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在光电子储存器计算中的设备级非线性和时间内存.
Won Woo Lee1, Junhyung Cho2, Jaehyun Hur3
1Department of Artificial Intelligence Semiconductor Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
Nano convergence
|November 27, 2025
概括
光电子设备通过利用光来实现更快,更复杂的数据处理来增强储存器计算 (RC). 本综述探讨了在神经形态计算应用中改进非线性和内存的设备物理.
科学领域:
- 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.
- 设备物理 设备物理
背景情况:
- 储计算 (RC) 是一种适用于非线性,与时间相关的数据处理的计算范式.
- 光电子设备通过将光与非线性动力学接口,为RC提供了独特的优势.
- 最近的进展集中在光电二极管,记忆器和光电晶体管上,以提高RC性能.
研究的目的:
- 从设备物理的角度回顾储计算的光电子设备发展.
- 突出了在光电子RC中改善非线性,时间记忆和节点多样性的机制.
- 连接材料设计,设备架构和容器动力学,用于扩展光电子RC.
主要方法:
- 审查RC的光电子设备最近的进展.
- 对增强非线性和时间记忆的设备级通路的分析.
- 不同的光学激发方案 (波长,强度,门灯共调) 的比较.
主要成果:
- 光电子设备使混合光电调能够进行增强的RC.
- 确定了多态生成,双向突触重量调制和时间响应量身定制的机制.
- 特定的激发方案会影响水库在模式识别和时间序列预测方面的性能.
结论:
- 光电子设备对于推进水库计算至关重要.
- 设备工程和材料设计是扩展光电子RC的关键.
- 这篇评论为设备工程和神经形态计算交集的研究人员提供了见解.
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