1,564 research outputs found

    Opening the system to the environment: new theories and tools in classical and quantum settings

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    The thesis is organized as follows. Section 2 is a first, unconventional, approach to the topic of EPs. Having grown interest in the topic of combinatorics and graph theory, I wanted to exploit its very abstract and mathematical tools to reinterpret something very physical, that is, the EPs in wave scattering. To do this, I build the interpretation of scattering events from a graph theory perspective and show how EPs can be understood within this interpretation. In Section 3, I move from a completely classical treatment to a purely quantum one. In this section, I consider two quantum resonators coupled to two baths and study their dynamics with local and global master equations. Here, the EPs are the key physical features used as a witness of validity of the master equation. Choosing the wrong master equation in the regime of interest can indeed mask physical and fundamental features of the system. In Section 4, there are no EPs. However I transition towards a classical/quantum framework via the topic of open systems. My main contribution in this work is the classical stochastic treatment and simulation of a spin coupled to a bath. In this work, I show how a natural quantum--to--classical transition occurs at all coupling strengths when certain limits of spin length are taken. As a key result, I also show how the coupling to the environment in this stochastic framework induces a classical counterpart to quantum coherences in equilibrium. After this last topic, in Section 5, I briefly present the key features of the code I built (and later extended) for the latter project. This, in the form of a Julia registry package named SpiDy.jl, has seen further applications in branching projects and allows for further exploration of the theoretical framework. Finally, I conclude with a discussion section (see Sec. 5) where I recap the different conclusions gathered in the previous sections and propose several possible directions.Engineering and Physical Sciences Research Council (EPSRC

    Accurate quantum transport modelling and epitaxial structure design of high-speed and high-power In0.53Ga0.47As/AlAs double-barrier resonant tunnelling diodes for 300-GHz oscillator sources

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    Terahertz (THz) wave technology is envisioned as an appealing and conceivable solution in the context of several potential high-impact applications, including sixth generation (6G) and beyond consumer-oriented ultra-broadband multi-gigabit wireless data-links, as well as highresolution imaging, radar, and spectroscopy apparatuses employable in biomedicine, industrial processes, security/defence, and material science. Despite the technological challenges posed by the THz gap, recent scientific advancements suggest the practical viability of THz systems. However, the development of transmitters (Tx) and receivers (Rx) based on compact semiconductor devices operating at THz frequencies is urgently demanded to meet the performance requirements calling from emerging THz applications. Although several are the promising candidates, including high-speed III-V transistors and photo-diodes, resonant tunnelling diode (RTD) technology offers a compact and high performance option in many practical scenarios. However, the main weakness of the technology is currently represented by the low output power capability of RTD THz Tx, which is mainly caused by the underdeveloped and non-optimal device, as well as circuit, design implementation approaches. Indeed, indium phosphide (InP) RTD devices can nowadays deliver only up to around 1 mW of radio-frequency (RF) power at around 300 GHz. In the context of THz wireless data-links, this severely impacts the Tx performance, limiting communication distance and data transfer capabilities which, at the current time, are of the order of few tens of gigabit per second below around 1 m. However, recent research studies suggest that several milliwatt of output power are required to achieve bit-rate capabilities of several tens of gigabits per second and beyond, and to reach several metres of communication distance in common operating conditions. Currently, the shortterm target is set to 5−10 mW of output power at around 300 GHz carrier waves, which would allow bit-rates in excess of 100 Gb/s, as well as wireless communications well above 5 m distance, in first-stage short-range scenarios. In order to reach it, maximisation of the RTD highfrequency RF power capability is of utmost importance. Despite that, reliable epitaxial structure design approaches, as well as accurate physical-based numerical simulation tools, aimed at RF power maximisation in the 300 GHz-band are lacking at the current time. This work aims at proposing practical solutions to address the aforementioned issues. First, a physical-based simulation methodology was developed to accurately and reliably simulate the static current-voltage (IV ) characteristic of indium gallium arsenide/aluminium arsenide (In-GaAs/AlAs) double-barrier RTD devices. The approach relies on the non-equilibrium Green’s function (NEGF) formalism implemented in Silvaco Atlas technology computer-aided design (TCAD) simulation package, requires low computational budget, and allows to correctly model In0.53Ga0.47As/AlAs RTD devices, which are pseudomorphically-grown on lattice-matched to InP substrates, and are commonly employed in oscillators working at around 300 GHz. By selecting the appropriate physical models, and by retrieving the correct materials parameters, together with a suitable discretisation of the associated heterostructure spatial domain through finite-elements, it is shown, by comparing simulation data with experimental results, that the developed numerical approach can reliably compute several quantities of interest that characterise the DC IV curve negative differential resistance (NDR) region, including peak current, peak voltage, and voltage swing, all of which are key parameters in RTD oscillator design. The demonstrated simulation approach was then used to study the impact of epitaxial structure design parameters, including those characterising the double-barrier quantum well, as well as emitter and collector regions, on the electrical properties of the RTD device. In particular, a comprehensive simulation analysis was conducted, and the retrieved output trends discussed based on the heterostructure band diagram, transmission coefficient energy spectrum, charge distribution, and DC current-density voltage (JV) curve. General design guidelines aimed at enhancing the RTD device maximum RF power gain capability are then deduced and discussed. To validate the proposed epitaxial design approach, an In0.53Ga0.47As/AlAs double-barrier RTD epitaxial structure providing several milliwatt of RF power was designed by employing the developed simulation methodology, and experimentally-investigated through the microfabrication of RTD devices and subsequent high-frequency characterisation up to 110 GHz. The analysis, which included fabrication optimisation, reveals an expected RF power performance of up to around 5 mW and 10 mW at 300 GHz for 25 μm2 and 49 μm2-large RTD devices, respectively, which is up to five times higher compared to the current state-of-the-art. Finally, in order to prove the practical employability of the proposed RTDs in oscillator circuits realised employing low-cost photo-lithography, both coplanar waveguide and microstrip inductive stubs are designed through a full three-dimensional electromagnetic simulation analysis. In summary, this work makes and important contribution to the rapidly evolving field of THz RTD technology, and demonstrates the practical feasibility of 300-GHz high-power RTD devices realisation, which will underpin the future development of Tx systems capable of the power levels required in the forthcoming THz applications

    Circulation Statistics in Homogeneous and Isotropic Turbulence

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    This is the committee version of a Thesis presented to the PostGrad Program in Physics of the Physics Institute of the Federal University of Rio de Janeiro (UFRJ), as a necessary requirement for the title of Ph.D. in Science (Physics). The development of the Vortex Gas Model (VGM) introduces a novel statistical framework for describing the characteristics of velocity circulation. In this model, the underlying foundations rely on the statistical attributes of two fundamental constituents. The first is a GMC field that governs intermittent behavior and the second constituent is a Gaussian Free field responsible for the partial polarization of the vortices in the gas. The model is revisited in a more sophisticated language, where volume exclusion among vortices is addressed. These additions were subsequently validated through numerical simulations of turbulent Navier-Stokes equations. This revised approach harmonizes with the multifractal characteristics exhibited by circulation statistics, offering a compelling elucidation for the phenomenon of linearization of the statistical circulation moments, observed in recent numerical simulation. In the end, a field theoretical approach, known as Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) functional method is carried out in the context of circulation probability density function. This approach delves into the realm of extreme circulation events, often referred to as Instantons, through two distinct methodologies: The First investigates the linear solutions and, by a renormalization group argument a time-rescaling symmetry is discussed. Secondly, a numerical strategy is implemented to tackle the nonlinear instanton equations in the axisymmetric approximation. This approach addresses the typical topology exhibited by the velocity field associated with extreme circulation events.Comment: Ph.D. Thesis - preliminary versio

    Terahertz Near-Field Microscopy on Resonant Structures and Thin Films

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    Selected problems of materials science. Vol. 2. Nano-dielectrics metals in electronics. Mеtamaterials. Multiferroics. Nano-magnetics

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    The textbook examines physical foundations and practical application of current electronics materials. Modern theories are presented, more important experimental data and specifications of basic materials necessary for practical application are given. Contemporary research in the field of microelectronics and nanophysics is taken into account, while special attention is paid to the influence of the internal structure on the physical properties of materials and the prospects for their use. English-language lectures and other classes on the subject of the book are held at Igor Sikorsky Kyiv Polytechnic Institute at the departments of “Applied Physics” and “Microelectronics” on the subject of materials science, which is necessary for students of higher educational institutions when performing scientific works. For master’s degree applicants in specialty 105 “Applied physics and nanomaterials”.Розглянуто фізичні основи та практичне застосування актуальних матеріалів електроніки. Подано сучасні теорії, наведено найважливіші експериментальні дані та специфікації основних матеріалів, які потрібні для практичного застосування. Враховано сучасні дослідження у галузі мікроелектроніки та нанофізики, при цьому особливу увагу приділено впливу внутрішньої структури на фізичні властивості матеріалів і на перспективи їх використання. Англомовні лекції та інші види занять за тематикою книги проводяться в КПІ ім. Ігоря Сікорського на кафедрах «Прикладна фізика» та «Мікро-електроніка» за напрямом матеріалознавство, що необхідно студентам вищих навчальних закладів при виконанні наукових робіт. Для здобувачів магістратури за спеціальністю 105 «Прикладна фізика та наноматеріали»

    Electron Thermal Runaway in Atmospheric Electrified Gases: a microscopic approach

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    Thesis elaborated from 2018 to 2023 at the Instituto de Astrofísica de Andalucía under the supervision of Alejandro Luque (Granada, Spain) and Nikolai Lehtinen (Bergen, Norway). This thesis presents a new database of atmospheric electron-molecule collision cross sections which was published separately under the DOI : With this new database and a new super-electron management algorithm which significantly enhances high-energy electron statistics at previously unresolved ratios, the thesis explores general facets of the electron thermal runaway process relevant to atmospheric discharges under various conditions of the temperature and gas composition as can be encountered in the wake and formation of discharge channels

    Novel neural architectures & algorithms for efficient inference

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    In the last decade, the machine learning universe embraced deep neural networks (DNNs) wholeheartedly with the advent of neural architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, etc. These models have empowered many applications, such as ChatGPT, Imagen, etc., and have achieved state-of-the-art (SOTA) performance on many vision, speech, and language modeling tasks. However, SOTA performance comes with various issues, such as large model size, compute-intensive training, increased inference latency, higher working memory, etc. This thesis aims at improving the resource efficiency of neural architectures, i.e., significantly reducing the computational, storage, and energy consumption of a DNN without any significant loss in performance. Towards this goal, we explore novel neural architectures as well as training algorithms that allow low-capacity models to achieve near SOTA performance. We divide this thesis into two dimensions: \textit{Efficient Low Complexity Models}, and \textit{Input Hardness Adaptive Models}. Along the first dimension, i.e., \textit{Efficient Low Complexity Models}, we improve DNN performance by addressing instabilities in the existing architectures and training methods. We propose novel neural architectures inspired by ordinary differential equations (ODEs) to reinforce input signals and attend to salient feature regions. In addition, we show that carefully designed training schemes improve the performance of existing neural networks. We divide this exploration into two parts: \textsc{(a) Efficient Low Complexity RNNs.} We improve RNN resource efficiency by addressing poor gradients, noise amplifications, and BPTT training issues. First, we improve RNNs by solving ODEs that eliminate vanishing and exploding gradients during the training. To do so, we present Incremental Recurrent Neural Networks (iRNNs) that keep track of increments in the equilibrium surface. Next, we propose Time Adaptive RNNs that mitigate the noise propagation issue in RNNs by modulating the time constants in the ODE-based transition function. We empirically demonstrate the superiority of ODE-based neural architectures over existing RNNs. Finally, we propose Forward Propagation Through Time (FPTT) algorithm for training RNNs. We show that FPTT yields significant gains compared to the more conventional Backward Propagation Through Time (BPTT) scheme. \textsc{(b) Efficient Low Complexity CNNs.} Next, we improve CNN architectures by reducing their resource usage. They require greater depth to generate high-level features, resulting in computationally expensive models. We design a novel residual block, the Global layer, that constrains the input and output features by approximately solving partial differential equations (PDEs). It yields better receptive fields than traditional convolutional blocks and thus results in shallower networks. Further, we reduce the model footprint by enforcing a novel inductive bias that formulates the output of a residual block as a spatial interpolation between high-compute anchor pixels and low-compute cheaper pixels. This results in spatially interpolated convolutional blocks (SI-CNNs) that have better compute and performance trade-offs. Finally, we propose an algorithm that enforces various distributional constraints during training in order to achieve better generalization. We refer to this scheme as distributionally constrained learning (DCL). In the second dimension, i.e., \textit{Input Hardness Adaptive Models}, we introduce the notion of the hardness of any input relative to any architecture. In the first dimension, a neural network allocates the same resources, such as compute, storage, and working memory, for all the inputs. It inherently assumes that all examples are equally hard for a model. In this dimension, we challenge this assumption using input hardness as our reasoning that some inputs are relatively easy for a network to predict compared to others. Input hardness enables us to create selective classifiers wherein a low-capacity network handles simple inputs while abstaining from a prediction on the complex inputs. Next, we create hybrid models that route the hard inputs from the low-capacity abstaining network to a high-capacity expert model. We design various architectures that adhere to this hybrid inference style. Further, input hardness enables us to selectively distill the knowledge of a high-capacity model into a low-capacity model by cleverly discarding hard inputs during the distillation procedure. Finally, we conclude this thesis by sketching out various interesting future research directions that emerge as an extension of different ideas explored in this work

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Semiconductor-Superconductor Josephson Junctions in the Presence of Zeeman and Spin-Orbit Fields

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    Epitaxially grown Al-InAs hybrids have a great potential for future applications. The most prominent incentive in this regard are potential Majorana zero modes, which are to be believed ideal candidates for fault-tolerant quantum computers. However, with the recent access to these novel materials, it is furthermore possible to conduct experiments on a wide range of generic phenomena. With the help of top-down fabrication, individual designed Josephson junctions offer an unprecedented playground for experimentalists due to the unique combination of the two-dimensional electron gas (2DEG) and the superconductor. This dissertation is about examining of the fundamental building blocks of single Josephson junctions built on such a heterostructure. For this purpose, we elaborated a fabrication process and installed a measurement technique based on a cold RLC resonator in the low MHz regime that is placed in series to the sample. In contrast to the normal resistance, the resonator is a tool which allows us to access the inductance of a superconducting system and thus to probe the supercurrent-carrying Andreev bound states (ABS). The main discoveries of this work include a complete picture of the ABS dependency on various parameters, such as the charge carrier density, the dc current, the magnetic fields, the temperature, or the transparency of the junction, which is close to unity. In the heterostructure, we can break inversion and time-reversal symmetry simultaneously with the interaction of spin-orbit and Zeeman fields. This, in combination with the ballistic character of the Josephson device, leads to a non-reciprocal current that depends on the cross product of current and Zeeman field. Furthermore, we report a rectification effect of the supercurrent even far below the critical temperature of the superconductor. The observed non-reciprocal current is a consequence of a distorted current-phase relation (CPR). Using the inductance, we can display this distortion and derive the novel magnetochiral anisotropy (MCA) coefficient for supercurrents. Moreover, with the MCA coefficient we extract the Dresselhaus component and witness furthermore a peculiar sign change of the MCA at the point where the Zeeman energy is as large as the induced gap. Finally, with the gained understanding and experience of single superconductor-semiconductor Josephson junctions, we create the basis for more complex devices, e.g. multiterminal Josephson junctions (MTJJs). Such junctions with multiple superconducting leads are predicted to host synthetic Weyl singularities in their ABS spectrum. In this work, we present first results of this new topic and show that it is possible to fabricate such MTJJs and to measure their inductance
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