Reconfigurable neuromorphic functions in antiferroelectric transistors through coupled polarization switching and charge trapping dynamics

Jing Gao, Yu-Chieh Chien, Jiali Huo, Lingqi Li, Haofei Zheng, Heng Xiang and Kah-Wee Ang ()
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Jing Gao: National University of Singapore
Yu-Chieh Chien: National University of Singapore
Jiali Huo: National University of Singapore
Lingqi Li: National University of Singapore
Haofei Zheng: National University of Singapore
Heng Xiang: National University of Singapore
Kah-Wee Ang: National University of Singapore

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract The growing demand for energy- and area-efficient emulation of biological nervous systems has fueled significant interest in neuromorphic computing. A promising strategy to achieve compact and efficient neuromorphic functionalities lies in the integration of volatile and non-volatile memory functions. However, implementing these functions is challenging due to the fundamentally distinct physical mechanisms. Traditional ferroelectric materials, with their stable polarization, are ideal for emulating biological synaptic functions but their non-volatile nature conflicts with the short-term memory necessary for neuron-like behavior. Here, we report the design for antiferroelectric gating in two-dimensional channel transistors, incorporating antiferroelectricity with charge trapping dynamics. By tuning the area ratio of the Metal-(Anti-)Ferroelectric-Metal-Insulator-Semiconductor (MFMIS) gate stacks, we enable selective reconfiguration of intrinsic volatile antiferroelectric switching and non-volatile switching-assisted charge trapping/de-trapping, thereby achieving both short- and long-term plasticity. This allows the integration of complementary functionalities of artificial neurons and synapses within a single device platform. Additionally, we further demonstrate synaptic and neuronal functions for implementing unsupervised learning rules and spiking behavior in spiking neural networks. This approach holds great potential for advancing both foundational materials design and technology for neuromorphic hardware applications.

Date: 2025
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DOI: 10.1038/s41467-025-59603-7

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