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The innovation introduced in this study consists of replacing toluene with safer solvents such as cyclopentane or diethyl ether in the processing of a preceramic polycarbosilane (allylhydridopolycarbosilane, AHPCS) and assessing its impact on the functionalisation of B4C powders to produce B4C/SiC composites. Fourier-transform infrared (FT-IR) with ATR and nuclear magnetic resonance (NMR) spectroscopy revealed no major modification in the polymer structure. SEC/MALS analysis showed a slight change in the number-average molar mass of the polymer regardless of the functionalisation solvent used in correlation with a slight decrease in the polymer ceramic yield due to oligomer loss. The thermal behaviour of the preceramic polymer investigated via mass spectrometry remained unaffected by the solvent change. The search for polymer residues after distillation highlighted the recyclability of both the functionalisation solvent and the polymer, despite a slight increase in the molar mass of the polymer. Finally, the sinterability of B4C/AHPCS samples was studied with the preparation of B4C/SiC composites via a polymer-derived ceramic (PDC) route and spark plasma sintering (SPS). The effect of the solvent on the microstructure and relative density of the specimens (>92%) is negligible. The specimens retain a fine and homogeneous phase distribution despite process modification. The results highlight the approach developed to use greener solvents for the chemical synthesis of functionalised ceramics and represent a step towards the generalisation of more environmentally friendly processes.

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Spintronic nano-synapses and nano-neurons perform neural network operations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided they implement state-of-the-art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radiofrequency signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly separable radiofrequency inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of-the-art identification of drones from their radiofrequency transmissions, without digitization and consuming only a few milliwatts, which constitutes a gain of several orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.

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Bio-inspired computing represents today a major challenge at different levels ranging from material science for the design of innovative devices and circuits to computer science for the understanding of the key features required for processing of natural data. In this paper, we propose a detail analysis of resistive switching dynamics in electrochemical metallization cells for synaptic plasticity implementation. We show how filament stability associated to joule effect during switching can be used to emulate key synaptic features such as short term to long term plasticity transition and spike timing dependent plasticity. Furthermore, an interplay between these different synaptic features is demonstrated for object motion detection in a spike-based neuromorphic circuit. System level simulation presents robust learning and promising synaptic operation paving the way to complex bio-inspired computing systems composed of innovative memory devices.

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Acyl chloride of N-phthaloyl-(S)-isoleucine is an efficient chiral auxiliary for the resolution of (±)-[2.2]paracyclophane-4-thiol. A preparative protocol, based on the conversion into diastereoisomeric thiolesters and separation by two fractional crystallizations and column chromatography, was developed. Deprotection with LiAlH4 allowed isolation of the individual thiol enantiomers in good yield (∼40%) and high enantiomeric purity (ee >93%). The absolute configurations were determined by comparison of the optical rotation value of the products with literature data and were confirmed by X-ray crystallography.

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Spin-transfer torque magnetic memory (STT-MRAM) is currently under intense academic and industrial development, since it features non-volatility, high write and read speed and high endurance. In this work, we show that when used in a non-conventional regime, it can additionally act as a stochastic memristive device, appropriate to implement a "synaptic" function. We introduce basic concepts relating to spin-transfer torque magnetic tunnel junction (STT-MTJ, the STT-MRAM cell) behavior and its possible use to implement learning-capable synapses. Three programming regimes (low, intermediate and high current) are identified and compared. System-level simulations on a task of vehicle counting highlight the potential of the technology for learning systems. Monte Carlo simulations show its robustness to device variations. The simulations also allow comparing system operation when the different programming regimes of STT-MTJs are used. In comparison to the high and low current regimes, the intermediate current regime allows minimization of energy consumption, while retaining a high robustness to device variations. These results open the way for unexplored applications of STT-MTJs in robust, low power, cognitive-type systems.

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Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.