/electronic systems. Stefano Grivet-Talocia, PhD, is an Associate Professor of Circuit Theory at the Politecnico di Torino in Turin, Italy, and President of IdemWorks. Dr. Grivet-Talocia is author of over 150 technical papers published in international journals and conference proceedings. He invented several algorithms in the area of passive macromodeling, making them available through IdemWorks. Bjørn Gustavsen, PhD, is a Chief Research Scientist in Energy Systems at SINTEF Energy Research in Trondheim, Norway. More than ten years ago, Dr. Gustavsen developed the original version of the vector fitting method with Prof. Semlyen at the University of Toronto. The vector fitting method is one of the most widespread approaches for model extraction. Dr. Gustavsen is also an IEEE fellow.

Inhaltsverzeichnis

Preface xix 1 Introduction 1 1.1 Why Macromodeling? 1 1.2 Scope 4 1.3 Macromodeling Flows 6 1.3.1 Macromodeling via Model Order Reduction 6 1.3.2 Macromodeling from Field Solver Data 7 1.3.3 Macromodeling from Measured Responses 8 1.4 Rational Macromodeling 9 1.5 Physical Consistency Requirements 11 1.6 Time-Domain Implementation 15 1.7 An Example 16 1.8 What Can Go Wrong? 17 2 Linear Time-Invariant Circuits and Systems 23 2.1 Basic Definitions 24 2.1.1 Linearity 24 2.1.2 Memory and Causality 26 2.1.3 Time Invariance 26 2.1.4 Stability 27 2.1.5 Passivity 28 2.2 Linear Time-Invariant Systems 28 2.2.1 Impulse Response 29 2.2.2 Properties of LTI Systems 32 2.3 Frequency-Domain Characterizations 33 2.4 Laplace and Fourier Transforms 34 2.4.1 Bilateral Laplace Transform and Transfer Matrices 34 2.4.2 Causal LTI Systems and the Unilateral Laplace Transform 36 2.4.3 Fourier Transform 36 2.5 Signal and System Norms∗ 37 2.5.1 Signal Norms 38 2.5.2 System Norms 41 2.6 Multiport Representations 44 2.6.1 Ports and Terminals 44 2.6.2 Immittance Representations 45 2.6.3 Scattering Representations 46 2.6.4 Reciprocity 48 2.7 Passivity 49 2.7.1 Power and Energy 50 2.7.2 Passivity and Causality 51 2.7.3 The Static Case 52 2.7.4 The Dynamic Case 53 2.7.5 Positive Realness Bounded Realness and Passivity 54 2.7.6 Some Examples 56 2.8 Stability and Causality 59 2.8.1 Laplace-Domain Conditions for Causality 61 2.8.2 Laplace-Domain Conditions for BIBO Stability 62 2.8.3 Causality and Stability 62 2.9 Boundary Values and Dispersion Relations∗ 64 2.9.1 Assumptions 64 2.9.2 Reconstruction of H(s) for s ∈ ℂ+ 65 2.9.3 Reconstruction of H(s) for s ∈ jℝ 65 2.9.4 Causality and Dispersion Relations 67 2.9.5 Generalizations 68 2.10 Passivity Conditions on the Imaginary Axis∗ 70 Problems 71 3 Lumped LTI Systems 73 3.1 An Example from Circuit Theory 74 3.1.1 Variation on a Theme 76 3.1.2 Driving-Point Impedance 77 3.2 State-Space and Descriptor Forms 77 3.2.1 Singular Descriptor Forms 77 3.2.2 Internal Representations of Lumped LTI Systems 79 3.3 The Zero-Input Response 80 3.4 Internal Stability 81 3.4.1 Lyapunov Stability 81 3.4.2 Internal Stability of LTI Systems 83 3.5 The Lyapunov Equation 84 3.6 The Zero-State Response 87 3.6.1 Impulse Response 88 3.7 Operations on State-Space Systems 89 3.7.1 Interconnections 90 3.7.2 Inversion 91 3.7.3 Similarity Transformations 91 3.8 Gramians 91 3.8.1 Observability 92 3.8.2 Controllability 93 3.8.3 Minimal Realizations 95 3.9 Reciprocal State-Space Systems 95 3.10 Norms 97 3.10.1 L2 Norm 98 3.10.2 H∞ Norm 99 Problems 100 4 Distributed LTI Systems 103 4.1 One-Dimensional Distributed Circuits 104 4.1.1 The Discrete-Space Case 104 4.1.2 The Continuous-Space Case 106 4.1.3 Discussion 109 4.2 Two-Dimensional Distributed Circuits∗ 111 4.2.1 The Discrete-Space Case 112 4.2.2 The Continuous-Space Case 114 4.2.3 A Closed-Form Solution 116 4.2.4 Spatial Discretization 118 4.2.5 Discussion 120 4.3 General Electromagnetic Characterization 123 4.3.1 3D Electromagnetic Modeling 126 4.3.2 Summary and Outlook 130 Problems 131 5 Macromodeling Via Model Order Reduction 135 5.1 Model Order Reduction 135 5.2 Moment Matching 136 5.2.1 Moments 136 5.2.2 Padé Approximation and AWE 138 5.2.3 Complex Frequency Hopping 139 5.3 Reduction by Projection 140 5.3.1 Krylov Subspaces 141 5.3.2 Implicit Moment Matching: The Orthogonal Case 142 5.3.3 The Arnoldi Process 143 5.3.4 PRIMA 145 5.3.5 Multipoint Moment Matching 147 5.3.6 An Example 148 5.3.7 Implicit Moment Matching: The Biorthogonal Case 151 5.3.8 Padé Via Lanczos (PVL) 154 5.4 Reduction by Truncation 155 5.4.1 Balancing 156 5.4.2 Balanced Truncation 158 5.5 Advanced Model Order Reduction∗ 159 5.5.1 Passivity-Preserving Balanced Truncation 159 5.5.2 Balanced Truncation of Descriptor Systems 160 5.5.3 Reducing Large-Scale Systems 161 Problems 166 6 Black-Box Macromodeling and Curve Fitting 169 6.1 Basic Curve Fitting 171 6.1.1 Linear Least Squares 172 6.1.2 Maximum Likelihood Estimation 174 6.1.3 Polynomial Fitting 176 6.2 Direct Rational Fitting 182 6.2.1 Polynomial Ratio Form 183 6.2.2 Pole–Zero Form 183 6.2.3 Partial Fraction Form 184 6.2.4 Partial Fraction Form with Fixed Poles 184 6.2.5 Nonlinear Least Squares 185 6.3 Linearization via Weighting 187 6.4 Asymptotic Pole–Zero Placement 191 6.5 ARMA Modeling 193 6.5.1 Modeling from Time-Domain Responses 195 6.5.2 Modeling from Frequency Domain Responses 197 6.5.3 Conversion of ARMA Models 201 6.6 Prony’s Method 203 6.7 Subspace-Based Identification∗ 204 6.7.1 Discrete-Time State-Space Systems 204 6.7.2 Macromodeling from Impulse Response Samples 205 6.7.3 Macromodeling from Input–Output Samples 207 6.7.4 From Discrete-Time to Continuous-Time State-Space Models 210 6.7.5 Frequency-Domain Subspace Identification 211 6.7.6 Generalized Pencil-of-Function Methods 212 6.7.7 Examples 214 6.8 Loewner Matrix Interpolation∗ 215 6.8.1 The Scalar Case 216 6.8.2 The Multiport Case 218 Problems 222 7 The Vector Fitting Algorithm 225 7.1 The Sanathanan–Koerner Iteration 226 7.1.1 The Steiglitz–McBride Iteration 229 7.2 The Generalized Sanathanan–Koerner Iteration 231 7.2.1 General Basis Functions 231 7.2.2 The Partial Fraction Basis 233 7.3 Frequency-Domain Vector Fitting 234 7.3.1 A Simple Model Transformation 234 7.3.2 Computing the New Poles 236 7.3.3 The Vector Fitting Iteration 237 7.3.4 From GSK to VF 239 7.4 Consistency And Convergence 241 7.4.1 Consistency 241 7.4.2 Convergence 242 7.4.3 Formal Convergence Analysis 245 7.5 Practical VF Implementation 247 7.5.1 Causality Stability and Realness 247 7.5.2 Order Selection and Initialization 253 7.5.3 Improving Numerical Robustness 254 7.6 Relaxed Vector Fitting 256 7.6.1 Weight Normalization Noise and Convergence 256 7.6.2 Relaxed Vector Fitting 259 7.7 Tuning VF 264 7.7.1 Weighting and Error Control 264 7.7.2 High-Frequency Behavior 266 7.7.3 High-Frequency Constraints 268 7.7.4 DC Point Enforcement 269 7.7.5 Simultaneous Constraints 271 7.8 Time-Domain Vector Fitting 273 7.9 z-Domain Vector Fitting 278 7.10 Orthonormal Vector Fitting 281 7.10.1 Orthonormal Rational Basis Functions 281 7.10.2 The OVF Iteration 284 7.10.3 The OVF Pole Relocation Step 285 7.10.4 Finding Residues 286 7.11 Other Variants 288 7.11.1 Magnitude Vector Fitting 288 7.11.2 Vector Fitting with L1 Norm Minimization 291 7.11.3 Dealing with Higher Pole Multiplicities 293 7.11.4 Including Higher Order Derivatives 294 7.11.5 Hard Relocation of Poles 295 7.12 Notes on Overfitting and Ill-Conditioning 296 7.12.1 Exact Model Identification 296 7.12.2 Curve Fitting 297 7.13 Application Examples 299 7.13.1 Surface Acoustic Wave Filter 299 7.13.2 Subnetwork Equivalent 301 7.13.3 Transformer Modeling from Time-Domain Measurements 303 Problems 303 8 Advanced Vector Fitting for Multiport Problems 307 8.1 Introduction 307 8.2 Adapting VF to Multiple Responses 308 8.2.1 Pole Identification 308 8.2.2 Fast Vector Fitting 310 8.2.3 Residue Identification 311 8.3 Multiport Formulations 312 8.3.1 Single-Element Modeling: Multi-SISO Structure 314 8.3.2 Single-Column Modeling: Multi-SIMO Structure 316 8.3.3 Matrix Modeling: MIMO Structure 317 8.3.4 Matrix Modeling: Minimal Realizations 318 8.3.5 Sparsity Considerations 322 8.4 Enforcing Reciprocity 322 8.4.1 External Reciprocity 324 8.4.2 Internal Reciprocity∗ 325 8.5 Compressed Macromodeling 329 8.5.1 Data Compression 329 8.5.2 Compressed Rational Approximation 330 8.5.3 An Application Example 331 8.6 Accuracy Considerations 333 8.6.1 Noninteracting Models 333 8.6.2 Interacting Models Scalar Case 334 8.6.3 Error Magnification in Multiport Systems 338 8.7 Overcoming Error Magnification 340 8.7.1 Elementwise Inverse Weighting 340 8.7.2 Diagonalization 342 8.7.3 Mode-Revealing Transformations 347 8.7.4 Modal Vector Fitting 356 8.7.5 External and Internal Ports 358 Problems 363 9 Passivity Characterization of Lumped LTI Systems 365 9.1 Internal Characterization of Passivity 365 9.1.1 A First Order Example 365 9.1.2 The Dissipation Inequality 367 9.1.3 Lumped LTI Systems 368 9.2 Passivity of Lumped Immittance Systems 368 9.2.1 Rational Positive Real Matrices 369 9.2.2 Extracting Purely Imaginary Poles 372 9.2.3 The Positive Real Lemma 376 9.2.4 Positive Real Functions Revisited 378 9.2.5 Popov Functions and Spectral Factorizations 379 9.2.6 Hamiltonian Matrices 381 9.2.7 Passivity Characterization via Hamiltonian Matrices 385 9.2.8 Determination of Local Passivity Violations 387 9.2.9 Quantification of Passivity Violations via Bisection 390 9.2.10 Quantification of Passivity Violations via Sampling 393 9.2.11 Frequency Transformations 394 9.2.12 Extended Hamiltonian Pencils 396 9.2.13 Generalized Hamiltonian Pencils 398 9.2.14 Positive Real Lemma for Descriptor Systems 399 9.3 Passivity of Lumped Scattering Systems 402 9.3.1 Rational Bounded Real Matrices 402 9.3.2 The Bounded Real Lemma 406 9.3.3 Bounded Real Functions Revisited 408 9.3.4 Popov Functions Spectral Factorizations and Hamiltonian Matrices 409 9.3.5 Passivity Characterization via Hamiltonian Matrices 410 9.3.6 Determination of Local Passivity Violations 413 9.3.7 Quantification of Passivity Violations via Bisection 416 9.3.8 Quantification of Passivity Violations via Sampling 420 9.3.9 Extended Hamiltonian Pencils 421 9.3.10 Generalized Hamiltonian Pencils 422 9.3.11 Bounded Real Lemma for Descriptor Systems 423 9.4 Advanced Passivity Characterization 426 9.4.1 On the Computation of Imaginary Hamiltonian Eigenvalues 426 9.4.2 Large-Scale Hamiltonian Eigenvalue Problems∗ 427 9.4.3 Half-Size Passivity Test Matrices 430 Problems 433 10 Passivity Enforcement of Lumped LTI Systems 437 10.1 Passivity Constraints for Lumped LTI Systems 437 10.1.1 Passive State-Space Immittance Systems 438 10.1.2 Passive State-Space Scattering Systems 439 10.2 State-Space Perturbation 440 10.2.1 Asymptotic Perturbation 441 10.2.2 Dynamic Perturbation 441 10.2.3 Input-State Perturbation 442 10.2.4 State-Output Perturbation 443 10.2.5 A Perturbation Strategy for Passivity Enforcement 444 10.3 Asymptotic Passivity Enforcement 445 10.3.1 Immittance Systems 445 10.3.2 Scattering Systems 446 10.4 Imaginary Poles of Immittance Systems 447 10.5 Local Passivity Enforcement 448 10.5.1 Local Passivity Constraints 449 10.5.2 Enforcing Local Passivity Constraints 454 10.6 Passivity Enforcement Via Hamiltonian Perturbation 460 10.6.1 Hamiltonian Perturbation of Immittance Systems 462 10.6.2 Hamiltonian Perturbation of Scattering Systems 464 10.6.3 Hamiltonian Perturbation Strategies 465 10.6.4 Slopes 468 10.6.5 Global Passivity Enforcement via Hamiltonian Perturbation 471 10.7 Linear Matrix Inequalities 474 10.7.1 Parameterizations 476 10.8 Computational Cost 477 10.9 Advanced Accuracy Control 478 10.9.1 Frequency-Selective Norms 478 10.9.2 Individual Response Weighting 480 10.9.3 Bandlimited Norms 481 10.9.4 Relative Norms 484 10.9.5 Data-Based Cost Functions 486 10.10 Least-Squares Residue Perturbation 487 10.10.1 Basic Residue Perturbation (RP) 487 10.10.2 Spectral Residue Perturbation (SRP) 492 10.10.3 Mode-Revealing Transformations 493 10.10.4 Modal Perturbation (MP) 494 10.10.5 Robust Iterations 495 10.11 Alternative Formulations 496 10.11.1 Passivity Constraints Based on H∞ norm∗ 496 10.11.2 Iterative Update by Fitting Passivity Violations 503 10.11.3 Pole Perturbation Approaches 505 10.11.4 Parameterization via Positive Fractions 506 10.12 Descriptor Systems∗ 508 10.12.1 Perturbation of Generalized Hamiltonian Pencils 508 10.12.2 Handling Singular Direct Coupling Terms 509 10.12.3 Proper Part Extraction 510 10.12.4 Handling Impulsive Terms 511 10.12.5 Accuracy Control 512 Problems 512 11 Time-Domain Simulation 517 11.1 Discretization of ODE Systems 518 11.2 Interconnection of Macromodels 520 11.3 Direct Convolution 522 11.3.1 Equivalent Circuit Implementations 524 11.3.2 Discussion 527 11.4 Interfacing State-Space Macromodels 528 11.4.1 Equivalent Circuit Interfaces 530 11.5 Interfacing Pole-Residue Macromodels 533 11.5.1 Scalar Single-Pole System 533 11.5.2 General Multiport High-Order Systems 535 11.5.3 Discussion 537 11.6 Equivalent Circuit Synthesis 537 11.6.1 Direct Admittance Synthesis 538 11.6.2 Direct State-Space Synthesis 541 11.6.3 Sparse Synthesis 543 11.6.4 Classical RLCT Synthesis∗ 545 Problems 559 12 Transmission Lines and Distributed Systems 563 12.1 Introduction 563 12.2 Multiconductor Transmission Lines 564 12.2.1 Per-Unit-Length Matrices 564 12.2.2 Frequency-Domain Solution via Modal Decomposition 566 12.2.3 Frequency-Domain Solution in the Physical Domain 570 12.3 Direct Macromodeling Approaches 573 12.3.1 Folded Line Equivalent Models 573 12.4 Lumped Segmentation Approaches 577 12.4.1 Segmenting 577 12.4.2 Topology-Based Methods 578 12.5 Matrix Rational Approximations 582 12.5.1 Padé Matrix Rational Approximations 583 12.5.2 Series Expansion into Eigenfunctions 586 12.6 Traveling Wave Formulations 590 12.6.1 Voltage Waves 591 12.6.2 Current Waves 592 12.6.3 Thévenin and Norton Equivalents 593 12.6.4 Terminal Admittance from Traveling Wave Model 593 12.6.5 Modal Traveling Waves 594 12.7 Lossless Traveling Wave Modeling 595 12.7.1 Delay Extraction for Lossless MTL 597 12.8 Traveling Wave Modeling of Scalar Lossy Transmission Lines 599 12.9 Representations Based on Multiple Reflections 601 12.9.1 The Delayed Vector Fitting Scheme 604 12.10 Basic Delay Extraction for Lossy MTL 606 12.11 Frequency-Dependent Traveling Wave Modeling 607 12.11.1 Modal Domain 608 12.11.2 Physical Domain 613 12.11.3 Delay Extraction and Optimization∗ 625 12.12 General Delayed-Rational Macromodeling 626 12.12.1 Delay Estimation 629 12.12.2 Passivity Enforcement 631 12.12.3 Equivalent Circuit Synthesis 637 12.13 Passivity of Traveling Wave Models∗ 638 12.14 Time-Domain Implementation for Traveling Wave Models 641 12.14.1 The Scalar Lossless Line 641 12.14.2 The Scalar Lossy Line 643 12.14.3 Lossy Multiconductor Transmission Lines 648 12.14.4 Examples 652 12.15 Discussion 657 Problems 658 13 Applications 663 13.1 Modeling for Signal and Power Integrity 663 13.1.1 Prelayout Analysis of Backplane Interconnects 664 13.1.2 Full Package Analysis 667 13.1.3 Full Board Analysis and Simulation 672 13.1.4 High-Speed Channel Modeling and Simulation 681 13.1.5 Model Extraction from Measurements 687 13.2 Computational Electromagnetics 691 13.2.1 Dynamic Subcell Models in Time-Domain Solvers 691 13.2.2 Automatic Stopping Criteria for Time-Domain Solvers 695 13.2.3 VF-Based Adaptive Frequency Sampling 698 13.3 Small-Signal Macromodels for RF and AMS Applications 701 13.4 Modeling for High-Voltage Power Systems 704 13.4.1 Subnetwork Equivalencing 705 13.4.2 Power Transformer Modeling from Frequency Sweep Measurements 708 13.4.3 Power Transformer Modeling from Manufacturer’s White-Box Model 715 13.5 Fluid Transmission Lines 720 13.6 Mechanical Systems 726 13.7 Ship Motion in Irregular Seas 728 13.8 Summary 733 14 Summary and Outlook 735 14.1 Parameterized Macromodels 735 14.1.1 Parameterized Macromodels with Fixed Poles 736 14.1.2 Fully Parameterized Macromodels 738 14.1.3 Higher Dimensional Parameter Spaces 742 14.2 Open Issues 743 14.2.1 Optimal Passivity Enforcement 743 14.2.2 Systems with Many Ports 744 14.2.3 White-Box Model Identification and Tuning 744 14.2.4 Transmission Line Models 745 14.2.5 Delay Systems 746 14.2.6 Extension to NL Systems 749 14.2.7 Integration with other solvers 749 Appendix A Notation 751 Appendix B Acronyms 757 Appendix C Linear Algebra 761 Appendix D Optimization Templates 781 Appendix E Signals and Transforms 805 Bibliography 839 Index 863

Autorenportrait

Stefano Grivet-Talocia, PhD, is an Associate Professor of Circuit Theory at the Politecnico di Torino in Turin, Italy, and President of IdemWorks. Dr. Grivet-Talocia is author of over 150 technical papers published in international journals and conference proceedings. He invented several algorithms in the area of passive macromodeling, making them available through IdemWorks. Bjørn Gustavsen, PhD, is a Chief Research Scientist in Energy Systems at SINTEF Energy Research in Trondheim, Norway. More than ten years ago, Dr. Gustavsen developed the original version of the vector fitting method with Prof. Semlyen at the University of Toronto. The vector fitting method is one of the most widespread approaches for model extraction. Dr. Gustavsen is also an IEEE fellow.

Back cover copy

Offers an overview of state of the art passive macromodeling techniques with an emphasis on black-box approaches This book offers coverage of developments in linear macromodeling, with a focus on effective, proven methods. After starting with a definition of the fundamental properties that must characterize models of physical systems, the authors discuss several prominent passive macromodeling algorithms for lumped and distributed systems and compare them under accuracy, efficiency, and robustness standpoints. The book includes chapters with standard background material (such as linear time-invariant circuits and systems, basic discretization of field equations, state-space systems), as well as appendices collecting basic facts from linear algebra, optimization templates, and signals and transforms. The text also covers more technical and advanced topics, intended for the specialist, which may be skipped at first reading. <ul> <li>Provides coverage of black-box passive macromodeling, an approach developed by the authors</li> <li>Elaborates on main concepts and results in a mathematically precise way using easy-to-understand language</li> <li>Illustrates macromodeling concepts through dedicated examples</li> <li>Includes a comprehensive set of end-of-chapter problems and exercises</li> </ul> Passive Macromodeling: Theory and Applications serves as a reference for senior or graduate level courses in electrical engineering programs, and to engineers in the fields of numerical modeling, simulation, design, and optimization of electrical/electronic systems. Stefano Grivet-Talocia, PhD, is an Associate Professor of Circuit Theory at the Politecnico di Torino in Turin, Italy, and President of IdemWorks. Dr. Grivet-Talocia is author of over 150 technical papers published in international journals and conference proceedings. He invented several algorithms in the area of passive macromodeling, making them available through IdemWorks. Bjørn Gustavsen, PhD, is a Chief Research Scientist in Energy Systems at SINTEF Energy Research in Trondheim, Norway. More than ten years ago, Dr. Gustavsen developed the original version of the vector fitting method with Prof. Semlyen at the University of Toronto. The vector fitting method is one of the most widespread approaches for model extraction. Dr. Gustavsen is also an IEEE fellow.