<p dir="ltr">The limitations of the traditional von Neumann computing architecture, particularly evident in the slowdown of Moore's law, have spurred the development of alternative domain-specific computing paradigms. This dissertation explores novel materials-physics based solutions for two promising alternatives: quantum computing and probabilistic computing, with a specific focus on leveraging magnetic spin textures and their unique properties. We demonstrate that magnetic spin textures, with their inherent topology and chirality, offer distinctive advantages in addressing key challenges in both computing paradigms. These textures' ability to couple with various degrees of freedom, such as electrical, thermal, mechanical, and optical, makes them particularly suitable for hybrid device implementations. Our work presents four contributions to the field.</p><p dir="ltr">First, we propose a novel approach of using skyrmions --- topologically protected rigid-object like spin textures --- to nucleate and braid Majorana modes in topological superconductor-magnetic multilayer heterostructures. We show analytically and numerically that skyrmion--vortex bound pairs can be braided in experimentally relevant timescales. Inspired by circuit quantum electrodynamics methods, we propose a novel readout scheme based on the dispersive coupling between vortex confinement states and Majorana bound states. This work paves the way for experimentally demonstrating the non-Abelian statistics of Majorana bound states, which might be a crucial step towards the development of fault-tolerant topological quantum computers.</p><p dir="ltr">Second, we study thermal relaxation mechanisms and timescales of spin-split chiral antiferromagnets. The class of spin-split antiferromagnets, including altermagnets, have recently emerged as excellent candidates for ultra-fast and low-energy spintronics applications. Due the lack of dipolar order, they are unaffected by stray fields. However, the spin-split bands still offer electrical control and readout of these antiferromagnets unlike the conventional antiferromagnets. While a lot of promising phenomena in these materials has already been experimentally demonstrated, thermal relaxation mechanisms of such magnets remain unexplored. Using reaction rate theories and statistical physics tools, we study the thermal dynamics of chiral antiferromagnets. We show that these materials thermally relax at ultra-fast picosecond-order timescales. Further, by building on the analogy between XY magnets and current biased Josephson junctions, we propose a novel approach to electrically tune the thermal barrier in chiral antiferromagnets. Although such chiral antiferromagnets may not be suitable for non-volatile memory type of applications, they emerge as promising candidates for the building blocks of probabilistic computers.</p><p dir="ltr">We then turn our attention to the strongly correlated quantum system of quantum spin liquids. We show that spin textures exchange coupled to a Kitaev spin liquid (KSL) can induce emergent gauge fields on the Majorana fermions in the spin liquid. These emergent gauge fields may trap zero energy modes if they are able to thread a net flux through the KSL. We derive analytical expressions for the gauge fields in the presence of spin textures and outline the conditions to obtain a net flux. Zero energy Majorana fermion modes trapped on such spin textures may eventually be used for fault tolerant quantum computing.</p><p dir="ltr">Finally, in the last project, we bring the quantum and probabilistic computing paradigms together by proposing a quantum two level system as a sensor for the building blocks of a probabilistic computer. we show that quantum spin defects such as Nitrogen vacancy centers (NV) can be used as novel probes for characterizing probabilistic bits. We show that various NV sensing protocols can be leveraged to create a complete picture of this nascent magnet based probabilistic bits including their energy barrier and attempt times.</p><p dir="ltr">Our findings suggest that magnetic spin textures, particularly their topological and chiral properties, could provide crucial solutions to current challenges in alternative computing platforms. This work bridges the gap between materials physics, device physics and the applications in alternative computing platforms.</p>

<p>Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks. </p> <p> </p> <p>Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.</p> <p> </p> <p>We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver. </p> <p> </p> <p>Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.</p>

<p dir="ltr">Probabilistic computing is a novel computing scheme that offers a more efficient approach than conventional complimentary metal-oxide-semiconductor (CMOS)-based logic in a variety of applications ranging from Bayesian inference to combinatorial optimization, and invertible Boolean logic. These applications, which have found use in the rapidly growing fields of machine learning and artificial intelligence, are traditionally computationally-intensive and so make the push for novel computing schemes that are intrinsically low-power and scalable all the more urgent.</p><p dir="ltr">The probabilistic bit (or p-bit, the base unit of probabilistic computing) is a naturally fluctuating entity that requires <i>tunable </i>stochasticity; low-barrier nanomagnets, in which the magnetic moment fluctuates randomly and continuously due to the presence of thermal energy, are a natural vehicle for providing the core functionality required. This dissertation describes the work done in mining the rich field of spintronics to produce devices that can act as natural hardware accelerators for probabilistic computing algorithms.</p><p dir="ltr">First, experiments exploring Fe₃O₄ nanoparticles as naturally stochastic systems are presented. Using NV center measurements on an array of such nanoparticles, it is shown that they fluctuate intrinsically at GHz frequencies at room temperature; these fluctuations could be harnessed to act as a stochastic noise source, and would, in principle, enable fast computation.</p><p dir="ltr">The focus then shifts to the development of a platform that allows for easier <i>electrical</i> readout: the low-barrier magnetic tunnel junction (MTJ). We show the work done in the development and characterization of these devices, how they respond to non-ideal environments, such as elevated temperatures and exposure to high-energy electromagnetic radiation, how their intrinsic stochasticity might be tuned with electrical currents and external magnetic fields, and then how these might be integrated with a simple transistor circuit to produce a compact low-energy implementation of a p-bit.</p><p dir="ltr">Next, by integrating our stochastic MTJs with 2D-MoS₂field-effect transistors (FETs), the first <i>on-chip </i>realization of a key p-bit building block, displaying voltage-controllable stochasticity, is demonstrated. This is followed by another key demonstration through the fabrication of stochastic MTJs directly on top of an integrated circuit platform, where the transistor circuitry is provided by 180nm-node CMOS technology.</p><p dir="ltr">In addition, supported by circuit simulations, this work provides a careful device-level analysis of the three transistor-one magnetic tunnel junction (3T-1MTJ) p-bit design, evaluating how the characteristics of each component can influence the overall p-bit’s output. In particular, we show that – against common wisdom – a large tunnel magnetoresistance (TMR) is not the best choice for p-bits; bimodal telegraphic fluctuations are highly undesirable and are a sign of a slow device; and an ideal inverter with a large gain is unsuitable for p-bit applications due to the higher likelihood of unwanted plateaus in the resulting p-bit’s output.</p><p dir="ltr">This analysis is extended to consider the impact of such non-ideal p-bits when used to construct probabilistic circuits, with the focus on the emulation of the Boolean logic AND gate through a three p-bit correlated system. It is found that a probabilistic circuit made with ideal p-bits can accurately emulate the function of an AND gate, while the non-ideal p-circuits suffer from an increased error rate in emulating the AND gate’s truth table.</p><p dir="ltr">The understanding gained at the individual device level, in what makes a good or bad MTJ, to how the different components of the 3T-1MTJ p-bit can affect its output, and subsequently how non-ideal p-bits can impact circuit performance, can be important for the future realization of scaled on-chip p-bit networks.</p>