Binary Correlators for Optical Computing and Pattern Recognition

The matrix-vector multiplier is an important building block in optical information processing architectures, examples of which are correlators for pattern recognition, associative memories, and neural networks. Such architectures are most suitable for implementation by optics due to the ease in realizing dense interconnections optically. The success of the implementation partially relies on the quality of the SLM used to record the information for processing. Limited dynamic range for the representation of the data recorded is a common drawback suffered by most commercially available devices. In this thesis, the importance of the dynamic range of the device on the performance of the implementation is investigated. The effect of limited dynamic range on the signal to noise ratio, probability of error, capacity, and training of various forms of matrix-vector multipliers are addressed. Through the use of theoretical analyses, computer simulations, and optical experiments, it will be shown that a large dynamic range is not essential in most applications. Specifically, it is shown that only one bit of dynamic range, i.e. two gray levels, for the representation of each data point, results in acceptable loss in performance.