Related papers: A Reconfigurable Nanophotonics Platform for Sub-Mi…
Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum power consumption. In this work, we present a new concept for…
Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it…
Integrated quantum photonics offers a promising path to scale up quantum optics experiments by miniaturizing and stabilizing complex laboratory setups. Central elements of quantum integrated photonics are quantum emitters, memories,…
Lithium-Niobate-On-Insulator (LNOI) is emerging as a promising platform for integrated quantum photonic technologies because of its high second-order nonlinearity and compact waveguide footprint. Importantly, LNOI allows for creating…
Plasmonic metasurfaces represent a promising platform for enhancing light-matter interaction. Active control of the optical response of metasurfaces is desirable for applications such as beam-steering, modulators and switches, biochemical…
Optical nanofibers confine light to subwavelength scales, and are of interest for the design, integration, and interconnection of nanophotonic devices. Here we demonstrate high transmission (> 97%) of the first family of excited modes…
Energy transfer between photons and molecules and between neighboring molecules is ubiquitous in living nature, most prominently in photosynthesis. While energy transfer is efficiently utilized by living systems, its adoption to connect…
Dynamic reconfiguration is crucial for nanoplasmonic structures to achieve diversified functions and optimize performances; however, the dynamic reconfiguration of spatial arrangements remains a formidable technological challenge. Here, we…
Optofluidics is dedicated to achieving integrated control of particle and fluid motion, particularly on the micrometer scale, by utilizing light to direct fluid flow and particle motion. The field has seen significant growth recently,…
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high…
Implantable silicon neural probes with integrated nanophotonic waveguides can deliver patterned dynamic illumination into brain tissue at depth. Here, we introduce neural probes with integrated optical phased arrays and demonstrate optical…
Understanding the dynamics of electrical signals within neuronal assemblies is crucial to unraveling complex brain function. Despite recent advances in employing optically active nanostructures in transmembrane potential sensing, there…
Topological plasmonics offers new ways to manipulate light by combining concepts from topology and plasmonics, similar to topological edge states in photonics. However, designing such topological states remains challenging due to the…
The ability to modulate light at high speeds is of paramount importance for telecommunications, information processing, and medical imaging technologies. This has stimulated intense efforts to master optoelectronic switching at visible and…
Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report…
Tunable optical delay lines that simultaneously offer nanosecond-scale delay, broadband operation, low dispersion, and compact footprint remain challenging to realize with conventional integrated photonic platforms. Here we demonstrate a…
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and…
On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip…
A major challenge for plasmonics as an enabling technology for quantum information processing is the realization of active spatio-temporal control of light on the nanoscale. The use of phase-shaped pulses or beams enforces specific…
Photonic nanostructures provide means of tailoring the interaction between light and matter and the past decade has witnessed a tremendous experimental and theoretical progress in this subject. In particular, the combination with…