Deep Learning Enabled Laser Speckle Wavemeter with a High Dynamic Range

Roopam K. Gupta, Graham D. Bruce, Simon J. Powis, Kishan Dholakia

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number2000120
JournalLaser and Photonics Reviews
Volume14
Issue number9
DOIs
Publication statusPublished - 2020 Sep 1

Bibliographical note

Funding 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

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