In situ chamber for studying battery failure using high-speed synchrotron radiography

The investigation of lithium-ion battery failures is a major challenge for personnel and equipment due to the associated hazards (thermal reaction, toxic gases and explosions). To perform such experiments safely, a battery abuse-test chamber has been developed and installed at the microtomography beamline ID19 of the European Synchrotron Radiation Facility (ESRF). The chamber provides the capability to robustly perform in situ abuse tests through the heat-resistant and gas-tight design for flexible battery geometries and configurations, including single-cell and multi-cell assemblies. High-speed X-ray imaging can be complemented by supplementary equipment, including additional probes (voltage, pressure and temperature) and thermal imaging. Together with the test chamber, a synchronization graphical user interface was developed, which allows an initial interpretation by time-synchronous visualization of the acquired data. Enabled by this setup, new meaningful insights can be gained into the internal processes of a thermal runaway of current and future energy-storage devices such as lithium-ion cells.

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.

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 (SiO_x) 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.

The interplay between structure and function in redox-based resistance switching

We report a study of the relationship between oxide microstructure at the scale of tens of nanometres and resistance switching behaviour in silicon oxide.

Investigation of resistance switching in SiO x RRAM cells using a 3D multi-scale kinetic Monte Carlo simulator

We employ an advanced three-dimensional (3D) electro-thermal simulator to explore the physics and potential of oxide-based resistive random-access memory (RRAM) cells. The physical simulation model has been developed recently, and couples a kinetic Monte Carlo study of electron and ionic transport to the self-heating phenomenon while accounting carefully for the physics of vacancy generation and recombination, and trapping mechanisms. The simulation framework successfully captures resistance switching, including the electroforming, set and reset processes, by modeling the dynamics of conductive filaments in the 3D space. This work focuses on the promising yet less studied RRAM structures based on silicon-rich silica (SiO _x ) RRAMs. We explain the intrinsic nature of resistance switching of the SiO _x layer, analyze the effect of self-heating on device performance, highlight the role of the initial vacancy distributions acting as precursors for switching, and also stress the importance of using 3D physics-based models to capture accurately the switching processes. The simulation work is backed by experimental studies. The simulator is useful for improving our understanding of the little-known physics of SiO _x resistive memory devices, as well as other oxide-based RRAM systems (e.g. transition metal oxide RRAMs), offering design and optimization capabilities with regard to the reliability and variability of memory cells.

Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices

Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiO_x) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.

Nanoscale Transformations in Metastable, Amorphous, Silicon-Rich Silica

Electrically biasing thin films of amorphous, substoichiometric silicon oxide drives surprisingly large structural changes, apparent as density variations, oxygen movement, and ultimately, emission of superoxide ions. Results from this fundamental study are directly relevant to materials that are increasingly used in a range of technologies, and demonstrate a surprising level of field-driven local reordering of a random oxide network.

X-ray spectromicroscopy investigation of soft and hard breakdown in RRAM 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.

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.