Abstract
Passively mode-locked fiber lasers based on nonlinear polarization rotation (NPR) have been widely used due to their ability to produce short pulses with high peak power. Nevertheless, environmental perturbations can influence the mode-locked state, making it a challenge for the practical implementation. Therefore, researchers are searching for assessment criteria to quickly assist and maintain mode-locking of NPR fiber lasers. Speckle patterns containing spectral information can be generated when the laser transmits through a scattering medium, which can serve as indicators of the mode-locked state. The mode-locked regions are confined to the area close to the minimum texture contrast of speckle patterns. Based on these characteristics, we manually simulate the automatic mode-locking (AML). In addition, we utilize convolutional neural networks (CNNs) to recognize speckle patterns of wavelength tunable lasers and determine the center wavelength.
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