  We report a broadband diffractive optical neural network design that
simultaneously processes a continuum of wavelengths generated by a
temporally-incoherent broadband source to all-optically perform a specific task
learned using deep learning. We experimentally validated the success of this
broadband diffractive neural network architecture by designing, fabricating and
testing seven different multi-layer, diffractive optical systems that transform
the optical wavefront generated by a broadband THz pulse to realize (1) a
series of tunable, single passband as well as dual passband spectral filters,
and (2) spatially-controlled wavelength de-multiplexing. Merging the native or
engineered dispersion of various material systems with a deep learning-based
design strategy, broadband diffractive neural networks help us engineer
light-matter interaction in 3D, diverging from intuitive and analytical design
methods to create task-specific optical components that can all-optically
perform deterministic tasks or statistical inference for optical machine
learning.
