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Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture
Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed.
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption.
Scientific Computing with Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems.
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms.
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax
Specifically, Gumbel-Softmax with a novel complex-domain regularization method is employed to enable differentiable one-to-one mapping from discrete device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task.
Band Structure Dependent Electronic Localization in Macroscopic Films of Single-Chirality Single-Wall Carbon Nanotubes
Significant understanding has been achieved over the last few decades regarding chirality-dependent properties of single-wall carbon nanotubes (SWCNTs), primarily through single-tube studies.
Mesoscale and Nanoscale Physics
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments.
