Spin dynamics in semiconductors

This article reviews the current status of spin dynamics in semiconductors which has achieved a lot of progress in the past years due to the fast growing field of semiconductor spintronics. The primary focus is the theoretical and experimental developments of spin relaxation and dephasing in both spin precession in time domain and spin diffusion and transport in spacial domain. A fully microscopic many-body investigation on spin dynamics based on the kinetic spin Bloch equation approach is reviewed comprehensively.Comment: a review article with 193 pages and 1103 references. To be published in Physics Reports

Terahertz and mid-infrared photodetectors based on intersubband transitions in novel materials systems

The terahertz (THz) and mid-infrared (MIR) spectral regions have many potential applications in the industrial, biomedical, and military sectors. Yet, a wide portion of this region of the electromagnetic spectrum (particularly the THz range) is still relatively unexplored, due mainly to the absence of suitable sources and photodetectors, related to the lack of practical semiconductor materials with adequately small band gap energies. Intersubband transitions (ISBTs) between quantized energy states in quantum heterostructures provide tunable wavelengths over a broad spectral range including the THz region, by choosing appropriate layer thicknesses and compositions. This work focuses on the development of THz and MIR Quantum Well Infrared Photodetectors (QWIPs) based on ISBTs in GaN/AlGaN and Si/SiGe heterostructures. Due to their large optical phonon energies, GaN materials allow extending the spectral reach of existing far-infrared photodetectors based on GaAs, and may enable higher-temperature operation. In the area of MIR optoelectronic devices, I have focused on developing QWIPs based on ISBTs in Si/SiGe heterostructures in the form of on strain-engineered nanomembranes. Due to their non-polar nature, these materials are free from reststrahlen absorption and ultrafast resonant electron/phonon scattering, unlike traditional III-V semiconductors. Therefore, Si/SiGe quantum wells (QWs) are also promising candidates for high-temperature high-performance ISB device operation (particularly in the THz region), with the additional advantage of direct integration with CMOS technology. In this thesis work, numerical modeling is used to design the active region of the proposed devices, followed by sample fabrication and characterization based on lock-in step-scan Fourier transform infrared spectroscopy. Three specific QWIP devices have been developed. The first is a III-nitride THz QWIP based on a novel double-step QW design in order to alleviate the material limitations provided by the intrinsic electric fields of GaN/AlGaN heterostructures. Next, I have developed a THz GaN/AlGaN QWIP grown on semi-polar (202 ̅1 ̅) GaN, where the detrimental effects of the internal fields are almost completely eliminated. Finally, I have demonstrated a Si/SiGe MIR QWIP based on a novel fabrication approach, where nanomembrane strain engineering is used to address the materials quality issues normally found in SiGe QWs. Promising photodetector performance is obtained in all cases.2017-06-21T00:00:00

Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

abstract: Digital imaging and image processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. In image-based applications, through different processing stages (e.g., acquisition, compression, and transmission), images are subjected to different types of distortions which degrade their visual quality. Image Quality Assessment (IQA) attempts to use computational models to automatically evaluate and estimate the image quality in accordance with subjective evaluations. Moreover, with the fast development of computer vision techniques, it is important in practice to extract and understand the information contained in blurred images or regions. The work in this dissertation focuses on reduced-reference visual quality assessment of images and textures, as well as perceptual-based spatially-varying blur detection. A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The proposed method requires a very small number of reduced-reference (RR) features. Extensive experiments performed on different benchmark databases demonstrate that the proposed RRIQA method, delivers highly competitive performance as compared with the state-of-the-art RRIQA models for both natural and texture images. In the context of texture, the effect of texture granularity on the quality of synthesized textures is studied. Moreover, two RR objective visual quality assessment methods that quantify the perceived quality of synthesized textures are proposed. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metrics outperforms full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures. Last but not least, an effective approach to address the spatially-varying blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings is proposed. The evaluations of the proposed approach on a diverse sets of blurry images with different blur types, levels, and content demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Spintronics: Fundamentals and applications

Spintronics, or spin electronics, involves the study of active control and manipulation of spin degrees of freedom in solid-state systems. This article reviews the current status of this subject, including both recent advances and well-established results. The primary focus is on the basic physical principles underlying the generation of carrier spin polarization, spin dynamics, and spin-polarized transport in semiconductors and metals. Spin transport differs from charge transport in that spin is a nonconserved quantity in solids due to spin-orbit and hyperfine coupling. The authors discuss in detail spin decoherence mechanisms in metals and semiconductors. Various theories of spin injection and spin-polarized transport are applied to hybrid structures relevant to spin-based devices and fundamental studies of materials properties. Experimental work is reviewed with the emphasis on projected applications, in which external electric and magnetic fields and illumination by light will be used to control spin and charge dynamics to create new functionalities not feasible or ineffective with conventional electronics.Comment: invited review, 36 figures, 900+ references; minor stylistic changes from the published versio

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performances. This study proposes a novel approach to narrow this gap and propose a real-time solution with shallow and compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs)

Asymmetric quantum well structures for enhanced infrared photon absorption

Compared to inter-band transition for photon absorption in a quantum wells, intra-band (or inter-subband) transitions in heterojunction (GaAs/InP) quantum wells can provide access to a broader range of wavelengths for detector design, specifically detectors operating in the mid infrared region of spectrum (4-12 [micro]m) and beyond is possible. These quantum wells not only provide great flexibility in optimizing the Eigen energy levels or wavefunctions, and inter-subband optical matrix elements determining the corresponding transition probability, but also allow controlling electron-phonon scattering rates and thus electron lifetime. The research presented in this dissertation investigates asymmetric quantum well structures formed through III-V semiconductor material system such as AlGaAs/ InxGa(1-x)As/InyGa(1-y) As/AlGaAs that can further improve the responsivity through higher carrier mobility. Asymmetry is introduced by using multiple materials to form the well region. The advantage of exploring stepped quantum well structure stems from experimental evidence that such structures are capable of absorbing normal incidence and thus eliminates the requirement of incorporating additional optical coupling schemes such as grating structures. An important contribution of this research is the development of an analytical model to analyze single or multiple quantum well structures to quantify photon absorption. The physical model developed in this work is based on non-equilibrium Green's function (NEGF), Fermi's golden rule and quantum mechanical wave impedance concept. The approach has two distinct advantages. First, it is accurate, easily programmable and yet computationally efficient. Second, it facilitates quantifying the broadening of states resulting from both photon absorption and tunneling, which provides important insight for improving detection efficiency. Instead of being presented through calculations, such broadening has been simply assumed in previously reported works. The method developed in this researcIncludes bibliographical references (pages 112-113)

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits