5位二进制光脉冲序列的分类使用金属化物洛夫斯基特记忆光的光发光
Jitendra Kumar1, Daniel Lizotte1, Alexandr Marunchenko1
1Chemical Physics and NanoLund, Lund University, 22100 Lund, Sweden.
概括
金属化物矿的memlumors,设备与状态依赖的光发光量产 (PLQY),可以分类时间依赖的光学信号. 这些神经形态发光器件显示出光学计算应用的前景.
科学领域:
- 材料科学 材料科学 材料科学
- 光电学是指光电子产品.
- 神经形态计算是一种神经形态计算.
背景情况:
- 记光器是神经形态发光器件,具有状态依赖的光发光量子产量 (PLQY).
- 金属化物矿表现出电荷捕获和光,使得基于光线历史的PLQY调制.
研究的目的:
- 为了证明矿记光器在分类依赖时间的二进制光学信号方面的能力.
- 探索矿在光学储计算中的潜力.
主要方法:
- 使用了MAPbBr3和三酸矿膜.
- 应用了激光脉冲和间隙的序列,以激发光发光.
- 分析时间集成的光发光信号以识别输入模式.
主要成果:
- 在微秒以下的时间尺度上成功分类了依赖时间的二进制光学信号.
- 仅使用时间集成光发光的方法,实现了对5位输入模式的完全识别.
- 证明了矿记忆物的国家依赖的PLQY.
结论:
- 矿的记忆可以有效地分类复杂的光学信号.
- 矿的光物理适合用于光学神经形态计算.
- 对矿光物理学的进一步研究可以推进光电池计算.
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