Eurekalert July 26, 2018
Researchers at UCLA have developed an optical deep learning framework called Diffractive Deep Neural Network (D2NN), that consists of layers of 3-D-printed, optically diffractive surfaces that work together to process information. Each point on a given layer either transmits or reflects an incoming wave, which represents an artificial neuron that is connected to other neurons of the following layers through optical diffraction. By altering the phase and amplitude, each “neuron” is tunable. They demonstrated that after training the system on the handwritten digits, D2NN could recognise the numbers with 95.08% accuracy. According to the researchers the system could easily be scaled up by using different 3-D fabrications methods, optical components, and detection systems… read more. TECHNICAL ARTICLE