747 citations · 1k across the 5 of their papers we have counts for
7 papers
Lithography-free in situ photonic inverse design
Yiqi Zhao, Martin M. Stein, Fan O. Wu +6
Inverse design has emerged as a powerful engineering approach in nanophotonics, leading to devices with unintuitive designs that extend beyond human intuition. However, inverse-des…
Programmable nonlinearity within nanophotonic waveguides
Benjamin A. Ash, Ryotatsu Yanagimoto, Mandar M. Sohoni +8
Nonlinear nanophotonics enables the engineering of optical functions using light-matter interactions in nanoscale structures with carefully designed geometries. Phase matching in n…
TRON: Trainable, architecture-reconfigurable random optical neural networks
Ziao Wang, Fei Xia, Logan G. Wright +5
Deep learning has triggered explosive growth in the demand for specialized hardware processors, thus motivating the development of scalable and reconfigurable computing substrates.…
Programmable on-chip nonlinear photonics
Ryotatsu Yanagimoto, Benjamin A. Ash, Mandar M. Sohoni +7
Nonlinear photonics uses coherent interactions between optical waves to engineer functionality that is not possible with purely linear optics. Traditionally, the function of a nonl…
Arbitrary control over multimode wave propagation for machine learning
Tatsuhiro Onodera, Martin M. Stein, Benjamin A. Ash +10
Controlled multimode wave propagation can enable more space-efficient photonic processors than architectures based on discrete components connected by single-mode waveguides. Inste…
Image sensing with multilayer, nonlinear optical neural networks
Tianyu Wang, Mandar M. Sohoni, Logan G. Wright +5
Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position,…
Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein +4
Deep neural networks have become a pervasive tool in science and engineering. However, modern deep neural networks' growing energy requirements now increasingly limit their scaling…