Zn2+工程低屏障LiNbO使可见光可编程铁电记忆器用于对噪声免疫神经形态视觉
Yifei Pei1, Yufei Shang1, Gongjie Liu1
1Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering and College of Physics Science and Technology, Hebei University, Baoding, P. R. China.
Advanced materials (Deerfield Beach, Fla.)
|January 22, 2026
概括
用Zn2+离子合酸 (LiNbO3) 的化剂显著降低了其极化切换能量屏障,使先进的光电子设备能够在低功率可见光下工作. 这一突破促进了神经形态计算和人工智能的新应用.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 纳米技术纳米技术
背景情况:
- 酸 (LiNbO3) 具有出色的铁电和光学特性,使其成为光电子集成设备的有希望的产品.
- LiNbO3中的高极化切换能量障碍限制了其在低功率可见光下使用,阻碍了实际应用.
研究的目的:
- 为了减少 LiNbO3 中的偏振切换能量屏障,以实现低功率可见光操作.
- 开发具有集成传感,存储和计算的高性能铁电光电子系统.
主要方法:
- 用Zn2+离子对LiNbO3进行合,以调节晶格结构并抑制NbLi抗体缺陷.
- 一个Pt/Zn-LiNbO3/Nb:SrTiO3光电子双模记忆器的制造.
- 评估设备性能,包括开关电压,开/关比,电阻状态,保持和耐用性.
- 模拟突触功能和构建一个光学储库计算神经网络.
主要成果:
- 2+兴奋剂将极化切换能量屏障降低了大约69%,在10mW cm-2可见光下使极化逆转成为可能.
- 记忆器表现出超稳定的开关,高开/关比 (~10^3),16个可区分的电阻状态,保留时间>10^4s,耐用性高达10^8个周期.
- 该设备成功模拟了突触功能,并使用光学水库计算网络在MNIST数据集上实现了98.6%的识别准确度.
结论:
- 2+兴奋剂的LiNbO3为低屏障,高性能铁电光电子设备提供了可行的途径.
- 开发的memristor显示出神经形态计算和人工智能的先进应用的潜力,模仿生物系统.
- 本文介绍了集成光电子系统的新材料设计策略.
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