Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
This work enables task-specific optical components for applications like optical machine learning, diverging from traditional design methods, but it is incremental as it builds on existing diffractive neural network concepts.
The authors tackled the problem of designing optical systems for specific tasks by developing a broadband diffractive neural network that processes a continuum of wavelengths from an incoherent source, experimentally validating it with seven multi-layer systems to create tunable spectral filters and wavelength de-multiplexing.
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.