arXiv subjects

A. Chakravarty

Publications and source records attributed to A. Chakravarty.

3 recordsLinked to original sources

arXiv2021

Training and pattern recognition by an opto-magnetic neural network

Neuromorphic computing aims to mimic the architecture of the human brain to carry out computational tasks that are challenging and much more energy consuming for standard hardware. Despite progress in several fields of physics and engineering, the realization of artificial neural networks which combine high operating speeds with fast and low-energy adaptability remains a challenge. Here we demonstrate an opto-magnetic neural network capable of learning and classification of digitized 3x3 characters exploiting local storage in the magnetic material. Using picosecond laser pulses, we find that micrometer sized synapses absorb well below 100 picojoule per synapse per laser pulse, with favorable scaling to smaller spatial dimensions. We thus succeeded in combining the speed and low-dissipation of optical networks with the low-energy adaptability and non-volatility of magnetism, providing a promising approach to fast and energy-efficient neuromorphic computing.

cs.ET↗

arXiv2019

Supervised learning of an opto-magnetic neural network with ultrashort laser pulses

The explosive growth of data and its related energy consumption is pushing the need to develop energy-efficient brain-inspired schemes and materials for data processing and storage. Here, we demonstrate experimentally that Co/Pt films can be used as artificial synapses by manipulating their magnetization state using circularly-polarized ultrashort optical pulses at room temperature. We also show an efficient implementation of supervised perceptron learning on an opto-magnetic neural network, built from such magnetic synapses. Importantly, we demonstrate that the optimization of synaptic weights can be achieved using a global feedback mechanism, such that the learning does not rely on external storage or additional optimization schemes. These results suggest there is high potential for realizing artificial neural networks using optically-controlled magnetization in technologically relevant materials, that can learn not only fast but also energy-efficient.

cs.ET↗

arXiv2018

TMOKE as efficient tool for the magneto-optic analysis of ultra-thin magnetic films

Ultra-thin magnetic dielectric films are of prime importance due to their applications for nanophotonics and spintronics. Here, we propose an efficient method for the magneto-optical investigation of ultra-thin magnetic films which allows one to access their state of magnetization and magneto-optical properties. It is based on the surface-plasmon-polariton-assisted transverse magneto-optical Kerr effect (TMOKE). In our experiments, sub-100 nm-thick bismuth-substituted lutetium iron-garnet films covered with a plasmonic gold grating have been analyzed. The excitation of surface plasmon-polaritons provides resonance enhancement of TMOKE up to 0.04 and makes it easily detectable in the experiment. For films thicker than 40 nm, the TMOKE marginally depends on the film thickness. A further decrease in the film thickness diminishes TMOKE since for such thicknesses the surface plasmon-polariton field partly penetrates inside the non-magnetic substrate. Nevertheless, the TMOKE remains measurable even for few-nm-thick films, which makes this technique unique for the magneto-optical study of ultra-thin films. Particularly, the proposed method reveals that the off-diagonal components of the magnetic film permittivity tensor grow slightly with the reduction of the film thickness.

physics.ins-det↗