|学科||机器人与智能机器 Robotics and Intelligent Machines|
|国家/州||United States of America|
A Kinetic Monte Carlo Study of the Scalability and Variability of the Forming Voltage of Transition Metal Oxide ReRAMs
I developed a fast kinetic Monte Carlo (kMC) simulator and applied it to a study of the geometrical scaling and statistical properties of resistive random access memories (ReRAMs). These devices are under intense investigation because they are promising alternatives to flash-based nonvolatile memories, which are not expected to scale to dimensions below ~20nm. "Forming" is performed just after manufacturing to functionalize the ReRAM by creating a conductive filament whose resistance is then modulated to encode "0" or "1" memory states. Since forming is a one-time process and since the underlying physics is stochastic in nature, statistically meaningful experimental characterizations of filament formation are difficult to perform and are not available.
I addressed this problem by developing a simulator that captures the unique physics of ReRAMs: strongly coupled ionic and electronic transport. I treat the electronic effects using equivalent resistor networks, and oxygen vacancy/ion effects using kMC. The distribution of vacancies determines the linear/nonlinear elements of the resistor network, and the heat generated by electron flow in this network determines the vacancy/ion generation rates in kMC. Using this simulator, I found that the critical voltage V_f at which the filament forms depends roughly linearly on device thickness and logarithmically on device width. I motivate the thickness dependence using an effective field argument, and then offer a plausible statistical argument to explain the width dependence. I also investigated the effects of (a) external temperature, (b) voltage ramp rate and (c) maximum current at forming on V_f and its variability.
英特尔国际科学与工程大奖赛，简称 "ISEF"，由美国 Society for Science and the Public（科学和公共服务协会）主办，英特尔公司冠名赞助，是全球规模最大、等级最高的中学生的科研科创赛事。ISEF 的竞赛学科包括了所有数学、自然科学、工程的全部领域和部分社会科学。ISEF 素有全球青少年科学竞赛的“世界杯”之美誉，旨在鼓励学生团队协作，开拓创新，长期专一深入地研究自己感兴趣的课题。
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· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·
Studies in which the use of machine intelligence is paramount to reducing the reliance on human intervention.
Biomechanics (BIE): Studies and apparatus which mimic the role of mechanics in biological systems.
Cognitive Systems (COG): Studies/apparatus that operate similarly to the ways humans think and process information. Systems that provide for increased interaction of people and machines to more naturally extend and magnify human expertise, activity, and cognition.
Control Theory (CON): Studies that explore the behavior of dynamical systems with inputs, and how their behavior is modified by feedback. This includes new theoretical results and the applications of new and established control methods, system modelling, identification and simulation, the analysis and design of control systems (including computer-aided design), and practical implementation.
Machine Learning (MAC): Construction and/or study of algorithms that can learn from data.
Robot Kinematics (KIN): The study of movement in robotic systems.
Other (OTH): Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.