Neural morphology perception system based on antiferroelectric <scp>AgNbO<sub>3</sub></scp> neurons

Abstract Biologically inspired neuromorphic perceptual systems have great potential for efficient processing of multisensory signals from the physical world. Recently, artificial neurons constructed by memristor have been developed with good biological plausibility and density, but the filament‐type memristor is limited by undesirable temporal and spatial variations, high electroforming voltage and limited reproducibility and the Mott insulator type memristor suffer from large driving current. Here, we propose a novel antiferroelectric artificial neuron (AFEAN) based on the intrinsic polarization and depolarization of AgNbO 3 (ANO) antiferroelectric (AFE) films to address these challenges. The antiferroelectric memristor exhibits low power consumption (8.99 nW), excellent durability (~10 5 ) and high stability. Using such an AFEAN, a spike‐based antiferroelectric neuromorphic perception system (AFENPS) has been designed, which can encode light level and temperature signals into spikes, and further construct a spiking neural network (SNN) (784 × 196 × 10) for optical image classification and thermal imaging classification, achieving 95.34% and 95.76% recognition accuracy on the MNIST dataset, respectively. This work paves the way for the simulation of spiking neurons using antiferroelectric materials and promising a promising method for the development of highly efficient hardware for neuromorphic perception systems. image

Authors

Institutions

Publication Details

Journal
InfoMat
Published
2024-11-26
DOI
https://doi.org/10.1002/inf2.12637
Citations
11
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.30

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Neural morphology perception system based on antiferroelectric AgNbO3 neurons

Linxia Wang, Jiameng Sun, Zhongrong Wang, Zheng Shu-Kai et al.
11 citations
InfoMat
Advanced Memory and Neural Computing
2.30
article

Neural morphology perception system based on antiferroelectric AgNbO3 neurons

Linxia Wang, Jiameng Sun, Zhongrong Wang, Zheng Shu-Kai, Yifei Pei, Xiaobing Yan, Yong Sun, Yiduo Shao, Jianxin Guo, Yibo Fan, Jianhui Zhao, Zhenyu Zhou, Lei Zhao, Jiacheng Wang
article en
11 citations

Abstract

Abstract Biologically inspired neuromorphic perceptual systems have great potential for efficient processing of multisensory signals from the physical world. Recently, artificial neurons constructed by memristor have been developed with good biological plausibility and density, but the filament‐type memristor is limited by undesirable temporal and spatial variations, high electroforming voltage and limited reproducibility and the Mott insulator type memristor suffer from large driving current. Here, we propose a novel antiferroelectric artificial neuron (AFEAN) based on the intrinsic polarization and depolarization of AgNbO 3 (ANO) antiferroelectric (AFE) films to address these challenges. The antiferroelectric memristor exhibits low power consumption (8.99 nW), excellent durability (~10 5 ) and high stability. Using such an AFEAN, a spike‐based antiferroelectric neuromorphic perception system (AFENPS) has been designed, which can encode light level and temperature signals into spikes, and further construct a spiking neural network (SNN) (784 × 196 × 10) for optical image classification and thermal imaging classification, achieving 95.34% and 95.76% recognition accuracy on the MNIST dataset, respectively. This work paves the way for the simulation of spiking neurons using antiferroelectric materials and promising a promising method for the development of highly efficient hardware for neuromorphic perception systems. image

InfoMatVol. 7(3)
Hebei University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hebei Province, Program for Top 100 Innovative Talents in Colleges and Universities of Hebei Province
Affordable and clean energy
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
2.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.