Bio-inspired mid-infrared neuromorphic transistors for dynamic trajectory perception using PdSe2/pentacene heterostructure

Huaiyu Gao, Xiaoyong Jiang, Xinyu Ma, Minrui Ye, Jie Yang (), Junyao Zhang, Yangchen Gao, Tangxin Li, Hailu Wang, Jian Mei, Xiao Fu, Xu Liu, Tongrui Sun, Ziyi Guo, Pu Guo, Fansheng Chen, Kai Zhang, Jinshui Miao (), Weida Hu and Jia Huang ()
Additional contact information
Huaiyu Gao: Tongji University
Xiaoyong Jiang: Chinese Academy of Sciences
Xinyu Ma: Chinese Academy of Sciences
Minrui Ye: ShanghaiTech University
Jie Yang: Tongji University
Junyao Zhang: Tongji University
Yangchen Gao: Tongji University
Tangxin Li: Chinese Academy of Sciences
Hailu Wang: Chinese Academy of Sciences
Jian Mei: Chinese Academy of Sciences
Xiao Fu: Chinese Academy of Sciences
Xu Liu: Tongji University
Tongrui Sun: Tongji University
Ziyi Guo: Tongji University
Pu Guo: Tongji University
Fansheng Chen: Chinese Academy of Sciences
Kai Zhang: Chinese Academy of Sciences
Jinshui Miao: Chinese Academy of Sciences
Weida Hu: Chinese Academy of Sciences
Jia Huang: Tongji University

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

Abstract: Abstract Mid-infrared (MIR) intelligent sensing technology is essential for precise identification and tracking for dynamic target detection in challenging and low-visibility environments. However, existing MIR vision systems based on traditional von Neumann architecture face significant delays and inefficiencies due to the separation of sensing, memory, and processing units. Neuromorphic motion devices offer better tracking capabilities, but most studies are limited to the near-infrared spectrum. Inspired by the fire beetle’s MIR sensing capabilities, we have developed an MIR neuromorphic device using a 2D inorganic/organic heterostructure. The device exhibits biological synaptic behavior in the MIR region (up to 4.25 μm) based on the persistent photoconductivity (PPC) effect, successfully realizing the function of dynamic trajectories memorization with real-time hardware implementation. Additionally, a reservoir computing (RC) system trained on an MIR flame motion dataset achieves a recognition accuracy of 94.79% in classifying flame motion direction. While the research on MIR neuromorphic devices is limited, this study underscores the potential of such devices to advance MIR-based machine vision applications.

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

Downloads: (external link)
https://www.nature.com/articles/s41467-025-60311-5 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_s41467-025-60311-5

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

DOI: 10.1038/s41467-025-60311-5

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 ().