Photonic Analog-to-Digital Conversion

340 Pages·2001·15.15 MB·English

by  Professor Barry L. Shoop (auth.)

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Barry L. Shoop
Photonic
Analog-to-Digital
Conversion
With 259 Figures and 11 Tables
, Springer
Professor Barry L. Shoop
Photonics Research Center
and Department of Electrical Engineering
and Computer Science
United States Military Academy
West Point, NY 10996, USA
ISBN 978-3-642-07460-8 ISBN 978-3-540-44408-4 (eBook)
DOI 10.1007/978-3-540-44408-4
Library of Congress Cataloging-in-Publication Data. Shoop, Barry, 1957-. Photonic analog-to-digital conver
sion/Barry Shoop. p. cm. - (Springer series in optical sciences, ISSN 0342-4111; v. 81) Includes bibliographical
references and index. I. Analog-to-digital converters. 2. Optoelectronic devices.
3. Optical devices. I. Title. II. Series. TK7887.6.S554 2001 621.36-dc21 00-069233
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© Springer-Verlag Berlin Heidelberg 2001
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Originally published by Springer-Verlag Berlin Heidelberg New York in 2001.
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Printed on acid-free paper
In Memory of Roy A. Shoop
1917 - 1999
Preface
The purpose of this book, "Photonic Analog-to-Digital Conversion," is to
provide a resource for researchers, engineers, and students interested in AjD
conversion, in general, and specifically in the application of photonic tech
nologies to the problem of AjD conversion. As a result of over 10 years of
research in the area of oversampled photonic AjD conversion, the reader will
also find several chapters dedicated to a detailed description of photonic
based overs amp ling architectures. This book is a progress report on the past
and present research involving photonic approaches to AjD conversion and
a glimpse of several promising architectures and technologies.
The development of novel high-speed, high-resolution AjD converter ar
chitectures continues to be an active research area and one which continues
to draw significant interest in both the electronics and photonics technology
fields. At the time of writing, there has been renewed interest in photonic
based AjD conversion techniques for high-speed and high-resolution AjD
conversion; therefore, new approaches and architectures are currently under
development which could not be included in this text.
This book is the result of many years of individual and collective re
search with contributions from many individuals. I am deeply indebted to
the many people who have made important contributions to the research
reported in this monograph. In particular, I am grateful to Joseph W. Good
man of Stanford University, under whose guidance I began my investigation
of photonic-based AjD conversion. I am thankful to Robert Gray and Bruce
Wooley, also of Stanford University, for many helpful discussions on the sub
ject of quantization noise spectra and the operation and implementation of
electronic L'L1 converters, respectively.
Since joining the Photonics Research Center at the United States Military
Academy at West Point, a number of faculty members and students have par
ticipated in and made significant contributions to the research reported here.
First and foremost is Eugene K. Ressler who has been my closest collaborator
on the digital image halft oning research project. Many of the mathematical
results on the error diffusion neural network are attributed to his steadfast
and relentless desire for mathematical closure. Robert W. Sadowski, Glen P.
Dudevoir, and Andre H. Sayles contributed to the electronic circuit design
and layout of the CMOS-SEED smart pixel implementations described in
VIn Preface
Chap. 8. The PSpice simulation of the entire 5 x 5 error diffusion neural
network, also discussed in Chap. 8, was performed by Robert W. Sadowski.
Dirk A. Hall, James J. Raftery, Jr., and Timothy J. Talty participated in
the experimental characterization of the 5 x 5 CMOS-SEED smart pixel ar
ray. James R. Loy assisted in the layout of the 3 x 3 OPTOCHIP neural
array. The diffractive optical filter also described in Chap. 8 was designed by
Joseph N. Mait of the Army Research Laboratory in Adelphi, MD and was
experimentally characterized by Gregory R. Kilby. Jean R. S. Blair, Thomas
D. Wagner, and David A. Nash worked closely with me on extensions of the
error diffusion neural network to color applications and partitioning schemes
to reduce computational complexity and improve processing speed of the
halft oning process. Daniel M. Litynski was involved in a number of early
discussions related to extensions of error diffusion to high-resolution AID
conversion techniques. Pankaj K. Das has been a close collaborator on many
of the concepts of high-resolution and high-speed photonic-based AID con
version. I also want to thank both Panakj K. Das from the University of
California at San Diego and Jonathan C. Twichell from MIT Lincoln Labo
ratory for constructive comments on the draft manuscript.
Finally, and most of all, I owe my family a great debt of gratitude. To
my wife, Linda, whose continuous support and encouragement enabled me
to confront the challenges associated with research. To my son Brandon and
daughter Aubrey who gave me a new perspective on life and taught me the
true meaning of family. And finally to my parents, Roy and Ruth Shoop,
who instilled in me from an early age the importance of God, family, and
education.
West Point, New York, Barry L. Shoop
December 2000
Contents
1. Introduction.............................................. 1
1.1 The Role of AID Conversion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Key Technological Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Motivation for Photonic AID Approaches ................. 3
1.4 Organization of this Book ............................... 4
2. Performance Characteristics
of Analog-to-Digital Converters. . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 AID Converter Characteristics. . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Sampling and Conversion Rate Characteristics .... . . . . . . . . . 9
2.2.1 Sampling Rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 Conversion Rate ................................. 10
2.3 Performance Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11
2.3.1 Resolution....................................... 11
2.3.2 Dynamic Range, SQNR,
and SNR Performance Measures. . . . . . . . . . . . . . . . . . .. 14
2.3.3 Spur-Free Dynamic Range. . . . . . . . . . . . . . . . . . . . . . . .. 17
2.4 Performance Degradations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 18
2.4.1 Two-Tone Intermodulation Distortion. . . . . . . . . . . . . .. 18
2.4.2 Differential Nonlinearity. . . . . . . . . . . . . . . . . . . . . . . . . .. 19
2.4.3 Integral Nonlinearity. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 20
2.4.4 Comparator Hysteresis. . . . . . . . . . . . . . . . . . . . . . . . . . .. 20
2.4.5 Thermal Noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 22
2.4.6 Aperture Jitter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 23
2.4.7 Comparator Ambiguity ........................... 24
2.4.8 Observations..................................... 26
Summary .................................................. 28
3. Approaches to Analog-to-Digital Conversion. . . . . . . . . . . . .. 29
3.1 AID Converter Coding Schemes. . . . . . . . . . . . . . . . . . . . . . . . .. 29
3.1.1 Thermometer Coding Scheme. . . . . . . . . . . . . . . . . . . . .. 29
3.1.2 Gray Code Coding Scheme. . . . . . . . . . . . . . . . . . . . . . .. 31
3.1.3 Circular Coding Scheme. . . . . . . . . . . . . . . . . . . . . . . . . .. 31
3.2 Nyquist-Rate Converter Architectures. . . . . . . . . . . . . . . . . . . .. 32
X Contents
3.2.1 Fully Parallel or Flash AID Conversion ............. 32
3.2.2 Subranging AID Conversion. . . . . . . . . . . . . . . . . . . . . .. 33
3.2.3 Folding Architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . .. 35
3.2.4 Other Parallel Architectures . . . . . . . . . . . . . . . . . . . . . .. 36
3.2.5 Neural Network Approach to AID Conversion. . . . . . .. 39
3.2.6 Full-Search AID Conversion . . . . . . . . . . . . . . . . . . . . . .. 43
3.2.7 Successive Approximation AID Conversion . . . . . . . . .. 43
3.3 Oversampled AID Conversion. . . . . . . . . . . . . . . . . . . . . . . . . . .. 44
3.3.1 The Modulator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 46
3.3.2 Operation....................................... 49
3.3.3 The Digital Postprocessor . . . . . . . . . . . . . . . . . . . . . . . .. 60
3.3.4 Oversampled AID Performance .... . . . . . . . . . . . . . . .. 69
3.4 Parallel Oversampling AID Conversion. . . . . . . . . . . . . . . . . . .. 79
Summary .................................................. 80
4. Photonic Devices
for Analog-to-Digital Conversion. . . . . . . . . . . . . . . . . . . . . . . . .. 83
4.1 Mach-Zehnder Interferometers. . . . . . . . . . . . . . . . . . . . . . . . . .. 83
4.2 Optical Waveguide Switches ............................. 89
4.2.1 Directional Coupler Waveguide Switches ............ 90
4.2.2 Reversed l1(3 Directional Coupler. . . . . . . . . . . . . . . . . .. 92
4.2.3 Digital Optical Waveguide Switches. . . . . . . . . . . . . . . .. 93
4.3 Acousto-Optic Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 94
4.4 Multiple Quantum Well Devices .......................... 10l
4.4.1 Optical Bistability ................................ 105
4.4.2 Optical Subtraction ............................... 106
4.4.3 Switching Speed and Energy Requirements .......... 108
4.5 Smart Pixel Technology ................................. 110
4.5.1 Monolithic Integration ............................ 110
4.5.2 Direct Epitaxy ................................... 114
4.5.3 Hybrid Integration ............................... 115
Summary .................................................. 122
5. Nyquist-Rate Photonic Analog-to-Digital Conversion ..... 123
5.1 Electro-Optic AID Conversion
Based on a Mach-Zehnder Interferometer .................. 123
5.2 Optical Folding-Flash AID Converter ..................... 127
5.3 Matrix-Multiplication and Beam Deflection ................ 129
5.4 Other Approaches to Photonic AID Conversion ............ 131
Summary .................................................. 131
6. Oversampled Photonic Analog-to-Digital Conversion ...... 133
6.1 Oversampling Photonic AID Conversion ................... 133
6.2 Optical Oversampled Modulators ......................... 134
6.2.1 The Interferometric Modulator ..................... 135
Contents XI
6.2.2 The Noninterferometric Modulator 138
6.3 The Digital Postprocessor ............................... 140
6.3.1 Electronic Postprocessing .......................... 140
6.3.2 Optoelectronic Postprocessing ...................... 141
6.3.3 Observations ..................................... 141
6.4 Performance Analysis ................................... 142
6.4.1 Linear Arithmetic Errors .......................... 142
6.4.2 Quantization Noise Spectra ........................ 143
6.4.3 Cascade Error Tolerances .......................... 151
6.5 Experimental Proof-of-Concept
Photonic Modulator Demonstration ....................... 157
6.5.1 Noninterferometric Optical Subtraction ............. 158
6.5.2 Experimental Photonic First-Order
Oversampled lVIodulator ........................... 162
Summary .................................................. 166
7. Low Resolution, Two-Dimensional Analog-to-Digital
Conversion: Digital Image Halft oning ..................... 169
7.1 Introduction ........................................... 169
7.2 Approaches to Halft oning ................................ 170
7.3 The Error Diffusion Algorithm ........................... 171
7.4 Neural Network Formalism .............................. 175
7.4.1 The Hopfield-Type Neural Network ................. 175
7.4.2 Observations ..................................... 180
7.5 The Error Diffusion Neural Network ...................... 181
7.5.1 The Error Diffusion Filter ......................... 186
7.5.2 Observations ..................................... 189
7.6 Quantitative Performance Metrics ........................ 191
7.6.1 Power Spectrum Estimation ....................... 193
7.6.2 Radially Averaged Power Spectra and Anisotropy .... 195
7.7 Performance Analysis ................................... 198
7.7.1 Floyd-Steinberg Performance Analysis .............. 198
7.7.2 Symmetric Jarvis Performance Analysis . . . . . . . . . . . . . 202
7.7.3 Error Diffusion Neural Network Performance
Analysis ......................................... 202
7.8 Extensions to Color ..................................... 211
Summary .................................................. 212
8. A Photonic-Based Error Diffusion
Neural Network .......................................... 215
8.1 First-Generatioll CMOS-SEED
Error Diffusion Neural Array ............................. 216
8.2 Second-Generation CMOS-SEED
Error Diffusion Neural Array ............................. 219
8.2.1 Detailed Circuit Description ....................... 225