Zn <sup>2+</sup> Engineered Low‐Barrier LiNbO <sub>3</sub> Enables Visible‐Light Programmable Ferroelectric Memristors for Noise‐Immune Neuromorphic Vision

Lithium niobate (LiNbO<sub>3</sub>), owing to its unique ferroelectric polarization and excellent optical properties, has shown great potential in high-performance optoelectronic integrated devices. However, the high polarization switching energy barrier makes it difficult to achieve polarization reversal under low-power visible light, severely limiting its practical applicability. Here, Zn<sup>2+</sup> ions were doped into the LiNbO<sub>3</sub> lattice to modulate the local lattice structure via valence-state imbalance, effectively suppressing the formation of Nb<sub>Li</sub> <sup>4+</sup> antisite defects and reducing electron-trap density. Meanwhile, the narrowed bandgap enhanced carrier excitation efficiency and improved depolarization-field screening, lowering the polarization switching energy barrier by approximately 69% and enabling polarization reversal under low-energy visible light illumination (10 mW cm<sup>-2</sup>). Accordingly, the fabricated Pt/Zn-LiNbO<sub>3</sub>/Nb:SrTiO<sub>3</sub> optoelectronic bimodal memristor exhibits ultra-stable switching voltage characteristics, with a voltage coefficient of variation as low as 2.2%-3.2%; a high on/off ratio of approximately 10<sup>3</sup>; 2<sup>4</sup> clearly distinguishable resistance states; retention exceeding 10<sup>4</sup> s; and excellent endurance up to 10<sup>8</sup> cycles. Under visible light stimulation, the device emulates multiple representative synaptic functions, including short-term to long-term memory (STP-LTP) transition, paired-pulse facilitation (PPF), and associative learning. Moreover, an optical reservoir computing neural network constructed from the device's multilevel optical memory and synaptic features achieves a high recognition accuracy of 98.6% on the noise-corrupted MNIST dataset, demonstrating robustness and visual recognition capability comparable to biological systems. This study proposes a new materials design paradigm for constructing low-barrier, high-performance ferroelectric optoelectronic systems with integrated sensing, storage, and computation functionalities.

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Journal
Advanced Materials
Published
2026-01-22
DOI
https://doi.org/10.1002/adma.202511352
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Zn 2+ Engineered Low‐Barrier LiNbO 3 Enables Visible‐Light Programmable Ferroelectric Memristors for Noise‐Immune Neuromorphic Vision

Dingxin Liu, Changli Liu, Gongjie Liu, Yufei Shang et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Zn 2+ Engineered Low‐Barrier LiNbO 3 Enables Visible‐Light Programmable Ferroelectric Memristors for Noise‐Immune Neuromorphic Vision

Dingxin Liu, Changli Liu, Gongjie Liu, Yufei Shang, Shuang Ma, Fuli Wang, Molan Guo, Jianning Wang, Jianxin Guo, Xiaobing Yan, Yue Hou, Jianhui Zhao, Yifei Pei
article en

Abstract

Lithium niobate (LiNbO<sub>3</sub>), owing to its unique ferroelectric polarization and excellent optical properties, has shown great potential in high-performance optoelectronic integrated devices. However, the high polarization switching energy barrier makes it difficult to achieve polarization reversal under low-power visible light, severely limiting its practical applicability. Here, Zn<sup>2+</sup> ions were doped into the LiNbO<sub>3</sub> lattice to modulate the local lattice structure via valence-state imbalance, effectively suppressing the formation of Nb<sub>Li</sub> <sup>4+</sup> antisite defects and reducing electron-trap density. Meanwhile, the narrowed bandgap enhanced carrier excitation efficiency and improved depolarization-field screening, lowering the polarization switching energy barrier by approximately 69% and enabling polarization reversal under low-energy visible light illumination (10 mW cm<sup>-2</sup>). Accordingly, the fabricated Pt/Zn-LiNbO<sub>3</sub>/Nb:SrTiO<sub>3</sub> optoelectronic bimodal memristor exhibits ultra-stable switching voltage characteristics, with a voltage coefficient of variation as low as 2.2%-3.2%; a high on/off ratio of approximately 10<sup>3</sup>; 2<sup>4</sup> clearly distinguishable resistance states; retention exceeding 10<sup>4</sup> s; and excellent endurance up to 10<sup>8</sup> cycles. Under visible light stimulation, the device emulates multiple representative synaptic functions, including short-term to long-term memory (STP-LTP) transition, paired-pulse facilitation (PPF), and associative learning. Moreover, an optical reservoir computing neural network constructed from the device's multilevel optical memory and synaptic features achieves a high recognition accuracy of 98.6% on the noise-corrupted MNIST dataset, demonstrating robustness and visual recognition capability comparable to biological systems. This study proposes a new materials design paradigm for constructing low-barrier, high-performance ferroelectric optoelectronic systems with integrated sensing, storage, and computation functionalities.

Advanced MaterialsVol. 38(13)
Hebei University (CN)
National Natural Science Foundation of China, Department of Education of Hebei Province, Natural Science Foundation of Hebei Province, Hebei University
Affordable and clean energy
Openalex Percentile: Top 81%
Advanced Memory and Neural Computing
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