arXiv subjects

Vidar Flodgren

Publications and source records attributed to Vidar Flodgren.

4 recordsLinked to original sources

arXiv2026

Controlling the Coupling Strengths in Nanophotonic Networks using Modified Yagi-Uda Nanoplasmonic Antennas

On-chip communication in optical neural networks is commonly done via waveguides which results in large system footprints. A compact alternative is to broadcast light signals between nano-optoelectronic components in free space and tailor the light field distribution using sub-wavelength nanophotonics. We propose and simulate nanoplasmonic metal structures in combination with III-V nanowire emitters and receivers to create an optical network in which the shape and position of these nanostructures create varying weights between the nano-optoelectronic nodes. Using Finite Difference Time Domain modelling, we investigate systems of experimentally verified nanowire optoelectronics combined with nanoplasmonic structures that can be made in the same fabrication step as electrical contacts powering the nanowires. We investigate both individual nanowire/antenna devices as well as networks corresponding to two layers in a neural network. We show that directed communication from a nanowire node can be significantly altered using Yagi-Uda antennas. Modifying the antenna with an asymmetric director component enables the directing of light several different angular directions simultaneously. We find that highly variable complex weight distribution between the connections in two seven node layers can be achieved depending on the combined geometry of the antenna components. The possible weight distributions in compact layers of nanowire nodes could be used for creating a variety of neural networks with complex connectivity. The concepts can be generalized to other types of nanoscale emitter/receiver systems.

physics.optics↗

arXiv2026

Self-selective growth of GaAs1-xBix on GaAs zinc blende/wurtzite nanowire heterostructures

Site-selective nanostructure growth and material incorporation at the atomic scale offer a promising pathway for engineering quantum materials and nanodevices. Here, GaAs nanowires (NWs) with an axial heterostructure of alternating zinc blende (Zb) and wurtzite (Wz) crystal phases are employed as templates for site-selective Ga and Bi overgrowth. Using X-ray photoemission electron microscopy (XPEEM) with nanoscale spatial resolution, we map elemental distribution and local chemical bonding to reveal the incorporation behavior of Bi atoms in {110} Zb and {11-20} Wz facets. Bi incorporation proceeds through an anion-exchange process, where Bi atoms replace As, forming local Ga-Bi bonds and producing a thin GaAs1-xBix shell. We observe crystal-phase-dependent Bi incorporation, with higher Bi concentration in the Zb segments than in the neighboring Wz segments within the same NW. Furthermore, the Zb segment with higher Bi content exhibits reduced susceptibility to oxidation compared with the Wz segment, resulting in increased Ga-oxide in the Wz surfaces. This study highlights GaAs NW Zb/Wz heterostructures as a template for controlled growth of GaBi and GaAs1-xBix nanostructures with tailored functionalities for quantum applications

cond-mat.mtrl-sci↗

arXiv2025

Nanoscale photonic neuron with biological signal processing

Computational hardware designed to mimic biological neural networks holds the promise to resolve the drastically growing global energy demand of artificial intelligence. A wide variety of hardware concepts have been proposed, and among these, photonic approaches offer immense strengths in terms of power efficiency, speed and synaptic connectivity. However, existing solutions have large circuit footprints limiting scaling potential and they miss key biological functions, like inhibition. We demonstrate an artificial nano-optoelectronic neuron with a circuit footprint size reduced by at least a factor of 100 compared to existing technologies and operating powers in the picowatt regime. The neuron can deterministically receive both exciting and inhibiting signals that can be summed and treated with a non-linear function. It demonstrates several biological relevant responses and memory timescales, as well as weighting of input channels. The neuron is compatible with commercial silicon technology, operates at multiple wavelengths and can be used for both computing and optical sensing. This work paves the way for two important research paths: photonic neuromorphic computing with nanosized footprints and low power consumption, and adaptive optical sensing, using the same architecture as a compact, modular front end

cond-mat.mes-hall↗

arXiv2020

Few-cycle lightwave-driven currents in a semiconductor at high repetition rate

When an intense, few-cycle light pulse impinges on a dielectric or semiconductor material, the electric field will interact nonlinearly with the solid, driving a coherent current. An asymmetry of the ultrashort, carrier-envelope-phase-stable waveform results in a net transfer of charge, which can be measured by macroscopic electric contact leads. This effect has been pioneered with extremely short, single-cycle laser pulses at low repetition rate, thus limiting the applicability of its potential for ultrafast electronics. We investigate lightwave-driven currents in gallium nitride using few-cycle laser pulses of nearly twice the duration and at a repetition rate two orders of magnitude higher than in previous work. We successfully simulate our experimental data with a theoretical model based on interfering multiphoton transitions, using the exact laser pulse shape retrieved from dispersion-scan measurements. Substantially increasing the repetition rate and relaxing the constraint on the pulse duration marks an important step forward towards applications of lightwave-driven electronics.

physics.optics↗