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Neural Network Applications in
Device and Subcircuit Modelling
for Circuit Simulation

CIP-GEGEVENS KONINKLIJKE BIBLIOTHEEK, DEN HAAG
Meijer, P.B.L.
Neural Network Applications in
Device and Subcircuit Modelling
for Circuit Simulation
Proefschrift Technische Universiteit Eindhoven,
- Met lit. opg., - Met samenvatting in het Nederlands.
ISBN 90-74445-26-8
Trefw.: IC design, modelling, neural networks, circuit simulation.

The work described in this thesis has been carried out at the Philips Research Laboratories in Eindhoven, The Netherlands, as part of the Philips Research programme.

© Philips Electronics N.V. 1996
All rights are reserved. Reproduction in whole or in part is
prohibited without the written consent of the copyright owner.

Neural Network Applications in
Device and Subcircuit Modelling
for Circuit Simulation

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de Rector Magnificus, prof.dr. J.H. van Lint, voor een commissie aangewezen door het College van Dekanen in het openbaar te verdedigen op donderdag 2 mei 1996 om 16.00 uur

door

Peter Bartus Leonard Meijer

geboren te Sliedrecht

Dit proefschrift is goedgekeurd door de promotoren:
prof.Dr.-Ing. J.A.G. Jess
prof.dr.ir. W.M.G. van Bokhoven

Contents
List of Figures
List of Tables
1 Introduction
 1.1 Modelling for Circuit Simulation
 1.2 Physical Modelling and Table Modelling
 1.3 Artificial Neural Networks for Circuit Simulation
 1.4 Potential Advantages of Neural Modelling
 1.5 Overview of the Thesis
2 Dynamic Neural Networks
 2.1 Introduction to Dynamic Feedforward Neural Networks
 2.2 Dynamic Feedforward Neural Network Equations
 2.3 Analysis of Neural Network Differential Equations
 2.4 Representations by Dynamic Neural Networks
 2.5 Mapping Neural Networks to Circuit Simulators
 2.6 Some Known and Anticipated Modelling Limitations
3 Dynamic Neural Network Learning
 3.1 Time Domain Learning
 3.2 Frequency Domain Learning
 3.3 Optional Guarantees for DC Monotonicity
4 Results
 4.1 Experimental Software
 4.2 Preliminary Results and Examples
5 Conclusions
 5.1 Summary
 5.2 Recommendations for Further Research
A Gradient Based Optimization Methods
 A.1 Alternatives for Steepest Descent
 A.2 Heuristic Optimization Method
B Input Format for Training Data
 B.1 File Header
 B.2 DC and Transient Data Block
 B.3 AC Data Block
 B.4 Example of Combination of Data Blocks
C Examples of Generated Models
 C.1 Pstar Example
 C.2 Standard SPICE Input Deck Example
 C.3 C Code Example
 C.4 FORTRAN Code Example
 C.5 Mathematica Code Example
D Time Domain Extensions
 D.1 Generalized Expressions for Time Integration
 D.2 Generalized Expressions for Transient Sensitivity
 D.3 Trapezoidal versus Backward Euler Integration
Bibliography
Summary
Samenvatting
Curriculum Vitae