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March 14, 2026Aggregate4 citationsOpen Access

Plasmon‐Doped Organic Heterojunction Optoelectronic Synapses for Near‐Infrared Visual Memory and Neuromorphic Computing

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JCJiepei CaoHLHong LianXWXiang Wang

Key Points

  • This research aims to develop an optical synaptic device using plasmon-doped organic heterojunctions for advanced computing applications.
  • Designed two-terminal optical synaptic device with gold nanorods doped organic heterojunctions.
  • Characterized electrical properties using Hall effect measurements.
  • Implemented convolutional neural networks and convolutional spiking neural networks for training.
  • Enhanced light absorption capacity in the near-infrared region (808 nm).
  • Increased photogenerated excitons by 16.8% and exciton dissociation probability by 8.4%.
  • Achieved recognition accuracies up to 98.57% for handwritten digits and 92.01% for dynamic gestures.

Abstract

ABSTRACT The explosive growth of artificial intelligence has intensified demands for new computing paradigms beyond conventional von Neumann architectures. In response, brain‐inspired computing‐in‐memory technologies are emerging as a promising path forward. Here, we designed a two‐terminal optical synaptic device utilizing organic heterojunctions doped with gold nanorods (AuNRs), leveraging the electric field enhancement innate to the localized surface plasmon resonance (LSPR) effect. The device doped with 1 wt% AuNRs demonstrates a markedly enhanced light absorption capacity in the near‐infrared (NIR) region of 808 nm. The generation rate of photogenerated excitons increases by 16.8%, while the probability of exciton dissociation rises by 8.4%. The paired‐pulse facilitation (PPF) index reaches 114.6% (Δ t = 1 s), indicating heightened sensitivity to optical pulse parameters. Additionally, Hall effect measurements were performed to characterize the electrical properties of the PEDOT:PSS:AuNRs films. The carrier mobility of the doped films increased 20‐fold compared to pristine PEDOT:PSS due to electron injection from AuNRs. This enhanced mobility contributes to faster synaptic response and higher conductance tunability in the synapse device, further supporting its performance in neuromorphic computing tasks. Furthermore, we successfully simulated the dynamic “learning–forgetting–relearning” processes associated with human visual memory. By exploiting the tunable conductance of the optimized synaptic device, we implemented both convolutional neural networks (CNNs) and convolutional spiking neural networks (CSNNs) for weight updates. After 100 and 150 training epochs, the system achieved recognition accuracies up to 98.57% for handwritten digits and 92.01% for dynamic gestures. This work presents an effective plasmon‐doping approach to enhancing the performance of organic memristors and can be extended to other material systems.

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Cite This Study

Cao et al. (2026) studied this question.

synapsesocial.com/papers/69b4ad9a18185d8a3980110chttps://doi.org/10.1002/agt2.70319
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