42 citations · 82 across the 10 of their papers we have counts for
17 papers
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Yingjie Li, Ruiyang Chen, Weilu Gao +1
Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed…
Perfectly Perform Machine Learning Task with Imperfect Optical Hardware Accelerator
Jichao Fan, Yingheng Tang, Weilu Gao
Optical architectures have been emerging as an energy-efficient and high-throughput hardware platform to accelerate computationally intensive general matrix-matrix multiplications…
Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning
Yingheng Tang, Princess Tara Zamani, Ruiyang Chen +4
The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine lear…
Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network
Ruiyang Chen, Yingjie Li, Minhan Lou +5
Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to…
Wafer-scale, full-coverage, acoustic self-limiting assembly of particles on flexible substrates
Liang Zhao, Bchara Sidnawi, Jichao Fan +6
Self-limiting assembly of particles represents the state-of-the-art controllability in nanomanufacturing processes where the assembly stops at a designated stage1,2, providing a de…
Physics-Guided and Physics-Explainable Recurrent Neural Network for Time Dynamics in Optical Resonances
Yingheng Tang, Jichao Fan, Xinwei Li +4
Understanding the time evolution of physical systems is crucial to revealing fundamental characteristics that are hidden in frequency domain. In optical science, high-quality reson…