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IMAGE: (Left) A D2NN ensemble, constituting 14 individual diffractive networks that have different types of filters placed between the object plane and the first diffractive layer. The ensemble class score comes. view more
Credit: by Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu, Yair Rivenson and Aydogan Ozcan
Recently there has been a reemergence of interest in optical computing platforms for artificial intelligence-related applications. Optics/photonics is ideally suited for realizing neural network models because of the high speed, large bandwidth and high interconnectivity of optical information processing. Introduced by UCLA researchers, Diffractive Deep Neural Networks (D2NNs) constitute such an optical computing framework, comprising successive transmissive and/or reflective diffractive surfaces that can process input information through light-matter interaction. These surfaces are designed using standard deep learning techniques in a computer, which a