Deep learning approach for analysis of terahertz quantum cascade lasers with distributed feedback

A novel approach based on deep neural networks is introduced to perform both efficient and accurate predictions of frequency, loss and electric-field distributions for the lowest-loss resonant mode in terahertz quantum cascade lasers (THz QCLs) with distributed feedback (DFB). The designed neural networks show promising results on various types of DFB structures in THz QCLs, including 1
^st
-order, 2
^nd
-order, 3
^rd
-order DFB and antenna-feedback schemes. Compared with conventional numerical simulations, the running time of our proposed methods is significantly decreased. To the best of our knowledge, this is the first demonstration of fast and precise predictions for resonant mode characteristics in THz QCLs utilizing deep learning.