期刊
LASER & PHOTONICS REVIEWS
卷 14, 期 9, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/lpor.202000120
关键词
automated noise rejection; deep learning; speckle metrology; wavelength measurement
资金
- Medical Research Scotland Ph.D. studentship [Ph.D. 873-2015]
- Leverhulme Trust [RPG-2017-197]
- UK Engineering and Physical Sciences Research Council [EP/P030017/1]
- EPSRC [EP/P030017/1] Funding Source: UKRI
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.
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