材料科学
光电子学
神经形态工程学
铁电性
记忆电阻器
极化(电化学)
电压
光子上转换
非易失性存储器
带隙
可见光谱
光学计算
铌酸锂
人工神经网络
MNIST数据库
光开关
内容寻址存储器
稳健性(进化)
限制
高效能源利用
低压
电子工程
作者
Yifei Pei,Yufei Shang,Gongjie Liu,Shuang Ma,Molan Guo,Changli Liu,Yue Hou,Fuli Wang,Jianning Wang,Dingxin Liu,Jianhui Zhao,Jianxin Guo,Xiaobing Yan
标识
DOI:10.1002/adma.202511352
摘要
Lithium niobate (LiNbO3), 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, Zn2+ ions were doped into the LiNbO3 lattice to modulate the local lattice structure via valence-state imbalance, effectively suppressing the formation of NbLi 4+ 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-2). Accordingly, the fabricated Pt/Zn-LiNbO3/Nb:SrTiO3 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 103; 24 clearly distinguishable resistance states; retention exceeding 104 s; and excellent endurance up to 108 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI