Phys.org August 1, 2024 Researchers at UC California developed a pyramid-structured diffractive optical network design (P-D2NN), optimized specifically for unidirectional image magnification and demagnification. The diffractive layers were pyramidally scaled in alignment with the direction of the image magnification or demagnification, to inhibit image formation in the opposite direction, thus achieved the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. The design maintained its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths. It allowed a unidirectional magnifier and a unidirectional demagnifier operation simultaneously in opposite directions, […]
Tag Archives: Optical network
The ultimate combination: A 3D-printed optical deep learning network
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 […]