基于异构的2D瓦纳碳化物/氧化物人工感觉神经元用于多色近红外物体识别
Yuanduo Qu1, Mengdi Hao1,2, Haoran Hao3
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, P. R. China.
Advanced materials (Deerfield Beach, Fla.)
|September 12, 2025
概括
一种新型的碳化/氧化异构结构记忆器可实现高效的近红外 (NIR) 检测和物体识别. 这一突破为自主系统和监控应用提供了低功耗,高精度的平台.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 神经形态工程的神经形态工程
背景情况:
- 近红外 (NIR) 光子检测和物体识别对于自主导航和监控至关重要.
- 传统系统面临能源效率低下和数据瓶.
- 对于下一代神经形态设备,需要先进的材料.
研究的目的:
- 设计和合成一种新的异构结构,以增强NIR检测和识别.
- 开发基于memristor的人工神经网络 (ANN) 进行复杂对象识别.
- 克服传统光电探测器和计算系统的局限性.
主要方法:
- 一个碳化/氧化 (V2C/V2O5-x) 异构结构通过拓化学转化合成.
- 异构结构的memristor表现出稳定的值型电阻切换 (RS) 行为.
- 研究了NIR响应和多颜色可调节RS特性.
主要成果:
- 在V2C/V2O5-x的memristor显示稳定的RS超过数千个周期和长期存储.
- 设备显示了与NIR光功率和波长的线性值电压变化.
- 一个ANN使用YOLOv7.7实现了高识别精度 (89.6%的汽车,85.9%的人)
结论:
- V2C/V2O5-x异构结构为神经形态设备提供了多功能功能.
- 基于memristor的ANN平台在现实场景中实现了强大的多色对象检测.
- 这项工作促进了自主和监控技术的NIR检测和识别.
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