Diffractive optical computing in free space
Abstract 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 o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45982-w |
| _version_ | 1797274098795020288 |
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| author | Jingtian Hu Deniz Mengu Dimitrios C. Tzarouchis Brian Edwards Nader Engheta Aydogan Ozcan |
| author_facet | Jingtian Hu Deniz Mengu Dimitrios C. Tzarouchis Brian Edwards Nader Engheta Aydogan Ozcan |
| author_sort | Jingtian Hu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T14:53:32Z |
| format | Article |
| id | doaj.art-732c75724b10402d816ed08afbd8894d |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-07T14:53:32Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-732c75724b10402d816ed08afbd8894d2024-03-05T19:33:32ZengNature PortfolioNature Communications2041-17232024-02-0115112110.1038/s41467-024-45982-wDiffractive optical computing in free spaceJingtian Hu0Deniz Mengu1Dimitrios C. Tzarouchis2Brian Edwards3Nader Engheta4Aydogan Ozcan5Electrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Systems Engineering, University of PennsylvaniaElectrical and Systems Engineering, University of PennsylvaniaElectrical and Systems Engineering, University of PennsylvaniaElectrical and Computer Engineering Department, University of CaliforniaAbstract 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.https://doi.org/10.1038/s41467-024-45982-w |
| spellingShingle | Jingtian Hu Deniz Mengu Dimitrios C. Tzarouchis Brian Edwards Nader Engheta Aydogan Ozcan Diffractive optical computing in free space Nature Communications |
| title | Diffractive optical computing in free space |
| title_full | Diffractive optical computing in free space |
| title_fullStr | Diffractive optical computing in free space |
| title_full_unstemmed | Diffractive optical computing in free space |
| title_short | Diffractive optical computing in free space |
| title_sort | diffractive optical computing in free space |
| url | https://doi.org/10.1038/s41467-024-45982-w |
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