Charge-Spin Conversion and Electronic Transport in Two-Dimensional Materials and van der Waals Heterostructures

Applications related to artificial intelligence (AI), 5G communication, cloud computing, Internet of Things (IoT) will necessitate wide range of data collection, communication and processing. Current charge-based technology using conventional materials suffers adverse effects with down-scaling the device size and has limited efficiency in meeting the future demands for computation and data storage. The exploration of alternative device technology along with new materials is important to enhance computing performance and energy efficiency. In this thesis, I investigated new materials for future memory and logic technologies.\ua0 Recently developed 2D materials such as graphene, semiconductors, and semimetals exhibit remarkable new properties that promise faster and energy efficient non-volatile memory and logic functionalities. For non-volatile memory technologies, increasing efforts are being directed towards exploiting charge-spin conversion phenomena in high spin-orbit coupling (SOC) materials to realize all-electric magnetic memory. Interestingly, magnetic memory devices have been demonstrated on an industrial scale; however, the moderate efficiency and fundamental limitations of the conventional materials employed limit their use in consumer electronics. This thesis addresses some of these critical challenges and presents charge-spin conversion mechanisms in layered high SOC materials such as topological insulators, semimetals, and two-dimensional (2D) materials heterostructures. At the same time, this thesis contributes in the direction of integrating memory and logic devices by investigating 2D semiconductor devices with sub-20 nm narrow channel width and memristive switching in field-effect transistors using 2D semiconductors with graphene contacts. Such 2D semiconductors have enormous prospects for next-generation high-performance and energy-efficient nanoscale field-effect transistors and integration with memory technologies. These studies of charge and spin transport in 2D materials and heterostructures can open the door for nanometer-scale memory, logic and sensing technologies

Giant Hall Switching by Surface-State-Mediated Spin-Orbit Torque in a Hard Ferromagnetic Topological Insulator

Topological insulators (TI) can apply highly efficient spin-orbit torque (SOT) and manipulate the magnetization with their unique topological surface states, and their magnetic counterparts, magnetic topological insulators (MTI) offer magnetization without shunting and are one of the highest in SOT efficiency. Here, we demonstrate efficient SOT switching of a hard MTI, V-doped (Bi,Sb)2Te3 (VBST) with a large coercive field that can prevent the influence of an external magnetic field and a small magnetization to minimize stray field. A giant switched anomalous Hall resistance of 9.2 $k\Omega$ is realized, among the largest of all SOT systems. The SOT switching current density can be reduced to $2.8\times10^5 A/cm^2$, and the switching ratio can be enhanced to 60%. Moreover, as the Fermi level is moved away from the Dirac point by both gate and composition tuning, VBST exhibits a transition from edge-state-mediated to surface-state-mediated transport, thus enhancing the SOT effective field to $1.56\pm 0.12 T/ (10^6 A/cm^2)$ and the spin Hall angle to $23.2\pm 1.8$ at 5 K. The findings establish VBST as an extraordinary candidate for energy-efficient magnetic memory devices

The 2017 Magnetism Roadmap

Building upon the success and relevance of the 2014 Magnetism Roadmap, this 2017 Magnetism Roadmap edition follows a similar general layout, even if its focus is naturally shifted, and a different group of experts and, thus, viewpoints are being collected and presented. More importantly, key developments have changed the research landscape in very relevant ways, so that a novel view onto some of the most crucial developments is warranted, and thus, this 2017 Magnetism Roadmap article is a timely endeavour. The change in landscape is hereby not exclusively scientific, but also reflects the magnetism related industrial application portfolio. Specifically, Hard Disk Drive technology, which still dominates digital storage and will continue to do so for many years, if not decades, has now limited its footprint in the scientific and research community, whereas significantly growing interest in magnetism and magnetic materials in relation to energy applications is noticeable, and other technological fields are emerging as well. Also, more and more work is occurring in which complex topologies of magnetically ordered states are being explored, hereby aiming at a technological utilization of the very theoretical concepts that were recognised by the 2016 Nobel Prize in Physics. Given this somewhat shifted scenario, it seemed appropriate to select topics for this Roadmap article that represent the three core pillars of magnetism, namely magnetic materials, magnetic phenomena and associated characterization techniques, as well as applications of magnetism. While many of the contributions in this Roadmap have clearly overlapping relevance in all three fields, their relative focus is mostly associated to one of the three pillars. In this way, the interconnecting roles of having suitable magnetic materials, understanding (and being able to characterize) the underlying physics of their behaviour and utilizing them for applications and devices is well illustrated, thus giving an accurate snapshot of the world of magnetism in 2017. The article consists of 14 sections, each written by an expert in the field and addressing a specific subject on two pages. Evidently, the depth at which each contribution can describe the subject matter is limited and a full review of their statuses, advances, challenges and perspectives cannot be fully accomplished. Also, magnetism, as a vibrant research field, is too diverse, so that a number of areas will not be adequately represented here, leaving space for further Roadmap editions in the future. However, this 2017 Magnetism Roadmap article can provide a frame that will enable the reader to judge where each subject and magnetism research field stands overall today and which directions it might take in the foreseeable future. The first material focused pillar of the 2017 Magnetism Roadmap contains five articles, which address the questions of atomic scale confinement, 2D, curved and topological magnetic materials, as well as materials exhibiting unconventional magnetic phase transitions. The second pillar also has five contributions, which are devoted to advances in magnetic characterization, magneto-optics and magneto-plasmonics, ultrafast magnetization dynamics and magnonic transport. The final and application focused pillar has four contributions, which present non-volatile memory technology, antiferromagnetic spintronics, as well as magnet technology for energy and bio-related applications. As a whole, the 2017 Magnetism Roadmap article, just as with its 2014 predecessor, is intended to act as a reference point and guideline for emerging research directions in modern magnetism

Leveraging the Intrinsic Switching Behaviors of Spintronic Devices for Digital and Neuromorphic Circuits

With semiconductor technology scaling approaching atomic limits, novel approaches utilizing new memory and computation elements are sought in order to realize increased density, enhanced functionality, and new computational paradigms. Spintronic devices offer intriguing avenues to improve digital circuits by leveraging non-volatility to reduce static power dissipation and vertical integration for increased density. Novel hybrid spintronic-CMOS digital circuits are developed herein that illustrate enhanced functionality at reduced static power consumption and area cost. The developed spin-CMOS D Flip-Flop offers improved power-gating strategies by achieving instant store/restore capabilities while using 10 fewer transistors than typical CMOS-only implementations. The spin-CMOS Muller C-Element developed herein improves asynchronous pipelines by reducing the area overhead while adding enhanced functionality such as instant data store/restore and delay-element-free bundled data asynchronous pipelines. Spintronic devices also provide improved scaling for neuromorphic circuits by enabling compact and low power neuron and non-volatile synapse implementations while enabling new neuromorphic paradigms leveraging the stochastic behavior of spintronic devices to realize stochastic spiking neurons, which are more akin to biological neurons and commensurate with theories from computational neuroscience and probabilistic learning rules. Spintronic-based Probabilistic Activation Function circuits are utilized herein to provide a compact and low-power neuron for Binarized Neural Networks. Two implementations of stochastic spiking neurons with alternative speed, power, and area benefits are realized. Finally, a comprehensive neuromorphic architecture comprising stochastic spiking neurons, low-precision synapses with Probabilistic Hebbian Plasticity, and a novel non-volatile homeostasis mechanism is realized for subthreshold ultra-low-power unsupervised learning with robustness to process variations. Along with several case studies, implications for future spintronic digital and neuromorphic circuits are presented