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Sorting-Free Carbon Nanotube Synaptic Transistors.

Donghee Shin1, Jaegyun Im1, Hoimin Kim2

  • 1School of Chemical Engineering, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|December 17, 2025
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Summary
This summary is machine-generated.

This study introduces unsorted carbon nanotube (CNT) synaptic transistors using ion gel gates. These devices enable energy-efficient neuromorphic computing without costly CNT sorting.

Keywords:
carbon nanotubesion gelneuromorphic computingsynaptic transistor

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Neuroscience

Background:

  • Neuromorphic devices require synaptic transistors with precise conductance modulation and scalability.
  • Carbon nanotubes (CNTs) offer desirable properties but require costly sorting due to structural heterogeneity.

Purpose of the Study:

  • To develop energy-efficient synaptic transistors using unsorted CNTs (UCNTs) for scalable neuromorphic applications.
  • To circumvent the need for CNT sorting by utilizing an ion gel gate with mobile ionic charges.

Main Methods:

  • Fabrication of UCNT-based synaptic transistors utilizing an ion gel gate.
  • Characterization of device performance, including ON/OFF ratio, synaptic functionalities, and endurance.
  • Operando Raman spectroscopy to confirm the doping mechanism.
  • Neuromorphic simulations using device parameters for MNIST recognition.

Main Results:

  • UCNT transistors achieved an ON/OFF ratio of ≈25 via anion-driven doping/de-doping.
  • Demonstrated key synaptic functionalities: short-term plasticity, linear potentiation/depression (50 states), paired-pulse facilitation (149%), and spike-frequency-dependent plasticity.
  • Achieved high endurance (>1000 cycles) and an MNIST recognition accuracy of 88.75% in simulations.

Conclusions:

  • This work presents a scalable and cost-effective method for fabricating CNT-based synaptic electronics.
  • The developed UCNT transistors show promise for practical neuromorphic computing applications.
  • Ion gel gating effectively addresses the challenge of CNT structural heterogeneity.