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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.
Quantum cascade lasers (QCL) have revolutionized the generation of mid-infrared light. Yet, the ultrafast carrier transport in mid-infrared QCLs has so far constituted a seemingly insurmountable obstacle for the formation of ultrashort light pulses.
Lead-magnesium niobate lead-titanate (PMN-PT) has been proven as an excellent material for sensing and actuating applications. The fabrication of advanced ultra-small PMN-PT-based devices relies on the availability of sophisticated procedures for the
The dispersion scan (d-scan) technique has emerged as a simple-to-implement characterization method for ultrashort laser pulses. D-scan traces are intuitive to interpret and retrieval algorithms that are both fast and robust have been developed to ob
We report on experimental results in a new regime of a relativistic light-matter interaction employing mid-infrared (3.9-micrometer wavelength) high-intensity femtosecond laser pulses. In the laser generated plasma, the electrons reach relativistic e
We study a model for frustrated tunneling ionization using ultrashort laser pulses. The model is based on the strong field approximation and it employs the saddle point approximation to predict quasiclassical trajectories that are captured on Rydberg