Fabrication aware design of sensitive silicon photonics devices

Since the turn of the century, Silicon Photonics (SiPh) has advanced data communication and computing via diverse integrated optical functions and multiplexing strategies. However, conventional design methodologies limit scalability, and inverse designs lead to features sensitive to fabrication process variations. This talk explores the harnessing of machine learning (ML) to predict and rectify these deviations in the design phase. This technique enhances design fidelity and device performance, while facilitating smaller design features, bypassing constraints of traditional methods. Highlighting PreFab, an innovative ML technology, applicable to both conventional and inverse designs, it predicts and corrects fabrication deviations, enabling refined design processes.