The speckle pattern produced when a laser is scattered by a disordered medium has recently been shown to give a surprisingly accurate or broadband measurement of wavelength. Here it is shown that deep learning is an ideal approach to analyze wavelength variations using a speckle wavemeter due to its ability to identify trends and overcome low signal to noise ratio in complex datasets. This combination enables wavelength measurement at high precision over a broad operating range in a single step, with a remarkable capability to reject instrumental and environmental noise, which has not been possible with previous approaches. It is demonstrated that the noise rejection capabilities of deep learning provide attometre‐scale wavelength precision over an operating range from 488 nm to 976 nm. This dynamic range is six orders of magnitude beyond the state of the art.

中文翻译：

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具有高动态范围的支持深度学习的激光散斑仪

最近已经显示出当激光被无序的介质散射时产生的散斑图样可以提供令人惊讶的准确的波长测量或宽带测量。此处表明，由于深度学习能够识别趋势并克服复杂数据集中的低信噪比，因此它是使用散斑波表分析波长变化的理想方法。这种组合可以一步一步地在宽范围的操作范围内以高精度进行波长测量，并具有出色的抑制仪器噪声和环境噪声的能力，这是以前方法无法实现的。事实证明，深度学习的噪声抑制功能可在488 nm至976 nm的工作范围内提供准米级的波长精度。