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Award ID contains: 2211990

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  1. ; ; (, IEEE)
    A feedforward optical neural network for image recognition is demonstrated through free-space optics in which the nonlinear activation function is provided by a resonant semiconductor saturable absorption mirror (RSAM). Although free-space optics with liquid crystal based spatial light modulator has the potential to allow parallel processing for improved performance, using a fiber-optic electrooptic modulator for parallel to serial conversion greatly simplifies the experimental setup. We show that this nonlinear activation function introduced by RSAM can provide a notable improvement of 8.1% to classification accuracy in comparison to a purely linear network when tested with the MNIST data set for image classification. The impact of noise is also investigated in system implementation. 
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    Free, publicly-accessible full text available July 6, 2026
  2. ; ; ; ; (, IEEE)
    A physics-aware simulation platform is proposed for optical neural networks (ONNs), incorporating phase-only modulation and passive free-space propagation. The platform enables end-to-end training under experimentally realistic constraints, with both the phase mask and propagation distance treated as learnable parameters. To facilitate classification, structured 2D output patterns are introduced, where each label corresponds to a fixed spatial light spot. When evaluated with the MNIST dataset, the system achieves 94.6% accuracy using a single phase modulation layer, demonstrating the effectiveness of spatial encoding in physically plausible ONNs. 
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    Free, publicly-accessible full text available July 6, 2026