Abstract

Mixed-mode-state control of lasers under continuous-wave (CW) operation, where multi-physics interactions among carriers, photons, and heat are involved, is important for realizing desired lasing characteristics, as well as for dynamic control of lasers. In this paper, we demonstrate mixed-mode-state control of a photonic-crystal surface-emitting laser (PCSEL) under CW operation by manipulating its current injection distribution. To control the current injection distribution, we introduce a multiple-electrode matrix into the p-side of the PCSEL, and we bond the PCSEL to a heatsink in the p-side-down-configuration to dissipate heat while also enabling current injection via each p-side electrode. Furthermore, we employ a convolutional neural network (CNN) to correlate the current distributions and the far-field patterns (FFPs) corresponding to the mode states, and to predict the current distributions necessary to obtain targeted FFPs. FFPs resembling the targeted ones with high fidelity (90%) are obtained by using the constructed CNN. These results lead to the realization of next-generation smart CW lasers capable of mixed-mode-state control even in a dynamic environment, which are essential for applications such as advanced material processing and even aerospace.

© 2024 Optica Publishing Group

Smart laser controlling mixed-mode state

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