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Related Experiment Video

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Transport of Surface-modified Carbon Nanotubes through a Soil Column
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Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing.

Ivan Sanchez Esqueda1, Xiaodong Yan2, Chris Rutherglen3

  • 1Information Sciences Institute , University of Southern California , Marina del Rey , California 90292 , United States.

ACS Nano
|June 27, 2018
PubMed
Summary

Aligned carbon nanotube (CNT) synaptic transistors offer improved performance for neuromorphic computing. These devices exhibit robust, tunable synaptic behavior suitable for large-scale artificial neural networks.

Keywords:
carbon nanotubemachine learningneuromorphicsynapsetransistor

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Area of Science:

  • Materials Science
  • Nanoelectronics
  • Computer Engineering

Background:

  • Neuromorphic computing aims to mimic the human brain's structure and function.
  • Conventional memristor technologies for neuromorphic systems often face limitations like abrupt conductance transitions and small dynamic ranges.
  • Carbon-based nanoelectronics offer potential for advanced computing functionalities.

Purpose of the Study:

  • To develop and characterize aligned carbon nanotube (CNT) synaptic transistors for large-scale neuromorphic computing.
  • To leverage charge-trapping effects in CNT field-effect transistors (FETs) for analog synaptic behavior.
  • To demonstrate the advantages of CNT synaptic devices over existing memristor technologies.

Main Methods:

  • Fabrication of top-gated CNT FETs with a high-k dielectric layer for charge trapping.
  • Utilizing wafer-level techniques for producing highly aligned CNT arrays with high semiconducting purity.
  • Conducting pulsed experiments to assess device robustness, nonvolatility, and uniformity.
  • Simulating unsupervised learning for pattern recognition using a spike-timing-dependent-plasticity (STDP) scheme.

Main Results:

  • Demonstrated gradual analog programmability of CNT channel conductance with a large dynamic range via charge trapping.
  • Achieved exceptional uniformity, robustness, and nonvolatility in aligned CNT FET synaptic behavior.
  • Confirmed CMOS compatibility of the CNT fabrication process, enabling large-scale integration.
  • Showcased fine-tunability of synaptic behavior for adaptive learning and neuron regulation.
  • Simulations indicated improved learning rates and pattern recognition accuracy using tuned CNT synapses.

Conclusions:

  • Aligned CNT synaptic transistors present a promising alternative to conventional memristors for neuromorphic computing.
  • The demonstrated uniformity, robustness, and CMOS compatibility pave the way for practical, large-scale CNT+CMOS neuromorphic systems.
  • Tunable synaptic characteristics of CNT devices enable advanced functionalities like online learning and homeostatic regulation.