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April 3, 2026ACS Applied Materials & Interfaces0 citations

Highly Reliable and Uniform Synaptic Transistors Enabled by a Polymer-Engineered Semiconducting Nanotube Network

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YKY.S. KimJSJaemin ShinSOS. K. OH

Key Points

  • The aim is to demonstrate a reliable synaptic transistor array using a polymer-engineered nanotube network for neuromorphic applications.
  • Development of an 8 × 8 synaptic transistor array
  • Utilization of PFDD-wrapped single-walled carbon nanotube (s-SWCNT) networks
  • Evaluation of electrical properties and conductance modulation
  • Validation of synaptic functions over 10,000 pulse endurance cycles
  • Achieved robust synaptic plasticity and diverse synaptic functions
  • Demonstrated low nonlinearity values of 2.08 and 2.95 for potentiation and depression
  • Attained a recognition accuracy of 90.26% in handwritten image classification

Abstract

In this study, we demonstrate an 8 × 8 synaptic transistor array based on a poly(9,9-di-n-dodecylfluorenyl-2,7-diyl) (PFDD)-wrapped semiconducting single-walled carbon nanotube (s-SWCNT) network. The PFDD polymer wrapping of the SWCNTs offers a synergistic combination for synaptic devices: high selectivity of sorting s-SWCNTs and energetically stable charge-trapping sites along the nanotube surface, enabling controlled conductance modulation. In addition, the simple polymer-SWCNT hybrid structure contributes to highly uniform electrical properties in the array. A reproducible conductance-tuning capability is demonstrated in a PFDD-SWCNT synaptic transistor, resulting in robust synaptic plasticity. Diverse synaptic functions, such as excitatory post-synaptic currents, short- and long-term memory, long-term potentiation (LTP) and depression (LTD), and paired-pulse facilitation, are emulated. The stable dynamic modulation of LTP/LTD in the PFDD-SWCNT synaptic device is validated over 10 000 pulse endurance cycles. Importantly, low nonlinearity values of 2.08 and 2.95 for potentiation and depression, respectively, with an asymmetric ratio of 63.8%, lead to a high recognition accuracy of 90.26% in the handwritten image classification task based on an artificial neural network simulation. The PFDD-SWCNT hybrid structure establishes a robust platform for high-precision and reliable neuromorphic circuitry.

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69cf5cd15a333a821460a6aahttps://doi.org/10.1021/acsami.5c26369
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