Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification

Rengjian Yu, Lihua He, Changsong Gao, Xianghong Zhang, Enlong Li, Tailiang Guo, Wenwu Li () and Huipeng Chen ()
Additional contact information
Rengjian Yu: National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
Lihua He: National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
Changsong Gao: National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
Xianghong Zhang: National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
Enlong Li: Department of Materials Science, Fudan University
Tailiang Guo: National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
Wenwu Li: Department of Materials Science, Fudan University
Huipeng Chen: National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-34565-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34565-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-34565-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().