In situ training of an in-sensor artificial neural network based on ferroelectric photosensors

Haipeng Lin, Jiali Ou, Zhen Fan (), Xiaobing Yan (), Wenjie Hu, Boyuan Cui, Jikang Xu, Wenjie Li, Zhiwei Chen, Biao Yang, Kun Liu, Linyuan Mo, Meixia Li, Xubing Lu, Guofu Zhou, Xingsen Gao and Jun-Ming Liu
Additional contact information
Haipeng Lin: South China Normal University
Jiali Ou: South China Normal University
Zhen Fan: South China Normal University
Xiaobing Yan: Hebei University
Wenjie Hu: South China Normal University
Boyuan Cui: South China Normal University
Jikang Xu: Hebei University
Wenjie Li: South China Normal University
Zhiwei Chen: South China Normal University
Biao Yang: Hebei University
Kun Liu: South China Normal University
Linyuan Mo: South China Normal University
Meixia Li: South China Normal University
Xubing Lu: South China Normal University
Guofu Zhou: South China Normal University
Xingsen Gao: South China Normal University
Jun-Ming Liu: South China Normal University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast ( 4 bits) photoresponses, as well as long retention (50 days), high endurance (109), high write speed (100 ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-55508-z 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:16:y:2025:i:1:d:10.1038_s41467-024-55508-z

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

DOI: 10.1038/s41467-024-55508-z

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 ().