  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 and broad spectrum. Nevertheless, environmental disturbances
can disrupt the mode-locked state, making it a challenge for practical
implementation. Therefore, scientists have proposed mode-locked NPR lasers
assisted with artificial intelligence, which can effectively address the issues
related to mode-locking stability. Speckle patterns containing spectral
information can be generated when the laser transmitting through a scattering
medium, which can be served as indicators of the mode-locked state. The
contrast of the Tamura texture feature of the speckle patterns exhibits
periodic "V" shaped variations with respect to the rotation angles of the
waveplates, according to experimental results. The stable mode-locking region
is confined to the area close to the minimum contrast. Based on these
characteristics, an intelligent approach employing a modified gradient
algorithm to identify the region of minimum speckle contrast for achieving
mode-locked state. The average number of iterations needed to achieve initial
mode-locking and recover mode-locking are about 20 and 10, respectively. Once
the mode-locking is achieved, the neural network can be employed to distinguish
single-pulse or multi-pulses outputs based on the speckle pattern, thereby
enabling intelligent stable mode-locked single-pulse genration from the NPR
fiber laser.
