Integration of programmable diffraction with digital neural networks (Conference Presentation)

I will discuss the integration of programmable diffraction with digital neural networks. Diffractive optical networks are designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially engineered surfaces. These diffractive processors integrated with digital neural networks have various applications, e.g., image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components for spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) optical statistical inference engines, (2) computational camera and microscope designs and (3) inverse design of optical systems that are task-specific. In this talk, I will give examples of each group, enabling transformative capabilities for various applications of interest in e.g., autonomous systems, defense/security, telecommunications as well as biomedical imaging and sensing.