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Spie Press Book

An Introduction to Optics in Computers
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Book Description

This volume surveys the entire field of optical computing. The emphasis is on breadth of coverage. The book is descriptive, the authors minimize the use of mathematics, and it is therefore most suitable for those who require an overall view of what is going on in this field. A detailed comparison is given of the capabilities of electronics and optics, and the degree to which these capabilities have been achieved is indicated. Other areas of focus include optical computing architectures, components and technologies, optical interconnects, and optical neural nets. Approximately 300 references to key works in the field are included.

Book Details

Date Published: 1 January 1992
Pages: 144
ISBN: 9780819408259
Volume: TT08

Table of Contents
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Preface
Chapter 1. Introduction
1.1 Optical computing
1.2 Optics vs electronics
1.2.1 Speed
1.2.2 Parallelism
1.2.3 Energy
1.2.4 Accuracy
1.3 Optical systems and components
1.3.1 Hybrid systems
1.4. Optical computers
1.4.1 Analog optical computing
1.4.2 Digital optical computing
1.4.3 Knowledge-based systems
1.5 References
Chapter 2. Architectures
2.1 Computer Architectures
2.1.1 According to complexity
2.1.2 According to instruction and data
2.1.2.1 SISD (single instruction single data)
2.1.2.2 SIMD (single instruction multiple data)
2.1.2.3 MISD (multiple instruction single data)
2.1.2.4 MIMD (multiple instructions multiple data)
2.1.3 According to class of problems
2.1.4 Classification of optical architectures
2.2 Optical architectures
2.2.1 General architectures
2.2.2 Bell Labs architecture
2.2.3 Correlation-based architectures
2.2.4 Vector-matrix-based systems
2.2.5 Optical disk-based architectures
2.2.6 Optical cellular processors
2.2.7 Optical symbolic substitution
2.2.8 Symbolic computing
2.2.9 Programmable optoelectronic multiprocessors (POEMs)
2.2.10 DOC-H
2.2.11 CMU hybrid neural network
2.2.12 Optical RISC machine
2.2.13 SMOEC computer
2.2.14 Optical bit-serial architectures
2.2.15 Optical array logic (OPALS)
2.2.16 Optical lookup tables
2.2.17 Arithmetic/logic unit based on optical crossbar architecture
2.3 References
Chapter 3. Components and Technologies
3.1. Spatial light modulators
3.1.1 Magneto-optic SLMs
3.1.2 Liquid crystal light valve (LCLV)
3.1.3 Liquid crystal television (LCTV)
3.1.4 Electrically addressed ferroelectric liquid crystal SLMs
3.1.5 PLZT SLMs
3.1.6 Deformable mirror SLMs
3.1.7 Multiple quantum-well-based SLMs
3.1.8 MQW light modulators
3.1.9 Acousto-optical cells
3.2 Symmetric self-electro-optic effect devices (SEEDs)
3.3 Holograms
3.3.1 Computer-generated holograms (CGHs)
3.3.2 Photorefractive nonlinear optics
3.3.3 PhotoPolymers and biopolymers
3.4 Binary or diffractive optics
3.5 Planar Optics
3.6 Micro-optics
3.7 Integrated optoelectronic processor arrays
3.8 Micro laser array
3.9 Optoelectronic VLSI chips
3. 10 GaAs/AlGaAs optical neurons
3.11 SRS inverter
3.12 SMBS switch
3.13 References
Chapter 4. Optical Interconnects
4.1 Why optical interconnects?
4.2 Advantages of optical interconnects
4.2.1 Optical interconnects for high levels of computer architectures
4.2.2 Optical interconnects for low levels of computer architectures
4.3 Optical versus electrical interconnections
4.3 Guided optical interconnects
4.3.1 Optical fiber bus routing
4.3.1.1 Shared multibus networks
4.3.1.2 Optical waveguide interconnects
4.3.1.3 Monolithic GaAs circuits
4.3.1.4 Couplers
4.4 Free-space interconnects
4.4.1 System architectures
4.4.2 Interconnection optics
4.4.2.1 Unfocused broadcasting
4.4.2.2 Clock distribution to VLSI Chips
4.4.2.3 Holographic multiple imaging
4.4.2.4 Space-invariant multiple imaging
4.4.2.5 Space-variant interconnects
4.5 Dynamic interconnects
4.5.1 Optical crossbars
4.5.2 Optical multistage interconnection networks (MINs)
4.5.2.1 Optical Omega network
4.5.2.2 Optical Clos networks
4.5.2.3 Optical Crossover networks
4.6 Massively parallel dynamic holographic interconnections
4.6.1 N4 weighted interconnections
4.6.2 Dynamic volume holographic interconnections
4.7 Devices and system considerations for free-space interconnections
4.7.1 Surface-emitting lasers and multiple quantum-well mode
4.7.2 Array illuminator
4.7.3 Alignment
4.8 References
Chapter 5. Neural Nets
5.1 General neural net architectures
5.2 Optical neural nets
5.2.1 Vector-matrix architectures
5.2.2 Optical correlator neural nets
5.2.2.1 Invariant recognition with Fourier-Mellin filters
5.2.3 Hologram-based neural nets
5.2.4 Holographic associative memory using red light
5.2.5 Neurocomputers using optical disks
5.2.6 Optical neuron
5.2.7 Ring resonator neural net
5.2.8 Neural nets using quantum-well-based devices
5.2.9 Neural nets using SEED devices
5.2.10 Photoconductive neural nets
5.2.11 Phototransistor neural net
5.2.12 Stochastic optical learning machine
5.2.13 Hughes programmable multilayer optical neural net
5.2.14 All-optical continuous time network
5.2.15 Optical pseudo-inverse associative memories
5.2.16 Optical neural net using back-propagation learning
5.2.17 Optical association
5.2.18 Versatile learning optical neural nets
5.2.19 Incoherent optical neurons
5.2.20 Programmable quadratic associative memory
5.2.21 Optical neural net with short-term-long-term memory
5.2.22 Second-generation neural nets
5.2.23 Time-division optical neural net
5.2.24 Recognition of alphabet
5.2.25 Learning in perceptron-like neural nets
5.2.26 Optical neural net for matrix inversion
5.2.27 Invariant classification with an all-optical system
5.2.28 Image classification using a ring-wedge detector
5.2.29 Learning in multilayer neural nets
5.2.30 CMU hybrid neural network
5.3 Applications of neural nets
5.3.l Learning systems
5.3.2 Pattern recognition
5.3.2.l Image analysis
5.3.2.2 Nondestructive testing
5.3.2.3 Robot vision
5.3.3 Robot control
5.3.4 Knowledge data bases
5.3.5 Optimization computation
5.3.6 Probabilistic reasoning
5.3.7 Medical applications
5.3.8 Applications in Economics
5.4 Defense applications of neural nets
5.4.1 The Hughes research program
5.4.1.1 Classification of artillery types
5.4.1.2 Defense of assets with limited resources
5.4.1.3 Multi-sensor passive track initiation
5.4.2 Sensor fusion
5.4.3 Target tracking and discrimination
5.4.4 Signal processing
5.4.5 Autonomous robotics
5.4.6 War-gaming
5.4.7 Guidance
5.4.8 Reconnaissance
5.4.9 Preselection of data for intelligence
5.4.10 Data compression
5.4.11 Autopilot
5.4.12 Communications satellite management
5.5 References
Chapter 6. The Future of Optical Computing
6.2 Conclusion
Index |

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