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We observe guided blue and green upconversion fluorescence in an ion-exchanged waveguide that is fabricated in an erbium-ytterbium-codoped silicate laser glass. The guides are optically pumped at room temperature by using a Ti:sapphire laser with lambda = 880-980 nm; the observed fluorescence is in two bands, 480-490 and 520-560 nm, and results from multiphoton excitation.
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We report a technique of integrating planar Fresnel microlenses with InGaAs/GaAs-based vertical-cavity surfacee-mitting laser arrays by selectively ion-beam milling the substrate. Depending on the application, one can focus, collimate, and bend the individual laser beams using such microlenses. An example is presented where a 32 x 32 array of microlenses, each with an aperture of 80microm and a focal length of 108 microm is integrated with a laser array. As expected, arrays of focused beams, each with a 2-microm spot size, are generated at a distance of approximately 110 microm.
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A compact and robust holographic correlator using a vertical-cavity surface-emitting microlaser diode array is described. The system is based on the unique coherence property of the surface-emitting microlaser array: temporally highly coherent and spatially incoherent. The performance of the system is experimentally demonstrated, and the application of the system for neural network implementations is proposed.
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High-order associative memories are of interest because of their potentially large storage capacity. We describe an optoelectronic implementation of a second-order, or quadratic, associative memory. The two-layer network is based on an inner-product representation, which allows for a compact implementation using an optical chip design. Experimental results obtained from our system are discussed.
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We describe a holographic associative memory that is able to identify individual words and insert word breaks in a concatenated word string. The system is shift invariant and has error-correction capability. Experimental results obtained from the system are presented.
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The effect of discretizing interconnection weight strengths in an optoelectronic learning neural network based on the backpropagation algorithm is investigated. We discuss how discretization arises in such an implementation. Using computer simulations we find that learning performance, as tested on the two-input XOR problem, is poor but that the addition of noise to the system results in substantial improvement.
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We describe the first reported experimental observation of an extremely fast shift of the n = 1 exciton transition energy in GaAs quantum-well heterostructures. The shift is produced by optical pumping below the band gap and is not associated with a carrier or exciton population. We interpret the shift in terms of an optical Stark effect. We present a model for the Stark effect on the ground-state exciton in quantum wells and find good agreement between the predictions of the model and our experimental results.