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

Iodine Vacancy Redistribution in Organic–Inorganic Halide Perovskite Films and Resistive Switching Effects

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138254/1/adma201700527_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138254/2/adma201700527-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138254/3/adma201700527.pd

Effects of Training Using Self-Modeling with Visual Cues on Skill Performance, Imagery, and Sports Confidence of Adolescent Female Soccer Players

PURPOSE This study aimed to examine the effects of motion analysis and image training using self-modeling with visual cues on the skill performance, imagery, and sports confidence of adolescent female soccer players. METHODS The participants were elite soccer players from two girls’ high school soccer teams divided into an experimental group (D girls’ high school, n=16) and a control group (I girls’ high school, n=13). The experimental group underwent motion analysis and image training when performing penalty kicks, short kicks, and long kicks using self-modeling with visual cues, while the control group underwent training using self-modeling videos without visual cues. Before and after the training, the evaluation score was calculated according to kick performance, and the imagery and sports confidence factors were measured. For the statistical analysis of all collected data, descriptive statistics, the Friedman test, the Mann-Whitney U test, and two-way repeated-measures analysis of variance were used. RESULTS First, on the motion analysis using self-modeling with visual cues, the experimental group’s penalty kick and short kick scores were improved and differed significantly, but no significant change was noted in long kick score. Second, as a result of image training using self-modeling with visual cues, all visual, kinesthetic, mood, and controllability factors of the experimental group improved except for the auditory factor, and the interaction effect was confirmed. In addition, the stated sports confidence of the experimental group was improved and the interaction effect confirmed. CONCLUSIONS The analysis of kick motion using self-modeling with visual cues was effective for the penalty kicks and short kicks of adolescent female soccer players. Moreover, this study confirmed that the analysis of kick motion improved the visual, kinesthetic, mood, and controllability sub-factors of imagery and significantly affected the players’ stated sports confidence

UniXGen: A Unified Vision-Language Model for Multi-View Chest X-ray Generation and Report Generation

Generated synthetic data in medical research can substitute privacy and security-sensitive data with a large-scale curated dataset, reducing data collection and annotation costs. As part of this effort, we propose UniXGen, a unified chest X-ray and report generation model, with the following contributions. First, we design a unified model for bidirectional chest X-ray and report generation by adopting a vector quantization method to discretize chest X-rays into discrete visual tokens and formulating both tasks as sequence generation tasks. Second, we introduce several special tokens to generate chest X-rays with specific views that can be useful when the desired views are unavailable. Furthermore, UniXGen can flexibly take various inputs from single to multiple views to take advantage of the additional findings available in other X-ray views. We adopt an efficient transformer for computational and memory efficiency to handle the long-range input sequence of multi-view chest X-rays with high resolution and long paragraph reports. In extensive experiments, we show that our unified model has a synergistic effect on both generation tasks, as opposed to training only the task-specific models. We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays. The source code is publicly available at: https://github.com/ttumyche/UniXGen

Phonon Renormalization in Reconstructed MoS2 Moire Superlattices

In moiré crystals formed by stacking van der Waals (vdW) materials, surprisingly diverse correlated electronic phases and optical properties can be realized by a subtle change in the twist angle. Here, we discover that phonon spectra are also renormalized in MoS2 twisted bilayers, adding a new perspective to moiré physics. Over a range of small twist angles, the phonon spectra evolve rapidly due to ultra-strong coupling between different phonon modes and atomic reconstructions of the moiré pattern. We develop a new low-energy continuum model for phonons that overcomes the outstanding challenge of calculating properties of large moiré supercells and successfully captures essential experimental observations. Remarkably, simple optical spectroscopy experiments can provide information on strain and lattice distortions in moiré crystals with nanometer-size supercells. The newly developed theory promotes a comprehensive and unified understanding of structural, optical, and electronic properties of moiré superlattices.The spectroscopy experiments at UT-Austin (J.Q.) were primarily funded by the U.S. Department of Energy, Office of Basic Energy Sciences under grant DE-SC0019398 and a grant from the University of Texas. Material preparation was funded by the Welch Foundation via grant F-1662. The collaboration between the X.L., C.S., K.L., and M.A. groups is facilitated by the NSF-MRSEC under DMR- 1720595, which funded J.C. and J.E. partially. L.L. and F.L. acknowledge support by the TU-D doctoral program of TU Wien, as well as from the Austrian Science Fund (FWF), project I-3827. The authors ac- knowledge discussions with S. Reichardt and the use of facilities and instrumentation supported by the National Science Foundation through the Center for Dynamics and Control of Materials: an NSF MRSEC under Cooperative Agreement No. DMR-1720595. P.T. acknowledges support from the National Natural Science Foundation of China (Grant No.11874350) and CAS Key Research Program of Frontier Sciences (Grant No. ZDBS-LY-SLH004). M.L. acknowledge the support from the Project funded by China Post- doctoral Science Foundation (Grant No. 2019TQ0317). The PFM work (D.L. and K.L.) was supported by NSF DMR-2004536 and Welch Foundation Grant F-1814. K.W. and T.T. acknowledge support from the Elemental Strategy Initiative conducted by the MEXT, Japan, Grant Number JPMXP0112101001, JSPS KAKENHI Grant Numbers JP20H00354 and the CREST(JPMJCR15F3), JST.Center for Dynamics and Control of Material

Perovskite Films: Iodine Vacancy Redistribution in Organic–Inorganic Halide Perovskite Films and Resistive Switching Effects (Adv. Mater. 29/2017)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138369/1/adma201770213.pd

Tuning Resistive Switching Characteristics of Tantalum Oxide Memristors through Si Doping

An oxide memristor device changes its internal state according to the history of the applied voltage and current. The principle of resistive switching (RS) is based on ion transport (<i>e.g.</i>, oxygen vacancy redistribution). To date, devices with bi-, triple-, or even quadruple-layered structures have been studied to achieve the desired switching behavior through device structure optimization. In contrast, the device performance can also be tuned through fundamental atomic-level design of the switching materials, which can directly affect the dynamic transport of ions and lead to optimized switching characteristics. Here, we show that doping tantalum oxide memristors with silicon atoms can facilitate oxygen vacancy formation and transport in the switching layer with adjustable ion hopping distance and drift velocity. The devices show larger dynamic ranges with easier access to the intermediate states while maintaining the extremely high cycling endurance (>10¹⁰ set and reset) and are well-suited for neuromorphic computing applications. As an example, we demonstrate different flavors of spike-timing-dependent plasticity in this memristor system. We further provide a characterization methodology to quantitatively estimate the effective hopping distance of the oxygen vacancies. The experimental results are confirmed through detailed <i>ab initio</i> calculations which reveal the roles of dopants and provide design methodology for further optimization of the RS behavior