502 citations · 2.4k across the 13 of their papers we have counts for
13 papers
Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks
Sunil Pai, Zhanghao Sun, Tyler W. Hughes +11
Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in d…
Anderson localization of electromagnetic waves in three dimensions
Alexey Yamilov, Sergey E. Skipetrov, Tyler W. Hughes +3
Anderson localization marks a halt of diffusive wave propagation in disordered systems. Despite extensive studies over the past 40 years, Anderson localization of light in three di…
Inverse design of photonic crystals through automatic differentiation
Momchil Minkov, Ian A. D. Williamson, Lucio C. Andreani +5
Gradient-based inverse design in photonics has already achieved remarkable results in designing small-footprint, high-performance optical devices. The adjoint variable method, whic…
Design of a multi-channel photonic crystal dielectric laser accelerator
Zhexin Zhao, Dylan S. Black, R. Joel England +5
To be useful for most scientific and medical applications, compact particle accelerators will require much higher average current than enabled by current architectures. For this pu…
Parallel fault-tolerant programming of an arbitrary feedforward photonic network
Sunil Pai, Ian A. D. Williamson, Tyler W. Hughes +4
Reconfigurable photonic mesh networks of tunable beamsplitter nodes can linearly transform -dimensional vectors representing input modal amplitudes of light for applications suc…
Forward-Mode Differentiation of Maxwell's Equations
Tyler W Hughes, Ian A D Williamson, Momchil Minkov +1
We present a previously unexplored forward-mode differentiation method for Maxwell's equations, with applications in the field of sensitivity analysis. This approach yields exact g…
Wave Physics as an Analog Recurrent Neural Network
Tyler W. Hughes, Ian A. D. Williamson, Momchil Minkov +1
Analog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natu…
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
Ian A. D. Williamson, Tyler W. Hughes, Momchil Minkov +3
We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small po…
Reconfigurable Photonic Circuit for Controlled Power Delivery to Laser-Driven Accelerators on a Chip
Tyler W. Hughes, R. Joel England, Shanhui Fan
Dielectric laser acceleration (DLA) represents a promising approach to building miniature particle accelerators on a chip. However, similar to conventional RF accelerators, an auto…
Adjoint method and inverse design for nonlinear nanophotonic devices
Tyler W. Hughes, Momchil Minkov, Ian A. D. Williamson +1
The development of inverse design, where computational optimization techniques are used to design devices based on certain specifications, has led to the discovery of many compact,…
Training of photonic neural networks through in situ backpropagation
Tyler W. Hughes, Momchil Minkov, Yu Shi +1
Recently, integrated optics has gained interest as a hardware platform for implementing machine learning algorithms. Of particular interest are artificial neural networks, since ma…
On-Chip Laser Power Delivery System for Dielectric Laser Accelerators
Tyler W. Hughes, Si Tan, Zhexin Zhao +12
We propose an on-chip optical power delivery system for dielectric laser accelerators based on a fractal 'tree-branch' dielectric waveguide network. This system replaces experiment…
Method for Computationally Efficient Design of Dielectric Laser Accelerators
Tyler Hughes, Georgios Veronis, Kent Wootton +2
Dielectric microstructures have generated much interest in recent years as a means of accelerating charged particles when powered by solid state lasers. The acceleration gradient (…