Journal
LIGHT-SCIENCE & APPLICATIONS
Volume 11, Issue 1, Pages -Publisher
SPRINGERNATURE
DOI: 10.1038/s41377-022-00752-5
Keywords
-
Categories
Funding
- National Natural Science Foundation of China [12134013, 61875181, 62122072, 12174368]
- CAS Pioneer Hundred Talents Program, the Fundamental Research Funds for the Central Universities in China
- USTC Research Funds of the Double First-Class Initiative [YD2030002003]
- University of Science and Technology of China's Centre for Micro and Nanoscale Research and Fabrication
- Institute of Artificial Intelligence at Hefei Comprehensive National Science Center [21KT016]
- Anhui Science and Technology Department [18030801138]
Ask authors/readers for more resources
The study introduces an all-optical convolutional computing method utilizing a metasurface imager to modify kernel functions unlimitedly, enriching the toolkit of all-optical computing. This approach bridges multi-functionality and high-integration in all-optical convolutions.
Electronic digital convolutions could extract key features of objects for data processing and information identification in artificial intelligence, but they are time-cost and energy consumption due to the low response of electrons. Although massless photons enable high-speed and low-loss analog convolutions, two existing all-optical approaches including Fourier filtering and Green's function have either limited functionality or bulky volume, thus restricting their applications in smart systems. Here, we report all-optical convolutional computing with a metasurface-singlet or -doublet imager, considered as the third approach, where its point spread function is modified arbitrarily via a complex-amplitude meta-modulator that enables functionality-unlimited kernels. Beyond one- and two-dimensional spatial differentiation, we demonstrate real-time, parallel, and analog convolutional processing of optical and biological specimens with challenging pepper-salt denoising and edge enhancement, which significantly enrich the toolkit of all-optical computing. Such meta-imager approach bridges multi-functionality and high-integration in all-optical convolutions, meanwhile possessing good architecture compatibility with digital convolutional neural networks.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available