We demonstrate the operation and rapid reconfiguration of a 12x12 Acousto-Optic Photonic Crossbar (AOPC). This AOPC can implement any desired permutation, fan-in, or fan-out interconnection between any subset out of twelve single-mode input fibers into any subset out of twelve single-mode output fibers. The system uses one large-aperture Acousto-Optic Deflector (AOD) driven by a sum-of-tones RF-waveform produced by an arbitrary waveform generator ( ARB) and computed from an experimentally measured lookup table, thus reducing the control complexity of the system. The design, based on the momentum-space technique, includes optical and acoustical rotation for the AOD, in order to optimize the efficiency of the desired interconnections and minimize the undesirable negative first-order acoustooptic Bragg-diffractions. A limitation in this type of systems is the unavoidable reconfiguration (dead) time introduced by the AOD itself, which can result in crosstalk between the individual input channels during that period oftime. In this paper, we experimentally investigate the reconfiguration time ofthis AOPC, by switching between two different crossbar patterns, and then measuring the time during which the detected signal can not be individually resolved for each input channel. Coupling efficiency problems and alignment procedures are also discussed and analyzed.
A neural network based system to identify images transmitted through a Coherent Fiber-optic Bundle (CFB) is presented. Patterns are generated in a computer, displayed on a Spatial Light Modulator (SLM), imaged onto the input face of the CFB, and recovered optically by a CCD sensor array for further processing. Input and output optical subsystems were designed and used to that end. The recognition step of the transmitted patterns is made by a powerful, widely-used, neural network simulator running on the control PC. A complete PC-based interface was developed to control the different tasks involved in the system. An optical analysis of the system capabilities was carried out prior to performing the recognition step. Several neural network topologies were tested, and the corresponding numerical results are also presented and discussed.
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