  We introduce an all-optical Diffractive Deep Neural Network (D2NN)
architecture that can learn to implement various functions after deep
learning-based design of passive diffractive layers that work collectively. We
experimentally demonstrated the success of this framework by creating
3D-printed D2NNs that learned to implement handwritten digit classification and
the function of an imaging lens at terahertz spectrum. With the existing
plethora of 3D-printing and other lithographic fabrication methods as well as
spatial-light-modulators, this all-optical deep learning framework can perform,
at the speed of light, various complex functions that computer-based neural
networks can implement, and will find applications in all-optical image
analysis, feature detection and object classification, also enabling new camera
designs and optical components that can learn to perform unique tasks using
D2NNs.

Neural and Evolutionary Computing;Machine Learning;Computational Physics;Optics