TR2022-107

Deep Transfer Learning for Nanophotonic Device Design


Abstract:

Applying a transfer-learning technique for generative deep neural networks, we demonstrate a very time-efficient inverse design framework for photonic integrated circuit devices, when there are new demands for structural/material parameters from an existing device library.