A Monolithic 3D/2D Perovskite Memristor Enabling Ultralow-Voltage Neuromorphic Computing and Biomimetic Sensing

Perovskite memristors hold great promise for neuromorphic computing and bioinspired sensing, yet their development is hindered by high operating voltages, environmental instability, and limited functional integration. Here, we report a robust memristor based on a 3D/2D halide perovskite heterostructure that overcomes these challenges. The device demonstrates ultralow switching voltages of ∼+0.18 V (SET) and ∼-0.4 V (RESET), a high ON/OFF ratio >10<sup>4</sup>, excellent retention (>10<sup>4</sup> s), and endurance (>600 cycles) under ambient conditions. This performance stems from a synergistic "bandgap staircase and built-in electric field" mechanism at the heterointerface, which enhances field confinement for low-voltage switching while the 2D layer suppresses ion migration. Remarkably, this single platform integrates a complete neuromorphic-sensory functional chain. It emulates synaptic plasticity, achieving 90.77% accuracy in MNIST handwritten digit recognition. It also functions as a physical reservoir for temporal pattern processing, reaching perfect classification accuracy. Furthermore, it serves as an artificial nociceptor that faithfully replicates key pain perceptions such as threshold, nonadaptation, sensitization, and relaxation. With ultralow power consumption of only 36 pJ per switch, this multifunctional memristor provides a versatile hardware prototype for next-generation intelligent systems and adaptive human-machine interfaces.

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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2026-03-13
DOI
https://doi.org/10.1021/acs.jpclett.5c04053
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

A Monolithic 3D/2D Perovskite Memristor Enabling Ultralow-Voltage Neuromorphic Computing and Biomimetic Sensing

Zhenfu Zhao, Hang Wang, Ran Zhao, Zhenwang Luo et al.
The Journal of Physical Chemistry Letters
Advanced Memory and Neural Computing
article

A Monolithic 3D/2D Perovskite Memristor Enabling Ultralow-Voltage Neuromorphic Computing and Biomimetic Sensing

Zhenfu Zhao, Hang Wang, Ran Zhao, Zhenwang Luo, Fei Zheng, Xu Wang, Ziyang Hu, Mingyang Huang
article en

Abstract

Perovskite memristors hold great promise for neuromorphic computing and bioinspired sensing, yet their development is hindered by high operating voltages, environmental instability, and limited functional integration. Here, we report a robust memristor based on a 3D/2D halide perovskite heterostructure that overcomes these challenges. The device demonstrates ultralow switching voltages of ∼+0.18 V (SET) and ∼-0.4 V (RESET), a high ON/OFF ratio >10<sup>4</sup>, excellent retention (>10<sup>4</sup> s), and endurance (>600 cycles) under ambient conditions. This performance stems from a synergistic "bandgap staircase and built-in electric field" mechanism at the heterointerface, which enhances field confinement for low-voltage switching while the 2D layer suppresses ion migration. Remarkably, this single platform integrates a complete neuromorphic-sensory functional chain. It emulates synaptic plasticity, achieving 90.77% accuracy in MNIST handwritten digit recognition. It also functions as a physical reservoir for temporal pattern processing, reaching perfect classification accuracy. Furthermore, it serves as an artificial nociceptor that faithfully replicates key pain perceptions such as threshold, nonadaptation, sensitization, and relaxation. With ultralow power consumption of only 36 pJ per switch, this multifunctional memristor provides a versatile hardware prototype for next-generation intelligent systems and adaptive human-machine interfaces.

The Journal of Physical Chemistry LettersVol. 17(12)
Ningbo University (CN)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
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A Monolithic 3D/2D Perovskite Memristor Enabling Ultralow-Voltage Neuromorphic Computing and Biomimetic Sensing — Zhenfu Zhao, Hang Wang, et al. · The Journal of Physical Chemistry Letters (2026) | TGRS Research Map | TGRS