Deep learning enabled laser speckle wavemeter with a high dynamic range
This enables high-precision, noise-resistant wavelength measurement for applications like spectroscopy or sensing, representing a significant advance over previous methods.
The paper tackled wavelength measurement using a laser speckle wavemeter by applying deep learning to analyze speckle patterns, achieving attometer-scale precision over a broad range from 488 nm to 976 nm, with a dynamic range six orders of magnitude beyond state-of-the-art.
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.