title: Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol.
abstract: Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.
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: Bongsik
authors.initials: B
authors.lastname: Choi
authors.affiliation: Mechatronics R&D Center, Samsung Electronics , Gyonggi-do 18448, Korea.
authors.forename: Meehyun
authors.initials: M
authors.lastname: Lim
authors.affiliation: School of Electrical Engineering, Kookmin University , Seoul 02707, Korea.
authors.forename: Jinsu
authors.initials: J
authors.lastname: Yoon
authors.affiliation: School of Electrical Engineering, Kookmin University , Seoul 02707, Korea.
authors.forename: Juhee
authors.initials: J
authors.lastname: Lee
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.identifier: 0000-0003-1301-2847
authors.initials: SJ
authors.lastname: Choi
keywords: analog switching; carbon nanotube; neuromorphic system; pattern recognition; synaptic transistor; weight update