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.
Bibliographical noteFunding Information:
The authors would like to acknowledge technical assistance from Dr. Donatella Cassettari. This work was supported by a Medical Research Scotland Ph.D. studentship Ph.D. 873‐2015 awarded to R.K.G, and grant funding from Leverhulme Trust (RPG‐2017‐197) and UK Engineering and Physical Sciences Research Council (grant EP/P030017/1). The opinions expressed in this article are the authors own and do not reflect the view of above mentioned funding agencies.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Condensed Matter Physics