We incorporate deep learning (DL) into tiled aperture coherent beam combining (CBC) systems for the first time, to the best of our knowledge. By using a well-trained convolutional neural network DL model, which has been constructed at a non-focal-plane to avoid the data collision problem, the relative phase of each beamlet could be accurately estimated, and then the phase error in the CBC system could be compensated directly by a servo phase control system. The feasibility and extensibility of the phase control method have been demonstrated by simulating the coherent combining of different hexagonal arrays. This DL-based phase control method offers a new way of eliminating dynamic phase noise in tiled aperture CBC systems, and it could provide a valuable reference on alleviating the long-standing problem that the phase control bandwidth decreases as the number of array elements increases.

The power scaling on mid-infrared Raman fibre lasers (RFLs) is in demand for applications in health, environment and security. In this paper, we present the simulated laser behaviours of the tellurite glass RFLs pumped by 300-W Tm-doped fibre lasers (TDFLs) at $2~\unicode[STIX]{x03BC}\text{m}$ for the first time. By combining the advantages of the TDFLs and tellurite fibre, the output power at $2.35~\unicode[STIX]{x03BC}\text{m}$ has reached over hundreds of watts by first-order Raman shift. Moreover, the cascaded RFLs have been demonstrated with a wavelength extension greater than $3~\unicode[STIX]{x03BC}\text{m}$ and output power of tens of watts. To maximize the output power and the slope efficiency of the RFLs, we further analyse the interaction between the Raman gain and cavity loss, which are determined by fibre length and output reflectance of the laser cavity.