Technology.org March 5, 2020 Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Previous diffractive approaches employed monochromatic coherent light as the illumination source. Researchers at UCLA designed a broadband diffractive optical neural network that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. They fabricated and tested seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize a series of tunable, single-passband and […]
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All-optical diffractive neural network closes performance gap with electronic neural networks
Science Daily August 13, 2019 Optical computing provides unique opportunities in terms of parallelization, scalability, power efficiency, and computational speed and has attracted major interest for machine learning. Researchers at UCLA have demonstrated systematic improvements in diffractive optical neural networks, based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity. Using this differential detection scheme, involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons, they numerically achieved blind testing accuracies of 98.54%, 90.54%, and 48.51% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 datasets, respectively. By utilizing the inherent parallelization capability […]