Deep Learning Enabled Laser Speckle Wavemeter with a High Dynamic Range. Issue 9 (2nd August 2020)

• Record Type: Journal Article
• Authors: Gupta, Roopam K.; Bruce, Graham D.; Powis, Simon J.; Dholakia, Kishan
• Is Part Of: Laser & photonics reviews. Volume 14:Issue 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-02
• Subjects: automated noise rejection -- deep learning -- speckle metrology -- wavelength measurement; Lasers -- Periodicals; Photonics -- Periodicals; Lasers -- Périodiques; Photonique -- Périodiques; 621.36
• DOI: 10.1002/lpor.202000120
• Languages: English
• ISSNs: 1863-8880

Abstract

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. Abstract : Deep learning is employed to develop a speckle‐based wavemeter with automated noise rejection capability. This noise rejection capability enables the wavemeter to accomplish attometre‐scale precision over a broad operating range from 488 to 976 nm with an extended capability to identify the wavelength of speckle produced from a different scattering medium.