One-Step Annealing-Configured Hf <sub>0.2</sub> Zr <sub>0.8</sub> O <sub>2</sub> Memristive–Antiferroelectric Devices for Bioinspired CSNN Neuromorphic Computing

Traditional computing systems are limited by separated architectures, inspiring the development of compatible artificial neuron and synaptic devices for hybrid neuromorphic computing. Here, we present a CMOS-compatible, single-stack Hf<sub>0.2</sub>Zr<sub>0.8</sub>O<sub>2</sub> (HZO) platform in which the as-deposited film serves as a memristive synapse, while a one-step postdeposition anneal yields an antiferroelectric (AFE) neuron device. Both device roles share identical CMOS-compatible premanufacturing steps; a single, nonreversible postanneal diverges the same TiN/HZO/TiN stack into the AFE-neuron path. A memristive device enables analogue conductance modulation for convolutional feature extraction. After annealing, antiferroelectric devices achieve spontaneous depolarization behavior, paving the way for spike-based encoding and biologically plausible neuronal dynamics. By integrating this process-compatible dual-mode device set within a unified material platform, a convolutional spiking neural network was constructed with 97.9% accuracy in dynamic gestures. This work highlights CMOS compatible neuromorphic electronics for hybrid neuromorphic computing within compact neuromorphic hardware.

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Publication Details

Journal
Nano Letters
Published
2025-12-04
DOI
https://doi.org/10.1021/acs.nanolett.5c04249
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

One-Step Annealing-Configured Hf 0.2 Zr 0.8 O 2 Memristive–Antiferroelectric Devices for Bioinspired CSNN Neuromorphic Computing

Lin Lü, Jiajie Yu, Yongkai Liu, Zhigang Li et al.
Nano Letters
Advanced Memory and Neural Computing
article

One-Step Annealing-Configured Hf 0.2 Zr 0.8 O 2 Memristive–Antiferroelectric Devices for Bioinspired CSNN Neuromorphic Computing

Lin Lü, Jiajie Yu, Yongkai Liu, Zhigang Li, Qingxin Chen, Tianyu Wang, Qingqing Sun, Lin Chen, Jialin Meng, Kangli Xu, Jinhao Zhang, Lu Chen, David Wei Zhang
article en

Abstract

Traditional computing systems are limited by separated architectures, inspiring the development of compatible artificial neuron and synaptic devices for hybrid neuromorphic computing. Here, we present a CMOS-compatible, single-stack Hf<sub>0.2</sub>Zr<sub>0.8</sub>O<sub>2</sub> (HZO) platform in which the as-deposited film serves as a memristive synapse, while a one-step postdeposition anneal yields an antiferroelectric (AFE) neuron device. Both device roles share identical CMOS-compatible premanufacturing steps; a single, nonreversible postanneal diverges the same TiN/HZO/TiN stack into the AFE-neuron path. A memristive device enables analogue conductance modulation for convolutional feature extraction. After annealing, antiferroelectric devices achieve spontaneous depolarization behavior, paving the way for spike-based encoding and biologically plausible neuronal dynamics. By integrating this process-compatible dual-mode device set within a unified material platform, a convolutional spiking neural network was constructed with 97.9% accuracy in dynamic gestures. This work highlights CMOS compatible neuromorphic electronics for hybrid neuromorphic computing within compact neuromorphic hardware.

Nano LettersVol. 25(50)
Shanghai International Studies University (CN), Shandong University (CN), Fudan University (CN), Shanghai Innovative Research Center of Traditional Chinese Medicine (CN), City University of Hong Kong, Shenzhen Research Institute (CN), Institute of Microelectronics (CN), Next Generation Technology (United States) (US), Shanghai Fudan Microelectronics (China) (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN), State Key Laboratory of Crystal Materials
Openalex Percentile: Top 61%
Advanced Memory and Neural Computing
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