ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
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- All Subjects: Physics
- Creators: Vasileska, Dragica
Description
ABSTRACT An Ensemble Monte Carlo (EMC) computer code has been developed to simulate, semi-classically, spin-dependent electron transport in quasi two-dimensional (2D) III-V semiconductors. The code accounts for both three-dimensional (3D) and quasi-2D transport, utilizing either 3D or 2D scattering mechanisms, as appropriate. Phonon, alloy, interface roughness, and impurity scattering mechanisms are included, accounting for the Pauli Exclusion Principle via a rejection algorithm. The 2D carrier states are calculated via a self-consistent 1D Schrödinger-3D-Poisson solution in which the charge distribution of the 2D carriers in the quantization direction is taken as the spatial distribution of the squared envelope functions within the Hartree approximation. The wavefunctions, subband energies, and 2D scattering rates are updated periodically by solving a series of 1D Schrödinger wave equations (SWE) over the real-space domain of the device at fixed time intervals. The electrostatic potential is updated by periodically solving the 3D Poisson equation. Spin-polarized transport is modeled via a spin density-matrix formalism that accounts for D'yakanov-Perel (DP) scattering. Also, the code allows for the easy inclusion of additional scattering mechanisms and structural modifications to devices. As an application of the simulator, the current voltage characteristics of an InGaAs/InAlAs HEMT are simulated, corresponding to nanoscale III-V HEMTs currently being fabricated by Intel Corporation. The comparative effects of various scattering parameters, material properties and structural attributes are investigated and compared with experiments where reasonable agreement is obtained. The spatial evolution of spin-polarized carriers in prototypical Spin Field Effect Transistor (SpinFET) devices is then simulated. Studies of the spin coherence times in quasi-2D structures is first investigated and compared to experimental results. It is found that the simulated spin coherence times for GaAs structures are in reasonable agreement with experiment. The SpinFET structure studied is a scaled-down version of the InGaAs/InAlAs HEMT discussed in this work, in which spin-polarized carriers are injected at the source, and the coherence length is studied as a function of gate voltage via the Rashba effect.
ContributorsTierney, Brian David (Author) / Goodnick, Stephen (Thesis advisor) / Ferry, David (Committee member) / Akis, Richard (Committee member) / Saraniti, Marco (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2011
Description
Light Emitting Diodes even with their longer life, robust build and low power consumption, they are still plagued by some problems the most significant of which are the current droop and thermal droop. Current droop causes a lowering in the Internal Quantum Efficiency with increased current injection while thermal droop lowers the whole Internal Quantum Efficiency curve with increase in temperature. The focus here was understanding effects of thermal droop and develop a method to control it.
Shockley Read Hall recombination plays a dominant role in the thermal droop effect when the current injection is low. Since the blue light emitting diode is based on Gallium Nitride, we need to take into consideration the effect of piezoelectric polarization in the quantum wells. The effects of the piezoelectric fields were studied based on the Gallium Nitride plane orientations. It was found in a Gallium Nitride light emitting diodes simulation study that more the number of quantum wells, lower would be the Radiative recombination rate. The problem of exacerbated spatial separation of electron hole wavefunctions in a thick single quantum well structure lead to the development of a dual well structure where one well assisted the other during high temperature operations. The Electron Blocking Layer was reduced in thickness and was made only 10 nm thick with a 5 nm Gallium Nitride buffer between it and the active region wells. The main reason for reducing the electron blocking layer thickness was to reduce the valance band offset and improve hole transport into the active region. Three different dual well designs were simulated of 3nm, 6nm and 9nm wide wells. The output parameters like the Power Spectral Density, Electron bound density, Light Output Power and Electron-Hole wavefunction overlaps were calculated. It was found that one of the wells acted as an assisting well where it had very little radiative recombination activity in it at room temperature.
As the temperature increased, it was observed that the electrons in the main well started to overflow out of it and into the assisting well where the radiative recombination rate increased significantly. This lead to a boost in Internal Quantum Efficiency.
Shockley Read Hall recombination plays a dominant role in the thermal droop effect when the current injection is low. Since the blue light emitting diode is based on Gallium Nitride, we need to take into consideration the effect of piezoelectric polarization in the quantum wells. The effects of the piezoelectric fields were studied based on the Gallium Nitride plane orientations. It was found in a Gallium Nitride light emitting diodes simulation study that more the number of quantum wells, lower would be the Radiative recombination rate. The problem of exacerbated spatial separation of electron hole wavefunctions in a thick single quantum well structure lead to the development of a dual well structure where one well assisted the other during high temperature operations. The Electron Blocking Layer was reduced in thickness and was made only 10 nm thick with a 5 nm Gallium Nitride buffer between it and the active region wells. The main reason for reducing the electron blocking layer thickness was to reduce the valance band offset and improve hole transport into the active region. Three different dual well designs were simulated of 3nm, 6nm and 9nm wide wells. The output parameters like the Power Spectral Density, Electron bound density, Light Output Power and Electron-Hole wavefunction overlaps were calculated. It was found that one of the wells acted as an assisting well where it had very little radiative recombination activity in it at room temperature.
As the temperature increased, it was observed that the electrons in the main well started to overflow out of it and into the assisting well where the radiative recombination rate increased significantly. This lead to a boost in Internal Quantum Efficiency.
ContributorsDas, Shiladitya (Author) / Zhao, Yuji (Thesis advisor) / Vasileska, Dragica (Committee member) / Ning, Cun-Zheng (Committee member) / Arizona State University (Publisher)
Created2017
Description
This dissertation aims to study and understand the effect of nonlinear dynamics and quantum chaos in graphene, optomechanics, photonics and spintronics systems.
First, in graphene quantum dot systems, conductance fluctuations are investigated from the respects of Fano resonances and quantum chaos. The conventional semi-classical theory of quantum chaotic scattering used in this field depends on an invariant classical phase-space structure. I show that for systems without an invariant classical phase-space structure, the quantum pointer states can still be used to explain the conductance fluctuations. Another finding is that the chaotic geometry is demonstrated to have similar effects as the disorders in transportations.
Second, in optomechanics systems, I find rich nonlinear dynamics. Using the semi-classical Langevin equations, I demonstrate a quasi-periodic motion is favorable for the quantum entanglement between the optical mode and mechanical mode. Then I use the quantum trajectory theory to provide a new resolution for the breakdown of the classical-quantum correspondences in the chaotic regions.
Third, I investigate the analogs of the electrical band structures and effects in the non-electrical systems. In the photonic systems, I use an array of waveguides to simulate the transport of the massive relativistic particle in a non-Hermitian scenario. A new form of Zitterbewegung is discovered as well as its analytical explanation. In mechanical systems, I use springs and mass points systems to achieve a three band degenerate band structure with a new pair of spatially separated edge states in the Dice lattice. A new semi-metal phase with the intrinsic valley-Hall effect is found.
At last, I investigate the nonlinear dynamics in the spintronics systems, in which the topological insulator couples with a magnetization. Rich nonlinear dynamics are discovered in this systems, especially the multi-stability states.
First, in graphene quantum dot systems, conductance fluctuations are investigated from the respects of Fano resonances and quantum chaos. The conventional semi-classical theory of quantum chaotic scattering used in this field depends on an invariant classical phase-space structure. I show that for systems without an invariant classical phase-space structure, the quantum pointer states can still be used to explain the conductance fluctuations. Another finding is that the chaotic geometry is demonstrated to have similar effects as the disorders in transportations.
Second, in optomechanics systems, I find rich nonlinear dynamics. Using the semi-classical Langevin equations, I demonstrate a quasi-periodic motion is favorable for the quantum entanglement between the optical mode and mechanical mode. Then I use the quantum trajectory theory to provide a new resolution for the breakdown of the classical-quantum correspondences in the chaotic regions.
Third, I investigate the analogs of the electrical band structures and effects in the non-electrical systems. In the photonic systems, I use an array of waveguides to simulate the transport of the massive relativistic particle in a non-Hermitian scenario. A new form of Zitterbewegung is discovered as well as its analytical explanation. In mechanical systems, I use springs and mass points systems to achieve a three band degenerate band structure with a new pair of spatially separated edge states in the Dice lattice. A new semi-metal phase with the intrinsic valley-Hall effect is found.
At last, I investigate the nonlinear dynamics in the spintronics systems, in which the topological insulator couples with a magnetization. Rich nonlinear dynamics are discovered in this systems, especially the multi-stability states.
ContributorsWang, Guanglei (Author) / Lai, Ying-Cheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Ning, Cun-Zheng (Committee member) / Zhao, Yuji (Committee member) / Arizona State University (Publisher)
Created2017
Forward and Backward Machine Learning for Modeling Copper Diffusion in Cadmium Telluride Solar Cells
Description
To optimize solar cell performance, it is necessary to properly design the doping profile in the absorber layer of the solar cell. For CdTe solar cells, Cu is used for providing p-type doping. Hence, having an estimator that, given the diffusion parameter set (time and Temperature) and the doping concentration at the junction, gives the junction depth of the absorber layer, is essential in the design process of CdTe solar cells (and other cell technologies). In this work it is called a forward (direct) estimation process. The backward (inverse) problem then is the one in which, given the junction depth and the desired concentration of Cu doping at the CdTe/CdS heterointerface, the estimator gives the time and/or the Temperature needed to achieve the desired doping profiles. This is called a backward (inverse) estimation process. Such estimators, both forward and backward, do not exist in the literature for solar cell technology. To train the Machine Learning (ML) estimator, it is necessary to first generate a large set of data that are obtained by using the PVRD-FASP Solver, which has been validated via comparison with experimental values. Note that this big dataset needs to be generated only once. Next, one uses Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) to extract the actual Cu doping profiles that result from the process of diffusion, annealing, and cool-down in the fabrication sequence of CdTe solar cells. Two deep learning neural network models are used: (1) Multilayer Perceptron Artificial Neural Network (MLPANN) model using a Keras Application Programmable Interface (API) with TensorFlow backend, and (2) Radial Basis Function Network (RBFN) model to predict the Cu doping profiles for different Temperatures and durations of the annealing process. Excellent agreement between the simulated results obtained with the PVRD-FASP Solver and the predicted values is obtained. It is important to mention here that it takes a significant amount of time to generate the Cu doping profiles given the initial conditions using the PVRD-FASP Solver, because solving the drift-diffusion-reaction model is mathematically a stiff problem and leads to numerical instabilities if the time steps are not small enough, which, in turn, affects the time needed for completion of one simulation run. The generation of the same with Machine Learning (ML) is almost instantaneous and can serve as an excellent simulation tool to guide future fabrication of optimal doping profiles in CdTe solar cells.
ContributorsSalman, Ghaith (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen M. (Thesis advisor) / Ringhofer, Christian (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Self-heating degrades the performance of devices in advanced technology nodes. Understanding of self-heating effects is necessary to improve device performance. Heat generation in these devices occurs at nanometer scales but heat transfer is a microscopic phenomena. Hence a multi-scale modeling approach is required to study the self-heating effects. A state of the art Monte Carlo device simulator and the commercially available Giga 3D tool from Silvaco are used in our study to understand the self heating effects. The Monte Carlo device simulator solves the electrical transport and heat generation for nanometer length scales accurately while the Giga 3D tool solves for thermal transport over micrometer length scales. The approach used is to understand the self-heating effects in a test device structure, composed of a heater and a sensor, fabricated and characterized by IMEC. The heater is the Device Under Test(DUT) and the sensor is used as a probe. Therefore, the heater is biased in the saturation region and the sensor is biased in the sub-threshold regime. Both are planar MOSFETs of gate length equal to 22 nm. The simulated I-V characteristics of the sensor match with the experimental behavior at lower applied drain voltages but differ at higher applied biases.
The self-heating model assumes that the heat transport within the device follows Energy Balance model which may not be accurate. To properly study heat transport within the device, a state of the art Monte Carlo device simulator is necessary. In this regard, the Phonon Monte Carlo(PMC) simulator is developed. Phonons are treated as quasi particles that carry heat energy. Like electrons, phonons obey a corresponding Boltzmann Transport Equation(BTE) which can be used to study their transport. The direct solution of the BTE for phonons is possible, but it is difficult to incorporate all scattering mechanisms. In the Monte Carlo based solution method, it is easier to incorporate different relevant scattering mechanisms. Although the Monte Carlo method is computationally intensive, it provides good insight into the physical nature of the transport problem. Hence Monte Carlo based techniques are used in the present work for studying phonon transport. Monte Carlo simulations require calculating the scattering rates for different scattering processes. In the present work, scattering rates for three phonon interactions are calculated from different approaches presented in the literature. Optical phonons are also included in the transport problem. Finally, the temperature dependence of thermal conductivity for silicon is calculated in the range from 100K to 900K and is compared to available experimental data.
The self-heating model assumes that the heat transport within the device follows Energy Balance model which may not be accurate. To properly study heat transport within the device, a state of the art Monte Carlo device simulator is necessary. In this regard, the Phonon Monte Carlo(PMC) simulator is developed. Phonons are treated as quasi particles that carry heat energy. Like electrons, phonons obey a corresponding Boltzmann Transport Equation(BTE) which can be used to study their transport. The direct solution of the BTE for phonons is possible, but it is difficult to incorporate all scattering mechanisms. In the Monte Carlo based solution method, it is easier to incorporate different relevant scattering mechanisms. Although the Monte Carlo method is computationally intensive, it provides good insight into the physical nature of the transport problem. Hence Monte Carlo based techniques are used in the present work for studying phonon transport. Monte Carlo simulations require calculating the scattering rates for different scattering processes. In the present work, scattering rates for three phonon interactions are calculated from different approaches presented in the literature. Optical phonons are also included in the transport problem. Finally, the temperature dependence of thermal conductivity for silicon is calculated in the range from 100K to 900K and is compared to available experimental data.
ContributorsShaik, Abdul Rawoof (Author) / Vasileska, Dragica (Thesis advisor) / Ferry, David (Committee member) / Goodnick, Stephan (Committee member) / Arizona State University (Publisher)
Created2016