LSPR‐Enhanced Reconfigurable NIR Organic Synapse for Neuromorphic Computing and Biomedical Sensing
ABSTRACT Light‐controlled memristors are a key technology for overcoming the computational limits of von Neumann architectures, offering great potential for next‐generation neuromorphic computing. Organic optical synapses offer a promising route toward low‐power, multifunctional neuromorphic hardware, yet their weak response to near‐infrared (NIR) light limits applications in biomedical sensing and intelligent vision. Here, a reconfigurable NIR synaptic device based on an organic heterojunction (ITO/PEDOT:PSS:AgTNPs/PM6:L8‐BO/PFN‐Br/Ag) is demonstrated, in which silver triangular nanoplates (AgTNPs) embedded in the poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) layer amplify NIR absorption and enhance synaptic performance. The resulting device exhibits reconfigurable synaptic plasticity—including short‐term memory (STM), long‐term memory (LTM), and paired‐pulse facilitation (PPF)—and achieves an ultralow operating energy of 2.7 fJ per event, among the lowest reported for NIR organic synapses. Beyond mimicking biological learning behaviors, the device performs optical encoding/decoding of Morse code and enables high‐accuracy neuromorphic tasks, achieving 98.9% accuracy in Modified National Institute of Standards and Technology database (MNIST) recognition and 94.2% accuracy in electrocardiogram (ECG) recognition using a convolutional neural network (CNN). Mechanistic analysis and finite element method (FEM) simulations confirm that localized surface plasmon resonance (LSPR)‐mediated photonic field enhancement increases the probabilities of exciton generation and dissociation, accounting for the improved NIR responsivity. This work establishes a practical strategy for developing NIR‐responsive neuromorphic and biomedical sensing platforms.
Authors
- X G Wang
- Peng Gao (ORCID: https://orcid.org/0000-0002-4963-2282)
- Hong Lian (ORCID: https://orcid.org/0000-0001-5884-4917)
- Shuanglong Wang (ORCID: https://orcid.org/0000-0002-7016-791X)
- Jiepei Cao (ORCID: https://orcid.org/0000-0002-3459-202X)
- Xingdong Ding
- Guotao Lan
- Zhaoxin Xu
- Shihui Dong
- Jiahui Ding
- Qingchen Dong
Institutions
- Shanghai University (CN)
- Hong Kong Polytechnic University (HK)
- City University of Hong Kong (HK)
- Fujian Institute of Research on the Structure of Matter (CN)
- University of Technology Malaysia (MY)
Publication Details
- Journal
- Advanced Optical Materials
- Published
- 2026-07-23
- DOI
- https://doi.org/10.1002/adom.71477
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00