Enhanced Synaptic Behaviors in Chitosan Electrolyte-Based Electric-Double-Layer Transistors with Poly-Si Nanowire Channel Structures

In this study, we enhance the synaptic behavior of artificial synaptic transistors by utilizing nanowire (NW)-type polysilicon channel structures. The high surface-to-volume ratio of the NW channels enables efficient modulation of the channel conductance, which is interpreted as the synaptic weight....

Full description

Saved in:
Bibliographic Details
Published in:Biomimetics (Basel, Switzerland) Switzerland), 2023-09, Vol.8 (5), p.432
Main Authors: Lee, Dong-Hee, Kim, Hwi-Su, Park, Ki-Woong, Park, Hamin, Cho, Won-Ju
Format: Article
Language:English
Subjects:
Artificial intelligence
Behavior
Brain
Chitin
Chitosan
chitosan electrolyte
electric double layers
Electrolytes
Etching
Long term memory
Nanotechnology
nanowire channel
Nanowires
Neural networks
neuromorphic computing
Paired-pulse facilitation
Pattern recognition
Physiological aspects
Scanning electron microscopy
Synaptic depression
Synaptic strength
synaptic transistor
Thin films
Transistors
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, we enhance the synaptic behavior of artificial synaptic transistors by utilizing nanowire (NW)-type polysilicon channel structures. The high surface-to-volume ratio of the NW channels enables efficient modulation of the channel conductance, which is interpreted as the synaptic weight. As a result, NW-type synaptic transistors exhibit a larger hysteresis window compared to film-type synaptic transistors, even within the same gate voltage sweeping range. Moreover, NW-type synaptic transistors demonstrate superior short-term facilitation and long-term memory transition compared with film-type ones, as evidenced by the measured paired-pulse facilitation and excitatory post-synaptic current characteristics at varying frequencies and pulse numbers. Additionally, we observed gradual potentiation/depression characteristics, making these artificial synapses applicable to artificial neural networks. Furthermore, the NW-type synaptic transistors exhibit improved Modified National Institute of Standards and Technology pattern recognition rate of 91.2%. In conclusion, NW structure channels are expected to be a promising technology for next-generation artificial intelligence (AI) semiconductors, and the integration of NW structure channels has significant potential to advance AI semiconductor technology.
ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics8050432