论文标题
基于深层衍射神经网络的超模型宽带聚焦光谱仪的设计
Design of ultracompact broadband focusing spectrometers based on deep diffractive neural networks
论文作者
论文摘要
我们提出了基于自适应深度衍射神经网络（A-d $^2 $ nns）的超系统，宽带聚焦光谱仪的逆设计。具体而言，我们介绍并表征了具有工程角度分散的两层衍射设备，这些设备沿预定型焦点轨迹的焦点和转向宽带入射辐射具有所需的带宽和$ 5 $ nm的光谱分辨率。此外，我们系统地研究具有侧面长度的两层设备的聚焦效率$ l = 100〜μ \ Mathrm {M} $和焦距长度$ f = 300〜 \，μ\ Mathrm {m Mathrm {m {m} $在整个可见频谱中，我们表现出准确地重建来自商业上超级亮度的发射光谱的精确重建。提出的A-D $^2 $ NNS设计方法扩展了有效的多焦点衍射光学设备的功能，包括具有自定义焦点轨迹的单发焦点光谱仪，用于应用于超光谱多光谱成像和无镜头显微镜的应用。
We propose the inverse design of ultracompact, broadband focusing spectrometers based on adaptive deep diffractive neural networks (a-D$^2$NNs). Specifically, we introduce and characterize two-layer diffractive devices with engineered angular dispersion that focus and steer broadband incident radiation along predefined focal trajectories with desired bandwidth and $5$ nm spectral resolution. Moreover, we systematically study the focusing efficiency of two-layer devices with side length $L=100~μ\mathrm{m}$ and focal length $f=300~\,μ\mathrm{m}$ across the visible spectrum and we demonstrate accurate reconstruction of the emission spectrum from a commercial superluminescent diode. The proposed a-D$^2$NNs design method extends the capabilities of efficient multi-focal diffractive optical devices to include single-shot focusing spectrometers with customized focal trajectories for applications to ultracompact multispectral imaging and lensless microscopy.
