Retina‐Inspired Bi‐Based Terahertz Photonic Neuromorphic Devices
The human retina capable of extracting key feature information is a crucial sensory element in the visual system. Constructing bionic devices with multitype feedback to emulate the retina behaviors in complex environments has been a persistent pursuit to broaden the visual range. However, the definition and regulation of synaptic weights persist as a bottleneck problem in the terahertz (THz) devices to achieve neuromorphic function. Here, we have proposed bismuth-based THz photonic neuromorphic devices with picosecond short-term plasticity and constructed the THz-optical neural network (THz-ONN) to imitate retina function. Crucially, the weight embodied by THz photoresponse can be precisely regulated via incremental optical pulses, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable plasticity. Further development of diverse neuromorphic devices for various scenarios could be realized through the combination of band alignment engineering and substrate effects to control photocarrier transport. The corresponding neuromorphic computing based on THz-ONN indicates the high recognition accuracy of hardware. The present study provides an exciting paradigm for the realization of THz neuromorphic devices and opens an avenue for mimicking biological sensory system.
Authors
- Mengyuan Wang
- Donggang Xie (ORCID: https://orcid.org/0000-0002-7832-0800)
- Longyu Shi
- M. H. Li
- Cunlin Zhang Cunlin Zhang
- Guangwei She (ORCID: https://orcid.org/0000-0003-1017-5456)
- Wensheng Shi (ORCID: https://orcid.org/0000-0001-5747-4862)
- Kuijuan Jin
- P Wang
- Yu Wu
- Guozhen Yang
- Huiwen Shi
- Chen Ge
- Pujing Zhang
Institutions
- Beijing Normal University (CN)
- FZU ‒ Institute of Physics of the Academy of Sciences of the Czech Republic (CZ)
- Technical Institute of Physics and Chemistry (CN)
- National Laboratory for Superconductivity (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2026-04-02
- DOI
- https://doi.org/10.1002/advs.75145
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Beijing Municipality
- National Key Research and Development Program of China