Nanophotonic structure inverse design for switching application using deep learning

Abstract Switching functionality is pivotal in advancing communication systems, serving as a paramount mechanism. Despite numerous innovations in this field, optical switch design, fabrication, and characterization have traditionally followed an iterative approach. Within this paradigm, the designer formulates an informed conjecture regarding the switch's structural configuration and subsequently resolves Maxwell's equations to ascertain its performance. Conversely, the inverse problem, which entails deriving a switch geometry to achieve a targeted electromagnetic response, continues to pose formidable challenges and necessitates substantial time and effort, particularly under the constraints of specific assumptions. In this work, we propose a deep neural network-based method to approximate the spectral transmittance of all-optical switches. The findings substantiate the efficacy of deep learning in the design of all-optical plasmonic switches, which are renowned as the fastest switches at the nanoscale. The nonlinear Kerr effect in square resonators is leveraged to demonstrate the switching performance. Juxtaposed with conventional simulations, the proposed model showcases a remarkable improvement in computational efficiency. Furthermore, deep learning can resolve nanophotonic inverse design problems without reliance on trial-and-error or empirical strategies. Compared to simulations, the mean squared error for both forward and inverse models is meager, with values of around 0.03 and 0.02, respectively. The deep learning-proposed switches exhibit excellent suitability for integration into photonic integrated circuits, substantially influencing the progression of all-optical signal processing.