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Compact Physical Implementation of Spiking Neural Network Using Ambipolar WSe2n-Type/p-Type Ferroelectric Field-Effect Transistor

Authors :
Huo, Jiali
Li, Lingqi
Zheng, Haofei
Gao, Jing
Tun, Thaw Tint Te
Xiang, Heng
Ang, Kah-Wee
Source :
ACS Nano; October 2024, Vol. 18 Issue: 41 p28394-28405, 12p
Publication Year :
2024

Abstract

Spiking neural networks (SNNs) are attracting increasing interests for their ability to emulate biological processes, offering energy-efficient computation and event-driven processing. Currently, no devices are known to combine both neuronal and synaptic functions. This study presents an experimental demonstration of an ambipolar WSe2n-type/p-type ferroelectric field-effect transistor (n/p-FeFET) integrated with ferroelectric Hf0.5Zr0.5O2(HZO) to achieve both volatile and nonvolatile properties in a single device. The nonvolatile n-FeFET, driven by the stable ferroelectric properties of HZO, exhibits highly linear synaptic behavior. In contrast, the volatile p-FeFET, influenced by electron self-compensation in the ambipolar WSe2, enables self-resetting leaky-integrate-and-fire neurons. Integrating neuronal and synaptic functions in the same device allows for compact neuromorphic computing applications. Additionally, simulations of SNNs using experimentally calibrated synaptic and neuronal models achieved a 93.8% accuracy in MNIST digit recognition. This innovative approach advances the development of SNNs with high biomimetic fidelity and reduced hardware costs.

Details

Language :
English
ISSN :
19360851 and 1936086X
Volume :
18
Issue :
41
Database :
Supplemental Index
Journal :
ACS Nano
Publication Type :
Periodical
Accession number :
ejs67593973
Full Text :
https://doi.org/10.1021/acsnano.4c11081