Diffractive optical computing in free space

Hu, Jingtian; Mengu, Deniz; Tzarouchis, Dimitrios C.; Edwards, Brian; Engheta, Nader; Ozcan, Aydogan

doi:10.1038/s41467-024-45982-w

Title: Diffractive optical computing in free space

Journal Article · 20 February 2024 · Nature Communications

DOI: https://doi.org/10.1038/s41467-024-45982-w · OSTI ID:2309790

Hu, Jingtian ^[1]; Mengu, Deniz ^[1]; Tzarouchis, Dimitrios C. ^[2];

^[3]; Engheta, Nader ^[3];

^[1]

University of California, Los Angeles, CA (United States)
University of Pennsylvania, Philadelphia, PA (United States); Meta Materials Inc., Athens (Greece)
University of Pennsylvania, Philadelphia, PA (United States)

Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.

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Research Organization:: Univ. of California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); US Air Force Office of Scientific Research (AFOSR); Multidisciplinary University Research Initiative (MURI); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0023088; # FA9550-21-1-0312; # FA9550-23-1-0307

OSTI ID:: 2309790

Alternate ID(s):: OSTI ID: 2341437; OSTI ID: 2341445

Journal Information:: Nature Communications, Vol. 15, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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