An electro-thermal computational study of conducting channels in dielectric thin films using self-consistent phase-field methodology: A view toward the physical origins of resistive switching

A large number of experimental studies suggest two-terminal resistive switching devices made of a dielectric thin film sandwiched by a pair of electrodes exhibit reversible multi-state switching behaviors; however coherent understanding of physical and chemical origins of their electrical properties needs to be further pursued to improve and customize the performance. In this paper, phase-field methodology is used to study the formation and annihilation of conductive channels resulting in reversible resistive switching behaviors that can generally occur in any dielectric thin films. Our focus is on the dynamical evolution of domains made of electrical charges under the influence of spatially varying electric field and temperature resulting in distinctive changes in electrical conductance.Comment: 6 pages, 5 figure

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings

Computational Study of Switching Mechanism and Data Retention in Dielectric Thin Film memristor Using Phase-Field Methodology

The possibility of neuro-inspired computing with eNVMs has increased drastically within the last decade as these devices proved to have the required characteristics such as linearity and scalability to be used as synapses in order to bring together memory and computational process in the network. Memristors with metal oxide stack are demonstrated to have increased number of multi-level states, with long-term stability, making them strong candidates to be used as synaptic devices in STDP.Since the conductive path formation in a metal oxide memristor devices plays a major role in training process in Spiking Neural Network, this thesis focuses in using a self-consistent computational phase field method to study conducting channel morphology of resistive switching thin film structures. This approach successfully predicts the formation and annihilation of conducting channels in typical dielectric thin film structures, comparable to a range of resistive switches, offering an alternative computational formulation based on metastable states treated at the atomic scale, as the system is biased by electric field potential, and as the external temperature of the system changes. In contrast to previous resistive switching thin film models, our formulation makes no a priori assumptions on conducting channel morphology and its fundamental transport mechanisms. This study, also, suggests that the generation and growth of nuclei sites in the system due to the influence of external electric field to be one possible root cause of retention failures of ON and OFF states, and eventual reliability degradation of the memristor device

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings