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In recent years there has been a great interest in neural networks, since neural networks are capable of performing pattern recognition, classification, decision, search, and optimization. A key element of most neural network systems is the massive number of weighted interconnections (synapses) used to tie relatively simple processing elements (neurons) together in a useful architecture. The inherent parallelism and interconnection capability of optics make it a likely candidate for the implementation of the neural network interconnection process. While there are several optical technologies worth exploring, this dissertation examines the capabilities and limitations of using fixed planar holographic interconnects in a neural network system. While optics is well suited to the interconnection task, nonlinear processing operations are difficult to implement in optics and better suited to electronic implementations. Therefore, a hybrid neural network architecture of planar interconnection holograms and opto-electronic neurons is a sensible approach to implementing a neural network. This architecture is analyzed. The interconnection hologram must accurately encode synaptic weights, have a high diffraction efficiency, and maximize the number of interconnections. Various computer generated hologram techniques are tested for their ability to produce the interconnection hologram. A new technique using the Gerchberg-Saxton process followed by a random-search error minimization produces the highest interconnect accuracy and highest diffraction efficiency of the techniques tested. The analysis shows that a reasonable size planar hologram has a capacity to connect 5000 neuron outputs to 5000 neuron inputs and that the bipolar synaptic weights can have an accuracy of approximately 5 bits. To demonstrate the concept of an opto-electronic neural network and planar holographic interconnects, a Hopfield style associative memory is constructed and shown to perform almost as well as an ideal system. |
