한빛사논문
Abstract
Sungho Kim†∥, Bongsik Choi‡∥, Meehyun Lim§∥, Jinsu Yoon‡, Juhee Lee‡, Hee-Dong Kim†, and Sung-Jin Choi*‡
† Department of Electrical Engineering, Sejong University, Seoul 05006, Korea
‡ School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
§ Mechatronics R&D Center, Samsung Electronics, Gyonggi-do 18448, Korea
*Corresponding Author
Author Contributions
∥These authors equally contributed to this work.
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.
Keywords : analog switching; carbon nanotube; neuromorphic system; pattern recognition; synaptic transistor; weight update
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