Design of task-specific optical systems using broadband diffractive neural networks

Abstract

Deep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, 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 tuneable, single-passband and 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 the 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.Optical computing: Harnessing a rainbow with diffractive neural networksAn optical machine learning framework termed Diffractive Deep Neural Networks has been expanded to compute a variety of desired tasks through broadband light and simultaneous processing of a wide and continuous range of wavelengths. Aydogan Ozcan and colleagues at the University of California, Los Angeles, USA, incorporated the wavelength-dependent nature of light–matter interaction occurring at multiple layers of a diffractive network to learn and extract input information at increasing levels of complexity. Simultaneously analyzing and processing light across many wavelengths present unique opportunities to enhance the inference and generalization capabilities of diffractive optical networks to perform machine learning tasks, e.g. all-optical object recognition, as well as to design deterministic and task-specific optical components, expanding the optical design space beyond human intuition.