title: Carbon Nanotube Synaptic Transistor Network for Pattern Recognition.
abstract: Inspired by the human brain, a neuromorphic system combining complementary metal-oxide semiconductor (CMOS) and adjustable synaptic devices may offer new computing paradigms by enabling massive neural-network parallelism. In particular, synaptic devices, which are capable of emulating the functions of biological synapses, are used as the essential building blocks for an information storage and processing system. However, previous synaptic devices based on two-terminal resistive devices remain challenging because of their variability and specific physical mechanisms of resistance change, which lead to a bottleneck in the implementation of a high-density synaptic device network. Here we report that a three-terminal synaptic transistor based on carbon nanotubes can provide reliable synaptic functions that encode relative timing and regulate weight change. In addition, using system-level simulations, the developed synaptic transistor network associated with CMOS circuits can perform unsupervised learning for pattern recognition using a simplified spike-timing-dependent plasticity scheme.
authors.affiliation: Department of Electrical Engineering, Sejong University , Seoul 05006, Korea.
authors.forename: Sungho
authors.initials: S
authors.lastname: Kim
authors.affiliation: School of Electrical Engineering, Kookmin University , Seoul 02707, Korea.
authors.forename: Jinsu
authors.initials: J
authors.lastname: Yoon
authors.affiliation: Department of Electrical Engineering, Sejong University , Seoul 05006, Korea.
authors.forename: Hee-Dong
authors.initials: HD
authors.lastname: Kim
authors.affiliation: School of Electrical Engineering, Kookmin University , Seoul 02707, Korea.
authors.forename: Sung-Jin
authors.initials: SJ
authors.lastname: Choi
mesh_terms: D003198:Computer Simulation; D004553:Electric Conductivity; D037742:Nanotubes, Carbon / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014173:Transistors, Electronic
chemical_list: D037742:Nanotubes, Carbon
keywords: analog switching; carbon nanotube; neuromorphic system; pattern recognition; synaptic device; transistor