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

Abstract

Deep learning has been transformative in many fields, also 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 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.

Impact of fabrication errors and refractive index on multilevel diffractive lens performance

“…Various research groups have designed achromatic MDLs via careful parametric optimization of the lens surface topography in the visible 3,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] , NIR 26 , SWIR 27 , LWIR 28 , THz 29,30 and microwave 31 bands. In fact, we have recently shown the design of a single achromatic MDL with a focal length of 18 mm and aperture of ~ 1 mm, operating across a continuous spectrum of wavelengths from 450 nm to 15 μm 32,33 .…”

Misalignment resilient diffractive optical networks

“…Furthermore, deep learning and related optimization tools have been harnessed to find data-driven solutions for various inverse problems arising in, e.g., microscopy [18][19][20][21][22], nanophotonic designs and plasmonics [23][24][25]. These demonstrations and others have been motivating some of the recent advances in optical neural networks and related optical computing techniques that aim to exploit the computational speed, power-efficiency, scalability and parallelization capabilities of optics for machine intelligence applications [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

“…Toward this broad goal, Diffractive Deep Neural Networks (D 2 NN) [36][37][38][39] have been introduced as a machine learning framework that unifies deep learning-based training of matter with the physical models governing light propagation to enable all-optical inference through a set of diffractive layers. The training stage of a diffractive network is performed using a computer and relies on deep learning and error backpropagation methods to tailor the light-matter interaction across a set of diffractive layers that collectively perform a given machine learning task, e.g., object classification.…”

Computer generated optical volume elements by additive manufacturing

“…The abovementioned studies used variants of generalized Gerchberg-Saxton algorithms [11]. Recently, algorithms that utilize the error backpropagation concept of deep neural networks have been used to calculate multi-layered diffractive elements to carry out different tasks including classification [12,13]. The output of such algorithms is a stack of phase masks that should satisfy the thin element approach.…”