System Reduction for Nanoscale IC Design: Mathematics in Industry, cartea 20
Editat de Peter Benneren Limba Engleză Paperback – 12 mai 2018
The goal is to develop and compare methods for system reduction in the design of high dimensional nanoelectronic ICs, and to test these methods in the practice of semiconductor development. Six chapters describe the challenges for numerical simulation of nanoelectronic circuits and suggest model reduction methods for constituting equations. These include linear and nonlinear differential equations tailored to circuit equations and drift diffusion equations for semiconductor devices. The performance of these methods is illustrated with numerical experiments using real-world data. Readers will benefit from an up-to-date overview of the latest model reduction methods in computational nanoelectronics.
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Paperback (1) | 376.02 lei 6-8 săpt. | |
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Specificații
ISBN-13: 9783319791548
ISBN-10: 3319791540
Pagini: 197
Ilustrații: XI, 197 p. 73 illus., 39 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.31 kg
Ediția:Softcover reprint of the original 1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Mathematics in Industry
Locul publicării:Cham, Switzerland
ISBN-10: 3319791540
Pagini: 197
Ilustrații: XI, 197 p. 73 illus., 39 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.31 kg
Ediția:Softcover reprint of the original 1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Mathematics in Industry
Locul publicării:Cham, Switzerland
Cuprins
Preface.- 1 Model order reduction of integrated circuits in electrical networks: Michael Hinze, Martin Kunkel, Ulrich Matthes, and Morten Vierling.- 2 Element-based model reduction in circuit simulation: Andreas Steinbrecher and Tatjana Stykel.- 3 Reduced Representation of Power Grid Models: Peter Benner and André Schneider.- 4 Coupling of numeric/symbolic reduction methods for generating parametrized models of nanoelectronic systems: Oliver Schmidt, Matthias Hauser, and Patrick Lang.- 5 Low-Rank Cholesky Factor Krylov Subspace Methods for Generalized Projected Lyapunov Equations: Matthias Bollhöfer and André K. Eppler.- Index.
Textul de pe ultima copertă
This book describes the computational challenges posed by the progression toward nanoscale electronic devices and increasingly short design cycles in the microelectronics industry, and proposes methods of model reduction which facilitate circuit and device simulation for specific tasks in the design cycle.
The goal is to develop and compare methods for system reduction in the design of high dimensional nanoelectronic ICs, and to test these methods in the practice of semiconductor development. Six chapters describe the challenges for numerical simulation of nanoelectronic circuits and suggest model reduction methods for constituting equations. These include linear and nonlinear differential equations tailored to circuit equations and drift diffusion equations for semiconductor devices. The performance of these methods is illustrated with numerical experiments using real-world data. Readers will benefit from an up-to-date overview of the latest model reduction methods in computational nanoelectronics.
The goal is to develop and compare methods for system reduction in the design of high dimensional nanoelectronic ICs, and to test these methods in the practice of semiconductor development. Six chapters describe the challenges for numerical simulation of nanoelectronic circuits and suggest model reduction methods for constituting equations. These include linear and nonlinear differential equations tailored to circuit equations and drift diffusion equations for semiconductor devices. The performance of these methods is illustrated with numerical experiments using real-world data. Readers will benefit from an up-to-date overview of the latest model reduction methods in computational nanoelectronics.
Caracteristici
Describes computational techniques for model reduction in nanoelectronics Offers special model reduction methods for device simulation and for terminal reduction Illustrates the performance of proposed methods, using real-world data