Deep Learning Enabled Laser Speckle Wavemeter with a High Dynamic Range

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.

Review on Speckle-Based Spectrum Analyzer

“…In addition to the PCA algorithm, deep learning is introduced for speckle-based wavemeter to improve the resolution. Convolutional neural network (CNN) is a typical tool which may be optimized for spectrum recovery from the speckle in the reference [52]. With the powerful helps of CNN, the system experimentally distinguishes the wavelengths with an interval of 2 am.…”

“…The bandwidths of 16 fm and 60 fm are achieved at the speckle image size of 256 pixels×16 pixels and 256 pixels×128 pixels, respectively. The deep learning method is demonstrated as an ideal demodulation algorithm to overcome the resolution-bandwidth trade-off relation for speckle-based wavemeter [52]. In the experiment, a single CNN is used to extract wavelength information from the speckle over an operating bandwidth from 488 nm to 976 nm with the precision of attometre-scale.…”

Decoding Optical Data with Machine Learning

“…For the purpose of imaging [ 85 ] or object classification [ 86 ] through such a complex media, DNNs have been trained to provide accurate recognition with remarkable robustness against environmental instabilities. Based on this platform, people have also demonstrated ML‐assisted laser speckle wavemeters, [ 87 ] hybrid scattering images, [ 85c,88 ] and specklegram sensors. [ 89 ] In addition, we will discuss the extreme events in optical fibers in Section 3.4, where ML helps researchers to study the nonlinear fiber optics.…”

Wavelength sensitivity of the speckle patterns produced by an integrating sphere

“…The applications of speckle metrology upon which we particularly focus are recently identified topics, namely spectrometry [10][11][12][13][14][15][16] and measurements of wavelength [16][17][18][19][20][21][22][23][24]. Both rely on the sensitivity of speckle patterns to a change in incident laser wavelength.…”