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.
Funder
Japan Society for the Promotion of Science
Council for Science, Technology and Innovation, Cross-ministerial Strategic Innovation Promotion Program
Program for Bridging the Gap between R&D and the Ideal Society (Society 5.0) and Gathering Economic and Social Value
Casio Science Promotion Foundation
Toyota Physical and Chemical Research Institute
Subject
Atomic and Molecular Physics, and Optics,Statistical and Nonlinear Physics