78 citations · 126 across the 5 of their papers we have counts for
5 papers
DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms
Christopher Yeung, Benjamin Pham, Ryan Tsai +2
In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of phot…
Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures
Christopher Yeung, Benjamin Pham, Zihan Zhang +2
From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of…
Enhancing Adjoint Optimization-based Photonics Inverse Design with Explainable Machine Learning
Christopher Yeung, David Ho, Benjamin Pham +2
A fundamental challenge in the design of photonic devices, and electromagnetic structures more generally, is the optimization of their overall architecture to achieve a desired res…
Global Inverse Design Across Multiple Photonic Structure Classes Using Generative Deep Learning
Christopher Yeung, Ryan Tsai, Benjamin Pham +6
Understanding how nano- or micro-scale structures and material properties can be optimally configured to attain specific functionalities remains a fundamental challenge. Photonic m…
Multiplexed Supercell Metasurface Design and Optimization with Tandem Residual Networks
Christopher Yeung, Ju-Ming Tsai, Brian King +5
Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal-insulator-metal (MIM) metasurfaces arranged…