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Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being those that have received the most attention. The presence and quality of the RS phenomenon in a materials system can be studied using different prototype cells, performing different experiments, displaying different figures of merit, and developing different computational analyses. Therefore, the real usefulness and impact of the findings presented in each study for the RS technology will be also different. This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained. The idea is to help the scientific community to evaluate the real usefulness and impact of an RS study for the development of RS technology.

International audienceWe report a study of resistive switching in a silicon-based memristor/resistive RAM (RRAM)device in which the active layer is silicon-rich silica. The resistive switching phenomenon is anintrinsic property of the silicon-rich oxide layer and does not depend on the diffusion of metallicions to form conductive paths. In contrast to other work in the literature, switching occurs inambient conditions, and is not limited to the surface of the active material. We propose a switchingmechanism driven by competing field-driven formation and current-driven destruction offilamentary conductive pathways. We demonstrate that conduction is dominated by trap assistedtunneling through noncontinuous conduction paths consisting of silicon nanoinclusions in a highlynonstoichiometric suboxide phase. We hypothesize that such nanoinclusions nucleate preferentiallyat internal grain boundaries in nanostructured films. Switching exhibits the pinched hysteresis I/Vloop characteristic of memristive systems, and on/off resistance ratios of 104:1 or higher can beeasily achieved. Scanning tunneling microscopy suggests that switchable conductive pathways are10 nm in diameter or smaller. Programming currents can be as low as 2 lA, and transition timesare on the nanosecond scale

The overlap of the principal luminescence band of the erbium ion with the low-loss optical transmission window of silica optical fibres, along with the drive for integration of photonics and silicon technology, has generated intense interest in doping silicon with erbium to produce a silicon-based optical source. Silicon is a poor photonic material due to its very short non-radiative lifetime and indirect band gap, but it has been hoped that the incorporation of optically active erbium ions into silicon will permit the development of silicon-based light sources that will interface with both CMOS technology and optical fibre communications. Some years into this activity, there have now been a wide range of experimental studies of material growth techniques, optical, physical and electrical properties, along with a considerable body of theoretical work dealing with the site of the erbium ion in silicon, along with activation and deactivation processes. This paper reviews the current state of what remains an active field, summarizing results from a range of studies conducted over the last few years, and points to further developments by considering the prospects for successful photonic integration of erbium and silicon.

Resistive switching offers a promising route to universal electronic memory, potentially replacing current technologies that are approaching their fundamental limits. In many cases switching originates from the reversible formation and dissolution of nanometre-scale conductive filaments, which constrain the motion of electrons, leading to the quantisation of device conductance into multiples of the fundamental unit of conductance, G0. Such quantum effects appear when the constriction diameter approaches the Fermi wavelength of the electron in the medium – typically several nanometres. Here we find that the conductance of silicon-rich silica (SiOx) resistive switches is quantised in half-integer multiples of G0. In contrast to other resistive switching systems this quantisation is intrinsic to SiOx, and is not due to drift of metallic ions. Half-integer quantisation is explained in terms of the filament structure and formation mechanism, which allows us to distinguish between systems that exhibit integer and half-integer quantisation.

We repalt the fabrication by PECVD of silicon-rich erbiumdoped silica films that exhibit both 1535 nm f%" and visible photoluminescence. Fluorescence spectra are presented along with absorption spectra that display a strong band edge in the blue, which we ascribe to the presence of Si microclusten. We are unable to observe chamcteristic E?+ absorption bands and propase that excitation of the rare e h is via an energy transfer pmcess from Si microciusters.

Three factors are currently driving the main developments in artificial intelligence (AI): availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. Graphics processing units (GPUs) have been demonstrated as effective co-processors for the implementation of machine learning (ML) algorithms based on deep learning (DL). Solutions based on DL and GPU implementations have led to massive improvements in many AI tasks, but have also caused an exponential increase in demand for computing power. Recent analyses show that the demand for computing power has increased by a factor of 300 000 since 2012, and the estimate is that this demand will double every 3.4 months-at a much faster rate than improvements made historically through Moore's scaling (a sevenfold improvement over the same period of time). [1] At the same time, Moore's law has been slowing down significantly for the last few years, [2] as there are strong indications that we will not be able to continue scaling down complementary metal-oxide-semiconductor (CMOS) transistors. This calls for the exploration of alternative technology roadmaps for the development of scalable and efficient AI solutions. Transistor scaling is not the only way to improve computing performance. Architectural innovations, such as GPUs, field-programmable arrays (FPGAs), and application-specific integrated circuits (ASICs), have all significantly advanced the ML field. [3] A common aspect of modern computing architectures for ML is a move away from the classical von-Neumann architecture that physically separates memory and computing. This approach yields a performance bottleneck that is often the main reason for both energy and speed inefficiency of ML implementations on conventional hardware platforms due to

We have carried out a study of the photoluminescence properties of silicon-rich silica. A series of films grown using plasma enhanced chemical vapor deposition over a range of growth conditions were annealed under argon at selected temperatures. Photoluminescence spectra were measured for each film at room temperature and for selected films at cryogenic temperatures. The photoluminescence spectra exhibit two bands. Fourier transform infrared and electron spin resonance spectroscopies were used to investigate bonding and defect states within the films. The data obtained strongly suggest the presence of two luminescence mechanisms which exhibit different dependencies on film growth conditions and postprocessing. We make assignments of the two mechanisms as ͑1͒ defect luminescence associated with oxygen vacancies and ͑2͒ radiative recombination of electron-hole pairs confined within nanometer-size silicon clusters ͑''quantum confinement''͒.