Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications

Resistive random-access memory, based on a simple two-terminal device structure, has attracted tremendous interest recently for applications ranging from non-volatile data storage to neuromorphic computing. Resistive switching (RS) effects in RRAM devices originate from internal, microscopic ionic migration and the associated electrochemical processes which modify the materials’ chemical composition and subsequently their electrical and other physical properties. Therefore, controlling the internal ionic transport and redox reaction processes, ideally at the atomic scale, is necessary to optimize the device performance for practical applications with large-size arrays. In this thesis we present our efforts in understanding and controlling the ionic processes in RRAM devices. This thesis presents a comprehensive study on the fundamental understanding on physical mechanism of the ionic processes and the optimization of materials and device structures to achieve desirable device performance based on theoretical calculations and experimental engineering. First, I investigate the electronic structure of Ta2O5 polymorphs, a resistive switching material, and the formation and interaction of oxygen vacancies in amorphous Ta2O5, an important mobile defect responsible for the resistive switching process, using first-principles calculations. Based on the understanding of the fundamental properties of the switching material and the defect, we perform detailed theoretical and experimental analyses that reveal the dynamic vacancy charge transition processes, further helping the design and optimization of the oxide-based RRAM devices. Next, we develop a novel structure including engineered nanoporous graphene to control the internal ionic transport and redox reaction processes at the atomic level, leading to improved device performance. We demonstrate that the RS characteristics can be systematically tuned by inserting a graphene layer with engineered nanopores at a vacancy-exchange interface. The amount of vacancies injected in the switching layer and the size of the conducting filaments can be effectively controlled by the graphene layer working as an atomically-thin ion-blocking material in which ionic transports/reactions are allowed only through the engineered nanosized openings. Lastly, better incremental switching characteristics with improved linearity are obtained through optimization of the switching material density. These improvements allow us to build RRAM crossbar networks for data clustering analysis through unsupervised, online learning in both neuromorphic applications and arithmetic applications in which accurate vector-matrix multiplications are required. We expect the optimization approaches and the optimized devices can be used in other machine learning and arithmetic computing systems, and broaden the range of problems RRAM based network can solve.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146119/1/jihang_1.pd

Robust Simulation of a TaO Memristor Model

This work presents a continuous and differentiable approximation of a Tantalum oxide memristor model which is suited for robust numerical simulations in software. The original model was recently developed at Hewlett Packard labs on the basis of experiments carried out on a memristor manufactured in house. The Hewlett Packard model of the nano-scale device is accurate and may be taken as reference for a deep investigation of the capabilities of the memristor based on Tantalum oxide. However, the model contains discontinuous and piecewise differentiable functions respectively in state equation and Ohm's based law. Numerical integration of the differential algebraic equation set may be significantly facilitated under substitution of these functions with appropriate continuous and differentiable approximations. A detailed investigation of classes of possible continuous and differentiable kernels for the approximation of the discontinuous and piecewise differentiable functions in the original model led to the choice of near optimal candidates. The resulting continuous and differentiable DAE set captures accurately the dynamics of the original model, delivers well-behaved numerical solutions in software, and may be integrated into a commercially-available circuit simulator

A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems

The human brain intrinsically operates with a large number of synapses, more than 10(15). Therefore, one of the most critical requirements for constructing artificial neural networks (ANNs) is to achieve extremely dense synaptic array devices, for which the crossbar architecture containing an artificial synaptic node at each cross is indispensable. However, crossbar arrays suffer from the undesired leakage of signals through neighboring cells, which is a major challenge for implementing ANNs. In this work, we show that this challenge can be overcome by using Pt/TaOy/nanoporous (NP) TaOx/Ta memristor synapses because of their self-rectifying behavior, which is capable of suppressing unwanted leakage pathways. Moreover, our synaptic device exhibits high non-linearity (up to 10(4)), low synapse coupling (S.C, up to 4.00 x 10(-5)), acceptable endurance (5000 cycles at 85 degrees C), sweeping (1000 sweeps), retention stability and acceptable cell uniformity. We also demonstrated essential synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), and spiking-timing-dependent plasticity (STDP), and simulated the recognition accuracy depending on the S.C for MNIST handwritten digit images. Based on the average S.C (1.60 x 10(-4)) in the fabricated crossbar array, we confirmed that our memristive synapse was able to achieve an 89.08% recognition accuracy after only 15 training epochs

Tuning a binary ferromagnet into a multi-state synapse with spin-orbit torque induced plasticity

Inspired by ion-dominated synaptic plasticity in human brain, artificial synapses for neuromorphic computing adopt charge-related quantities as their weights. Despite the existing charge derived synaptic emulations, schemes of controlling electron spins in ferromagnetic devices have also attracted considerable interest due to their advantages of low energy consumption, unlimited endurance, and favorable CMOS compatibility. However, a generally applicable method of tuning a binary ferromagnet into a multi-state memory with pure spin-dominated synaptic plasticity in the absence of an external magnetic field is still missing. Here, we show how synaptic plasticity of a perpendicular ferromagnetic FM1 layer can be obtained when it is interlayer-exchange-coupled by another in-plane ferromagnetic FM2 layer, where a magnetic-field-free current-driven multi-state magnetization switching of FM1 in the Pt/FM1/Ta/FM2 structure is induced by spin-orbit torque. We use current pulses to set the perpendicular magnetization state which acts as the synapse weight, and demonstrate spintronic implementation of the excitatory/inhibitory postsynaptic potentials and spike timing-dependent plasticity. This functionality is made possible by the action of the in-plane interlayer exchange coupling field which leads to broadened, multi-state magnetic reversal characteristics. Numerical simulations, combined with investigations of a reference sample with a single perpendicular magnetized Pt/FM1/Ta structure, reveal that the broadening is due to the in-plane field component tuning the efficiency of the spin-orbit-torque to drive domain walls across a landscape of varying pinning potentials. The conventionally binary FM1 inside our Pt/FM1/Ta/FM2 structure with inherent in-plane coupling field is therefore tuned into a multi-state perpendicular ferromagnet and represents a synaptic emulator for neuromorphic computing.Comment: 37 pages with 11 figures, including 20 pages for manuscript and 17 pages for supplementary informatio

From biomaterial-based data storage to bio-inspired artificial synapse

The implementation of biocompatible and biodegradable information storage would be a significant step toward next-generation green electronics. On the other hand, benefiting from high density, multifunction, low power consumption and multilevel data storage, artificial synapses exhibit attractive future for built-in nonvolatile memories and reconstructed logic operations. Here, we provide a comprehensive and critical review on the developments of bio-memories with a view to inspire more intriguing ideas on this area that may finally open up a new chapter in next-generation consumer electronics. We will discuss that biomolecule-based memory employed evolutionary natural biomaterials as data storage node and artificial synapse emulated biological synapse function, which is expected to conquer the bottleneck of the traditional von Neumann architecture. Finally, challenges and opportunities in the aforementioned bio-memory area are presented