New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and ...noisy data that we are generating at an ever-increasing rate. To realize this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?
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.
Abstract
Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of ...research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artificial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science—committee machines—in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.
Cycle-to-cycle (C2C) current variability occurring in ReRAM devices is not only a stochastic feature inherent to electron transport in low-dimensional conducting structures but also a consequence of ...the measurement protocol used to characterize the device evolution during resistance switching. In such latest case, C2C changes depend on the particular arrangement of the ions or vacancies that form the conducting filament spanning the dielectric film. In this letter, a discrete first-order autoregressive model AR(1) with long-term variation is used to represent both the random and the "deterministic" behaviors of the high resistance state current. Simulation of C2C instabilities in SiO x is carried out through the quantum point-contact model for filamentary electron transport in dielectrics with fluctuating confinement potential barrier height. Simplicity is of utmost importance, since the proposed approach is aimed for circuit simulation environments in which complex and time-consuming computations need to be avoided.
We studied intrinsic resistance switching behaviour in sputter-deposited amorphous silicon suboxide (a-SiO
) films with varying degrees of roughness at the oxide-electrode interface. By combining ...electrical probing measurements, atomic force microscopy (AFM), and scanning transmission electron microscopy (STEM), we observe that devices with rougher oxide-electrode interfaces exhibit lower electroforming voltages and more reliable switching behaviour. We show that rougher interfaces are consistent with enhanced columnar microstructure in the oxide layer. Our results suggest that columnar microstructure in the oxide will be a key factor to consider for the optimization of future SiOx-based resistance random access memory.
Columnar microstructures are critical for obtaining good resistance switching properties in SiOx resistive random access memory (ReRAM) devices. In this work, the formation and structure of columnar ...boundaries are studied in sputtered SiOx layers. Using TEM measurements, we analyze SiOx layers in Me–SiOx–Mo heterostructures, where Me = Ti or Au/Ti. We show that the SiOx layers are templated by the Mo surface roughness, leading to the formation of columnar boundaries protruding from troughs at the SiOx/Mo interface. Electron energy-loss spectroscopy measurements show that these boundaries are best characterized as voids, which in turn facilitate Ti, Mo, and Au incorporation from the electrodes into SiOx. Density functional theory calculations of a simple model of the SiO2 grain boundary and column boundary show that O interstitials preferentially reside at the boundaries rather than in the SiO2 bulk. The results elucidate the nature of the SiOx microstructure and the complex interactions between the metal electrodes and the switching oxide, each of which is critically important for further materials engineering and the optimization of ReRAM devices.
Resistive random access memory (RRAM) is considered an attractive candidate for next generation memory devices due to its competitive scalability, low-power operation and high switching speed. The ...technology however, still faces several challenges that overall prohibit its industrial translation, such as low yields, large switching variability and ultimately hard breakdown due to long-term operation or high-voltage biasing. The latter issue is of particular interest, because it ultimately leads to device failure. In this work, we have investigated the physicochemical changes that occur within RRAM devices as a consequence of soft and hard breakdown by combining full-field transmission x-ray microscopy with soft x-ray spectroscopic analysis performed on lamella samples. The high lateral resolution of this technique (down to 25 nm) allows the investigation of localized nanometric areas underneath permanent damage of the metal top electrode. Results show that devices after hard breakdown present discontinuity in the active layer, Pt inclusions and the formation of crystalline phases such as rutile, which indicates that the temperature increased locally up to 1000 K.
We report that implanting argon ions into a film of uniform atomic layer deposition (ALD)-grown SiO
x
enables electroforming and switching within films that previously failed to electroform at ...voltages <15 V. We note an implantation dose dependence of electroforming success rate: electroforming can be eliminated when the dosage is high enough. Our devices are capable of multi-level switching during both set and reset operations, and multiple resistance states can be retained for more than 30,000 s under ambient conditions. High endurance of more than 7 million (7.9 × 10
6
) cycles is achieved alongside low switching voltages (±1 V). Comparing SiO
x
fabricated by this approach with sputtered SiO
x
we find similar conduction mechanisms between the two materials. Our results show that intrinsic SiO
x
switching can be achieved with defects created solely by argon bombardment; in contrast to defects generated during deposition, implantation generated defects are potentially more controllable. In the future, noble ion implantation into silicon oxide may allow optimization of already excellent resistance switching devices.
Filamentary conduction in resistive switching metal- insulator-metal devices is often modeled from the circuital viewpoint using diode-like structures with series resistances. We show in this letter ...which arrangement of diodes and resistances is compatible with experimental multilevel set and reset I-V characteristics in electroformed TiN/SiO x /TiN structures. The proposed model is based on the solution of the generalized diode equation corresponding to N diodes arranged in parallel with a single series resistance. The model is simple yet accurate and it is able to capture the essential features exhibited by the I-V curves in the low and high bias regimes, revealing that a single equation can deal with both the low and high resistance states. An exact expression for the differential conductance suitable for small-signal analysis and circuit simulators is also provided.
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