Compact artificial neuron based on anti-ferroelectric transistor

Rongrong Cao, Xumeng Zhang, Sen Liu, Jikai Lu, Yongzhou Wang, Hao Jiang, Yang Yang, Yize Sun, Wei Wei, Jianlu Wang, Hui Xu, Qingjiang Li () and Qi Liu ()
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Rongrong Cao: National University of Defense Technology
Xumeng Zhang: Fudan University
Sen Liu: National University of Defense Technology
Jikai Lu: Chinese Academy of Sciences
Yongzhou Wang: National University of Defense Technology
Hao Jiang: Fudan University
Yang Yang: Chinese Academy of Sciences
Yize Sun: Chinese Academy of Sciences
Wei Wei: Chinese Academy of Sciences
Jianlu Wang: Fudan University
Hui Xu: National University of Defense Technology
Qingjiang Li: National University of Defense Technology
Qi Liu: Fudan University

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf0.2Zr0.8O2 anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf0.2Zr0.8O2 films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>1012), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware.

Date: 2022
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DOI: 10.1038/s41467-022-34774-9

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