深度学习加速了光子功率分隔器的发现
Gandhi Alagappan1, Ching Eng Png1
1Agency for Science, Technology, and Research (A-STAR), Institute of High-Performance Computing, Fusionopolis, 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore.
Nanophotonics (Berlin, Germany)
|December 16, 2024
概括
深度学习加速了紧的光子功率分隔器的设计. 这些设备提供超低损耗和宽带宽,推进了光子技术.
科学领域:
- 光子学是指光子学的使用方法.
- 光学工程是指光学工程.
- 深度学习应用程序
背景情况:
- 光子功率分隔器是光通信系统中必不可少的组件.
- 传统的设计方法在同时优化多个性能指标方面面临着挑战.
- 制造变化可以显著影响设备性能.
研究的目的:
- 开发一种用于设计高性能光子功率分隔器的新方法.
- 探索深度学习在光子学中用于反向设计的应用.
- 为了实现具有紧足迹,超低损耗和超宽带宽的设备.
主要方法:
- 应用深度学习加速反向设计算法.
- 开发具有高精度的深度学习模型 (10-6到10-8),用于TE和TM两极分化.
- 利用这些模型在经验上可描述的子空间内进行超快速搜索.
主要成果:
- 发现了一系列具有特殊性能的光子功率分隔器的光谱.
- 展示具有可编程功率分割比率和多余损失低至0.14dB的设备.
- 实现了低于λ2的足迹和低损耗带宽,覆盖了整个电信频谱 (O,S,E,C,L,U频段).
结论:
- 深度学习使先进的光子设备的有效反向设计成为可能.
- 开发的功率分隔器表现出最先进的性能和稳定性.
- 这种方法为下一代光子应用铺平了道路.
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