基于人工神经网络和PCSBL重建算法的高分辨率芯片内空间异质里叶变换光谱仪
Optics express
|October 20, 2023
概括
在在绝缘体平台上的新型紧型里叶变换光谱仪提供了宽带宽和高分辨率. 先进的算法能够精确地重建复杂的光谱信号.
科学领域:
- 光子学 是一个光子学.
- 频谱学是一种光谱学.
- 集成光学 集成光学 集成光学
背景情况:
- 里埃变换 (FT) 光谱对于光谱分析至关重要.
- 芯片上的光谱仪提供了小型化和便携性的优势.
- 在绝缘体 (SOI) 平台使先进的光子集成电路成为可能.
研究的目的:
- 提出并展示一种新的紧型芯片上的里埃变换光谱仪.
- 为了实现广泛的操作带宽和高光谱分辨率.
- 为准确的信号重建开发和验证光谱检索算法.
主要方法:
- 使用16通道功率分割器和马赫-泽恩德干扰仪 (MZI) 阵列的光谱仪的设计.
- 整合MZIs与线性增长的光路长度 (OPL) 差异.
- 开发一种光谱检索算法,将模式合稀疏贝叶斯学习 (PCSBL) 和人工神经网络 (ANN) 结合起来.
主要成果:
- 在100 nm带宽 (1500-1600 nm) 上显示平面传输特征.
- 实现了高分辨率,能够重建带宽0.5纳米的窄带信号,在半最大时全宽 (FWHM).
- 通过使用开发的算法,成功重建了3nm分离的三峰信号.
结论:
- 在SOI平台上提出的芯片上的FT光谱仪实现了宽带宽和高分辨率.
- 集成的PCSBL和ANN光谱检索算法提高了信号重建的准确性.
- 这种紧的设备显示出各种光谱应用的巨大潜力.
相关概念视频
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