Deep learning enabled laser speckle wavemeter with a high dynamic range

TL;DR

This paper demonstrates that deep learning applied to laser speckle patterns enables highly precise wavelength measurement across a broad range, significantly surpassing previous methods in noise rejection and dynamic range.

Contribution

The integration of deep learning with speckle pattern analysis achieves high-precision, broadband wavelength measurement with exceptional noise robustness, extending the dynamic range by six orders of magnitude.

Findings

Attometre-scale wavelength precision achieved.

Broad operating range from 488 nm to 976 nm.

Dynamic range exceeds previous methods by six orders of magnitude.

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 analyse 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.