← Search

Ray Chen

3 accepted papers

2024

PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices

NeurIPS 2024poster

Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hindering scalability and turnaround time in the photonic circuit design process. Neur…

2022

NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

NeurIPS 2022accept

Optical computing has become emerging technology in next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation is critical to the design, optimization, and validation of photonic devices and circuits. However, costly numerical…

2021

L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization

NeurIPS 2021poster

Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra-low execution latency, and high energy efficiency. In-situ training on the online programmable photonic chips is…