Computational Capacity and Energy Consumption of Complex Resistive Switch Networks

Resistive switches are a class of emerging nanoelectronics devices that exhibit a wide variety of switching characteristics closely resembling behaviors of biological synapses. Assembled into random networks, such resistive switches produce emerging behaviors far more complex than that of individual devices. This was previously demonstrated in simulations that exploit information processing within these random networks to solve tasks that require nonlinear computation as well as memory. Physical assemblies of such networks manifest complex spatial structures and basic processing capabilities often related to biologically-inspired computing. We model and simulate random resistive switch networks and analyze their computational capacities. We provide a detailed discussion of the relevant design parameters and establish the link to the physical assemblies by relating the modeling parameters to physical parameters. More globally connected networks and an increased network switching activity are means to increase the computational capacity linearly at the expense of exponentially growing energy consumption. We discuss a new modular approach that exhibits higher computational capacities and energy consumption growing linearly with the number of networks used. The results show how to optimize the trade-off between computational capacity and energy efficiency and are relevant for the design and fabrication of novel computing architectures that harness random assemblies of emerging nanodevices

Ion beam effect on Ge-Se chalcogenide glass films: Non-volatile memory array formation, structural changes and device performance

The conductive bridge non-volatile memory technology is an emerging way to replace traditional charge based memory devices for future neural networks and configurable logic applications. An array of the memory devices that fulfills logic operations must be developed for implementing such architectures. A scheme to fabricate these arrays, using ion bombardment through a mask, has been suggested and advanced by us. Performance of the memory devices is studied, based on the formation of vias and damage accumulation due to the interactions of Ar+ ions with GexSe1-x (x=0.2, 0.3 and 0.4) chalcogenide glasses as a function of the ion energy and dose dependence. Blanket films and devices were created to study the structural changes, surface roughness, and device performance. Raman Spectroscopy, Atomic Force Microscopy (AFM), Energy Dispersive X-Ray Spectroscopy (EDS) and electrical measurements expound the Ar+ ions behavior on thin films of GexSe1-x system. Raman studies show that there is a decrease in area ratio between edge-shared to corner-shared structural units, revealing occurrence of structural reorganization within the system as a result of ion/film interaction. AFM results demonstrate a tendency in surface roughness improvement with increased Ge concentration, after ion bombardment. EDS results reveal a compositional change in the vias, with a clear tendency of greater interaction between ions and the Ge atoms, as evidenced by greater compositional changes in the Ge rich films

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page