most citedAll-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning

7 citations · 7 across the 1 of their papers we have counts for

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physics.optics20252 cited

Ultrafast neuromorphic computing with nanophotonic optical parametric oscillators

Midya Parto, Gordon H. Y. Li, Ryoto Sekine +5

Over the past decade, artificial intelligence (AI) has led to disruptive advancements in fundamental sciences and everyday technologies. Among various machine learning algorithms,…

physics.optics20254 cited

All-optical computing with beyond 100-GHz clock rates

Gordon H. Y. Li, Midya Parto, Jinhao Ge +9

A computer's clock rate ultimately determines the minimum time between sequential operations or instructions. Despite exponential advances in electronic computer performance owing…

physics.optics2025

Energy-Efficient Ultrashort-Pulse Characterization using Nanophotonic Parametric Amplification

Thomas Zacharias, Robert Gray, Ryoto Sekine +3

The growth of ultrafast nanophotonic circuits necessitates the development of energy-efficient on-chip pulse characterization techniques. Nanophotonic realizations of Frequency Res…

physics.optics2025

Optimizing for a Near Single-Mode Type-0 Optical Parametric Amplifier in Nanophotonics

Shivam Mundhra, Elina Sendonaris, Robert M. Gray +2

Thin-film lithium niobate (TFLN) has recently emerged as a promising platform for integrated nonlinear photonics, enabling the use of optical parametric amplifiers (OPAs) for appli…

physics.optics20227 cited

All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning

Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra +4

In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a…