Interference between postural control and mental task performance in patients with vestibular disorder and healthy controls

OBJECTIVES - To determine whether interference between postural control and mental task performance in patients with balance system impairment and healthy subjects is due to general capacity limitations, motor control interference, competition for spatial processing resources, or a combination of these.METHOD - Postural stability was assessed in 48 patients with vestibular disorder and 24 healthy controls while they were standing with eyes closed on (a) a stable and (b) a moving platform. Mental task performance was measured by accuracy and reaction time on mental tasks, comprising high and low load, spatial and non-spatial tasks. Interference between balancing and performing mental tasks was assessed by comparing baseline (single task) levels of sway and mental task performance with levels while concurrently balancing and carrying out mental tasks.RESULTS - As the balancing task increased in difficulty, reaction times on both low load mental tasks grew progressively longer and accuracy on both high load tasks declined in patients and controls. Postural sway was essentially unaffected by mental activity in patients and controls.CONCLUSIONS - It is unlikely that dual task interference between balancing and mental activity is due to competition for spatial processing resources, as levels of interference were similar in patients with vestibular disorder and healthy controls, and were also similar for spatial and non-spatial tasks. Moreover, the finding that accuracy declined on the high load tasks when balancing cannot be attributed to motor control interference, as no motor control processing is involved in maintaining accuracy of responses. Therefore, interference between mental activity and postural control can be attributed principally to general capacity limitations, and is hence proportional to the attentional demands of both tasks

High Performance Resistance Switching Memory Devices Using Spin-on Silicon Oxide

In this paper, we present high performance resistance switching memory devices (RRAM) with an SiO 2 -like active layer formed from spin-on hydrogen silsesquioxane (HSQ). Our metal-insulator-metal (MIM) devices exhibit switching voltages of less than 1 V, cycling endurances of more than 10 7 cycles without failure, electroforming below 2 V and retention time of resistance states of more than 10 5 seconds at room temperature. We also report arrays of nanoscale HSQ-based RRAM devices in the form of multilayer nanopillars with switching performance comparable to that of our thin film devices. We are able to address and program individual RRAM nanopillars using conductive atomic force microscopy. These promising results, coupled with a much easier fabrication method than traditional ultra-high vacuum based deposition techniques, make HSQ a strong candidate material for the next generation memory devices

Conductance tomography of conductive filaments in intrinsic silicon-rich silica RRAM

We present results from an imaging study of filamentary conduction in silicon suboxide resistive RAM devices. We used a conductive atomic force microscope to etch through devices while measuring current, allowing us to produce tomograms of conductive filaments. To our knowledge this is the first report of such measurements in an intrinsic resistance switching material

Nanosecond analog programming of substoichiometric silicon oxide resistive RAM

Slow access time, high power dissipation and a rapidly approaching scaling limit constitute roadblocks for existing non-volatile flash memory technologies. A new family of storage devices is needed. Filamentary resistive RAM (ReRAM) offers scalability, potentially sub-10nm, nanosecond write times and a low power profile. Importantly, applications beyond binary memories are also possible. Here we look at aspects of the electrical response to nanosecond stimuli of intrinsic resistance switching TiN/SiOx/TiN ReRAM devices. Simple sequences of identical pulses switch devices between two or more states, leading to the possibility of simplified programmers. Impedance mismatch between the device under test and the measurement system allows us to track the electroforming process and confirm it occurs on the nanosecond timescale. Furthermore, we report behavior reminiscent of neuronal synapses (potentiation, depression and short-term memory). Our devices therefore show great potential for integration into novel hardware neural networks

Neuromorphic Dynamics at the Nanoscale in Silicon Suboxide RRAM

Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic applications due to their scalability, low-power operation and diverse functional behaviors. However, understanding the dynamics of individual filaments, and the surrounding material, is challenging, owing to the typically very large cross-sectional areas of test devices relative to the nanometer scale of individual filaments. In the present work, conductive atomic force microscopy is used to study the evolution of conductivity at the nanoscale in a fully CMOS-compatible silicon suboxide thin film. Distinct filamentary plasticity and background conductivity enhancement are reported, suggesting that device behavior might be best described by composite core (filament) and shell (background conductivity) dynamics. Furthermore, constant current measurements demonstrate an interplay between filament formation and rupture, resulting in current-controlled voltage spiking in nanoscale regions, with an estimated optimal energy consumption of 25 attojoules per spike. This is very promising for extremely low-power neuromorphic computation and suggests that the dynamic behavior observed in larger devices should persist and improve as dimensions are scaled down

A nanoscale analysis method to reveal oxygen exchange between environment, oxide, and electrodes in ReRAM devices

The limited sensitivity of existing analysis techniques at the nanometer scale makes it challenging to systematically examine the complex interactions in redox-based resistive random access memory (ReRAM) devices. To test models of oxygen movement in ReRAM devices beyond what has previously been possible, we present a new nanoscale analysis method. Harnessing the power of secondary ion mass spectrometry, the most sensitive surface analysis technique, for the first time, we observe the movement of 16O across electrically biased SiOx ReRAM stacks. We can therefore measure bulk concentration changes in a continuous profile with unprecedented sensitivity. This reveals the nanoscale details of the reversible field-driven exchange of oxygen across the ReRAM stack. Both the reservoir-like behavior of a Mo electrode and the injection of oxygen into the surface of SiOx from the ambient are observed within one profile. The injection of oxygen is controllable through changing the porosity of the SiOx layer. Modeling of the electric fields in the ReRAM stacks is carried out which, for the first time, uses real measurements of both the interface roughness and electrode porosity. This supports our findings helping to explain how and where oxygen from ambient moisture enters devices during operation

Committee Machines—A Universal Method to Deal with Non-Idealities in RRAM-Based Neural Networks

Artificial neural networks (ANNs) are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Recent years have seen an emergence of research in hardware that strives to break the bottleneck of von Neumann architecture and optimise the data flow; namely to bring memory and computing closer together. One of the most often suggested solutions is the physical implementation of ANNs in which their synaptic weights are realised with analogue resistive devices, such as resistive random-access memory (RRAM). 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 machine (CM) -- in the context of RRAM-based neural networks. Using simulations and experimental data from three different types of RRAM devices, we show that CMs employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, programming non-linearities, random telegraph noise, cycle-to-cycle variability and line resistance. Importantly, we show that the accuracy can be improved even without increasing the number of devices

In situ transmission electron microscopy of resistive switching in thin silicon oxide layers

Silicon oxide-based resistive switching devices show great potential for applications in nonvolatile random access memories. We expose a device to voltages above hard breakdown and show that hard oxide breakdown results in mixing of the SiOx layer and the TiN lower contact layers. We switch a similar device at sub-breakdown fields in situ in the transmission electron microscope (TEM) using a movable probe and study the diffusion mechanism that leads to resistance switching. By recording bright-field (BF) TEM movies while switching the device, we observe the creation of a filament that is correlated with a change in conductivity of the SiOx layer. We also examine a device prepared on a microfabricated chip and show that variations in electrostatic potential in the SiOx layer can be recorded using off-axis electron holography as the sample is switched in situ in the TEM. Taken together, the visualization of compositional changes in ex situ stressed samples and the simultaneous observation of BF TEM contrast variations, a conductivity increase, and a potential drop across the dielectric layer in in situ switched devices allow us to conclude that nucleation of the electroforming—switching process starts at the interface between the SiOx layer and the lower contact

Intrinsic resistance switching in amorphous silicon oxide for high performance SiOx ReRAM devices

In this paper, we present a study of intrinsic bipolar resistance switching in metal-oxide-metal silicon oxide ReRAM devices. Devices exhibit low electroforming voltages (typically − 2.6 V), low switching voltages (± 1 V for setting and resetting), excellent endurance of > 107 switching cycles, good state retention (at room temperature and after 1 h at 260 °C), and narrow distributions of switching voltages and resistance states. We analyse the microstructure of amorphous silicon oxide films and postulate that columnar growth, which results from sputter-deposition of the oxide on rough surfaces, enhances resistance switching behavior

Simulation of Inference Accuracy Using Realistic RRAM Devices

Resistive Random Access Memory (RRAM) is a promising technology for power efficient hardware in applications of artificial intelligence (AI) and machine learning (ML) implemented in non-von Neumann architectures. However, there is an unanswered question if the device non-idealities preclude the use of RRAM devices in this potentially disruptive technology. Here we investigate the question for the case of inference. Using experimental results from silicon oxide (SiOx) RRAM devices, that we use as proxies for physical weights, we demonstrate that acceptable accuracies in classification of handwritten digits (MNIST data set) can be achieved using non-ideal devices. We find that, for this test, the ratio of the high- and low-resistance device states is a crucial determinant of classification accuracy, with ~96.8% accuracy achievable for ratios >3, compared to ~97.3% accuracy achieved with ideal weights. Further, we investigate the effects of a finite number of discrete resistance states, sub-100% device yield, devices stuck at one of the resistance states, current/voltage non-linearities, programming non-linearities and device-to-device variability. Detailed analysis of the effects of the non-idealities will better inform the need for the optimization of particular device properties