概括
研究人员开发了一种新的光学计算策略,使用模式划分多重复合 (MDM) 来提高波长划分多重复合 (WDM) 的带宽. 这种新的方法增强了深度神经网络的硬件功能.
科学领域:
- 光学计算
- 光子学
- 集成光学
背景情况:
- 深度神经网络的发展需要先进的硬件计算平台.
- 光学计算,特别是波长分割多重传输 (WDM),提供了更大的计算带宽,但面临着整合和容量挑战.
- 现有的WDM架构需要新的解决方案来增强通道容量.
研究的目的:
- 在光学计算中引入模式分割复杂化 (MDM) 作为新的自由度.
- 为增强计算带宽提供WDM与MDM增强的多维架构.
- 在微环共振器平台上展示基于MDM的光学计算的可行性.
主要方法:
- 提出了一种新的光学计算架构,结合了模式分割多重复合 (MDM) 和波长分割多重复合 (WDM).
- 设计和实验验证的关键光子组件:多模式光束分割器,高阶模式的热光学调器和多模式波导曲线.
- 使用造工艺制造了一种原理证明矩阵复合系统.
主要成果:
- 成功展示了拟议的MDM-WDM光学计算架构的基本组件.
- 该制造系统适用于MDM和组合MDM-WDM计算模式.
- 微环共振器平台可以集成MDM进行增强的光学计算.
结论:
- 模式分割复杂化 (MDM) 为显著增加光学计算带宽提供了一条新的途径.
- 拟议的多维MDM-WDM架构有效地提高了下一代计算的通道容量.
- 实验验证证了基于MDM的光学计算对深度神经网络硬件的可行性.
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