770 research outputs found

    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 ÎŒm2 and 49 ÎŒm2-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

    Data-Driven Exploration of Coarse-Grained Equations: Harnessing Machine Learning

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    In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in this thesis is to develop methodologies that can automatically extract simplified equations by training models using available data. ML algorithms have the ability to learn underlying patterns and relationships within the data, making it possible to extract simplified equations that capture the essential behavior of the system. This study considers three distinct learning categories: black-box, gray-box, and white-box learning. The initial phase of the research focuses on black-box learning, where no prior information about the equations is available. Three different neural network architectures are explored: multi-layer perceptron (MLP), convolutional neural network (CNN), and a hybrid architecture combining CNN and long short-term memory (CNN-LSTM). These neural networks are applied to uncover the non-linear equations of motion associated with phase-field models, which include both non-conserved and conserved order parameters. The second architecture explored in this study addresses explicit equation discovery in gray-box learning scenarios, where a portion of the equation is unknown. The framework employs eXtended Physics-Informed Neural Networks (X-PINNs) and incorporates domain decomposition in space to uncover a segment of the widely-known Allen-Cahn equation. Specifically, the Laplacian part of the equation is assumed to be known, while the objective is to discover the non-linear component of the equation. Moreover, symbolic regression techniques are applied to deduce the precise mathematical expression for the unknown segment of the equation. Furthermore, the final part of the thesis focuses on white-box learning, aiming to uncover equations that offer a detailed understanding of the studied system. Specifically, a coarse parametric ordinary differential equation (ODE) is introduced to accurately capture the spreading radius behavior of Calcium-magnesium-aluminosilicate (CMAS) droplets. Through the utilization of the Physics-Informed Neural Network (PINN) framework, the parameters of this ODE are determined, facilitating precise estimation. The architecture is employed to discover the unknown parameters of the equation, assuming that all terms of the ODE are known. This approach significantly improves our comprehension of the spreading dynamics associated with CMAS droplets

    Radiation Tolerant Electronics, Volume II

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    Research on radiation tolerant electronics has increased rapidly over the last few years, resulting in many interesting approaches to model radiation effects and design radiation hardened integrated circuits and embedded systems. This research is strongly driven by the growing need for radiation hardened electronics for space applications, high-energy physics experiments such as those on the large hadron collider at CERN, and many terrestrial nuclear applications, including nuclear energy and safety management. With the progressive scaling of integrated circuit technologies and the growing complexity of electronic systems, their ionizing radiation susceptibility has raised many exciting challenges, which are expected to drive research in the coming decade.After the success of the first Special Issue on Radiation Tolerant Electronics, the current Special Issue features thirteen articles highlighting recent breakthroughs in radiation tolerant integrated circuit design, fault tolerance in FPGAs, radiation effects in semiconductor materials and advanced IC technologies and modelling of radiation effects

    Machine Learning and Its Application to Reacting Flows

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    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    Advanced Technologies for Biomass

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    The use of biomass and organic waste material as a primary resource for the production of fuels, chemicals, and electric power is of growing significance in light of the environmental issues associated with the use of fossil fuels. For this reason, it is vital that new and more efficient technologies for the conversion of biomass are investigated and developed. Today, various advanced methods can be used for the conversion of biomass. These methods are broadly classified into thermochemical conversion, biochemical conversion, and electrochemical conversion. This book collects papers that consider various aspects of sustainability in the conversion of biomass into valuable products, covering all the technical stages from biomass production to residue management. In particular, it focuses on experimental and simulation studies aiming to investigate new processes and technologies on the industrial, pilot, and bench scales
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