Damage threshold of LiF crystal irradiated by femtosecond hard XFEL pulse sequence.

Here we demonstrate the results of investigating the damage threshold of a LiF crystal after irradiating it with a sequence of coherent femtosecond pulses using the European X-ray Free Electron Laser (EuXFEL). The laser fluxes on the crystal surface varied in the range ∼ 0.015-13 kJ/cm2 per pulse when irradiated with a sequence of 1-100 pulses (tpulse ∼ 20 fs, Eph = 9 keV). Analysis of the surface of the irradiated crystal using different reading systems allowed the damage areas and the topology of the craters formed to be accurately determined. It was found that the ablation threshold decreases with increasing number of X-ray pulses, while the depth of the formed craters increases non-linearly and reaches several hundred nanometers. The obtained results have been compared with data already available in the literature for nano- and picosecond pulses from lasers in the soft X-ray/VUV and optical ranges. A failure model of lithium fluoride is developed and verified with simulation of material damage under single-pulse irradiation. The obtained damage threshold is in reasonably good agreement with the experimentally measured one.

Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing.

Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-p-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100-x Nanocomposite Memristors.

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100-x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.