activity
20192022
most citedGlobal optimization of dielectric metasurfaces using a physics-driven neural network

441 citations · 466 across the 4 of their papers we have counts for

collaborators

5 papers

cs.ET202215 cited

Automated Atomic Silicon Quantum Dot Circuit Design via Deep Reinforcement Learning

Robert Lupoiu, Samuel S. H. Ng, Jonathan A. Fan +1

Robust automated design tools are crucial for the proliferation of any computing technology. We introduce the first automated design tool for the silicon dangling bond quantum dot…

physics.app-ph20225 cited

WaveY-Net: Physics-augmented deep learning for high-speed electromagnetic simulation and optimization

Mingkun Chen, Robert Lupoiu, Chenkai Mao +4

The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices. We introduce WaveY-Net, a hybrid d…

physics.app-ph2020

Multi-objective and categorical global optimization of photonic structures based on ResNet generative neural networks

Jiaqi Jiang, Jonathan A. Fan

We show that deep generative neural networks, based on global topology optimization networks (GLOnets), can be configured to perform the multi-objective and categorical global opti…

physics.comp-ph20195 cited

Progressive-Growing of Generative Adversarial Networks for Metasurface Optimization

Fufang Wen, Jiaqi Jiang, Jonathan A. Fan

Generative adversarial networks, which can generate metasurfaces based on a training set of high performance device layouts, have the potential to significantly reduce the computat…

cs.LG2019441 cited

Global optimization of dielectric metasurfaces using a physics-driven neural network

Jiaqi Jiang, Jonathan A. Fan

We present a global optimizer, based on a conditional generative neural network, which can output ensembles of highly efficient topology-optimized metasurfaces operating across a r…