Abstract

I will discuss the integration of programmable diffraction with digital neural networks. Diffractive 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 networks have various applications, e.g., image analysis, feature detection, object classification and seeing through diffusers. These diffractive systems can broadly impact (1) optical statistical inference engines, (2) computational camera/microscope designs and (3) inverse design of task-specific optical systems. I will give examples of each group, enabling transformative capabilities for applications in, e.g., autonomous systems, defense/security, telecommunications, and biomedical imaging.