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Memristors for Memory and Computing Applications

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Abstract

Memristive systems are an important class of nanoionic devices whose intriguing properties stem from formation/dissolution of localized conduction channels and have attracted extensive research interests as a disruptive technology for nonvolatile memory, in-memory logic, and brain-inspired computing. Here I will cover three topics regarding memristor research: 1) Mechanism: understandings of the switching mechanism and device dynamics are fundamentally important for guiding future device developments. We have performed systematic TEM studies on metal filaments based ReRAM devices, including both ex situ and in situ observations, on both vertical and planar device structures, in different material systems, and at different dimensions. The results reveal rich information about the structure, composition and chemical state of the filaments, the filament geometry and growth directions, as well as fundamental electrochemical dynamics that govern the ionic transport and filament growth processes [1-3]. We have used electrostatic force microscopy to directly probe oxygen ion migration and accumulation in HfO2 by in situ measurements of electrostatic force gradient between the probe and the sample, as systematically verified by the charge duration, oxygen gas eruption and controlled studies utilizing different electrolytes, field directions and environments. At higher voltages, oxygen–deficient nano-filaments are formed, as directly identified employing a CS-corrected transmission electron microscope [4]. 2) Device optimization: we engineered the analog switching linearity in TaOx based memristors by homogenizing the filament growth/dissolution rate via introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. Important synaptic learning rules in biological brains such as spike timing dependent plasticity was implemented using these optimized devices [5]. In addition, we experimentally demonstrated physically evolving networks in nanoscale, solid-state, multi-terminal memristive devices and heterosynaptic plasticity, an important learning rule found in biological systems, was demonstrated in a system where activity of the modulatory terminal strongly affects the synaptic facilitation/depression between the preand post-synaptic terminals [6]. 3) Integration: Large-scale integration of memristive synapses faces great challenges such as the sneak path problem, which can be mitigated by introducing nonlinearity into the device on-state. We have obtained memristors with on-state nonlinearity by adopting a novel oxide heterostructure where each oxide layer can be tailed for a specific function during resistance switching [7], and by employing functionalized graphene as the electrode that has threshold switching characteristics [8].

© 2017 Optical Society of America

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