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An optical associative neural network with a stochastic thresholding procedure has been demonstrated. The use of stochastic processing drastically improved the convergence rate into the correct global minima (recognition rate). The properties of undesirable spurious minima were also investigated. It was found that the spurious minima were represented as the mixed states of the stored vectors. A useful method to estimate the required noise level to vanish the spurious minima is described.

A GaAs/AlGaAs optical synaptic interconnection device for neural networks is reported for the first time to our knowledge. This device consists of a light-emitting-diode array, an interconnection matrix, and a photodiode array, which are integrated into a hybrid-layered structure on a GaAs substrate. The device structure and characteristics are reported in detail. The fabricated device can simulate a 32-neuron system. Experimental results of the Hopfield associative memory with three stored vectors are also described.