A Novel Generative Inverse Approach Towards Silicon-Based Nano-Photonic Power Splitter Design Generation

In recent years, the design of chip-based photonic systems has significantly moved towards Artificial Intelligence-assisted data-driven methods instead of conventional intuition and simulation-based ones. The time-consuming nature of traditional chip-design methods, coupled with their insufficient flexibility to accommodate rapidly evolving integrated circuit requirements, contributes to this situation. In this work, we propose a novel generative model for the inverse design of nanophotonic power splitters. Our proposed model generates power splitters from arbitrary response spectra from 1.46 to 1.63 mu m with a central wavelength of 1.55 mu m. The model employs machine learning and a quadratic programming solver, which consists of a linear regressor and a mixed integer quadric programming solver. It is deterministic due to its generator being a quadratic programming solver. We empirically show that the generated structures have error margins within 10-4% and 2x10-4% for any given arbitrary response spectra. Furthermore, we also show that the model is capable of handling and generating Out-of-Distribution responses and their associated devices. Our code and dataset are available here.