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- All Subjects: engineering
- Creators: Sugar, Thomas
Description
Hydrogen sulfide (H2S) has been identified as a potential ingredient for grain boundary passivation of multicrystalline silicon. Sulfur is already established as a good surface passivation material for crystalline silicon (c-Si). Sulfur can be used both from solution and hydrogen sulfide gas. For multicrystalline silicon (mc-Si) solar cells, increasing efficiency is a major challenge because passivation of mc-Si wafers is more difficult due to its randomly orientated crystal grains and the principal source of recombination is contributed by the defects in the bulk of the wafer and surface.
In this work, a new technique for grain boundary passivation for multicrystalline silicon using hydrogen sulfide has been developed which is accompanied by a compatible Aluminum oxide (Al2O3) surface passivation. Minority carrier lifetime measurement of the passivated samples has been performed and the analysis shows that success has been achieved in terms of passivation and compared to already existing hydrogen passivation, hydrogen sulfide passivation is actually better. Also the surface passivation by Al2O3 helps to increase the lifetime even more after post-annealing and this helps to attain stability for the bulk passivated samples. Minority carrier lifetime is directly related to the internal quantum efficiency of solar cells. Incorporation of this technique in making mc-Si solar cells is supposed to result in higher efficiency cells. Additional research is required in this field for the use of this technique in commercial solar cells.
In this work, a new technique for grain boundary passivation for multicrystalline silicon using hydrogen sulfide has been developed which is accompanied by a compatible Aluminum oxide (Al2O3) surface passivation. Minority carrier lifetime measurement of the passivated samples has been performed and the analysis shows that success has been achieved in terms of passivation and compared to already existing hydrogen passivation, hydrogen sulfide passivation is actually better. Also the surface passivation by Al2O3 helps to increase the lifetime even more after post-annealing and this helps to attain stability for the bulk passivated samples. Minority carrier lifetime is directly related to the internal quantum efficiency of solar cells. Incorporation of this technique in making mc-Si solar cells is supposed to result in higher efficiency cells. Additional research is required in this field for the use of this technique in commercial solar cells.
ContributorsSaha, Arunodoy, Ph.D (Author) / Tao, Meng (Thesis advisor) / Vasileska, Dragica (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation addresses challenges pertaining to multi-junction (MJ) solar cells from material development to device design and characterization. Firstly, among the various methods to improve the energy conversion efficiency of MJ solar cells using, a novel approach proposed recently is to use II-VI (MgZnCd)(SeTe) and III-V (AlGaIn)(AsSb) semiconductors lattice-matched on GaSb or InAs substrates for current-matched subcells with minimal defect densities. CdSe/CdTe superlattices are proposed as a potential candidate for a subcell in the MJ solar cell designs using this material system, and therefore the material properties of the superlattices are studied. The high structural qualities of the superlattices are obtained from high resolution X-ray diffraction measurements and cross-sectional transmission electron microscopy images. The effective bandgap energies of the superlattices obtained from the photoluminescence (PL) measurements vary with the layer thicknesses, and are smaller than the bandgap energies of either the constituent material. Furthermore, The PL peak position measured at the steady state exhibits a blue shift that increases with the excess carrier concentration. These results confirm a strong type-II band edge alignment between CdSe and CdTe. The valence band offset between unstrained CdSe and CdTe is determined as 0.63 eV±0.06 eV by fitting the measured PL peak positions using the Kronig-Penney model. The blue shift in PL peak position is found to be primarily caused by the band bending effect based on self-consistent solutions of the Schrödinger and Poisson equations. Secondly, the design of the contact grid layout is studied to maximize the power output and energy conversion efficiency for concentrator solar cells. Because the conventional minimum power loss method used for the contact design is not accurate in determining the series resistance loss, a method of using a distributed series resistance model to maximize the power output is proposed for the contact design. It is found that the junction recombination loss in addition to the series resistance loss and shadowing loss can significantly affect the contact layout. The optimal finger spacing and maximum efficiency calculated by the two methods are close, and the differences are dependent on the series resistance and saturation currents of solar cells. Lastly, the accurate measurements of external quantum efficiency (EQE) are important for the design and development of MJ solar cells. However, the electrical and optical couplings between the subcells have caused EQE measurement artifacts. In order to interpret the measurement artifacts, DC and small signal models are built for the bias condition and the scan of chopped monochromatic light in the EQE measurements. Characterization methods are developed for the device parameters used in the models. The EQE measurement artifacts are found to be caused by the shunt and luminescence coupling effects, and can be minimized using proper voltage and light biases. Novel measurement methods using a pulse voltage bias or a pulse light bias are invented to eliminate the EQE measurement artifacts. These measurement methods are nondestructive and easy to implement. The pulse voltage bias or pulse light bias is superimposed on the conventional DC voltage and light biases, in order to control the operating points of the subcells and counterbalance the effects of shunt and luminescence coupling. The methods are demonstrated for the first time to effectively eliminate the measurement artifacts.
ContributorsLi, Jingjing (Author) / Zhang, Yong-Hang (Thesis advisor) / Tao, Meng (Committee member) / Schroder, Dieter (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Lithium metal is a promising anode for the next generation lithium batteries owing to its high capacity (3860 mAh g-1) and the lowest negative reduction potential (-3.04 V). Commercial produced lithium anodes have a native rough surface which deteriorates the cycling performance of the battery. Here, an attempt has been made to deposit lithium on copper from an electrolytic cell consisting of simple electrolyte of pyridine and lithium chloride at room temperature. Water is known to react aggressively with the lithium metal, however in the electrochemical plating process, it has a significant beneficial effect in catalyzing the electrochemical reactions. The effect of trace amounts of water was investigated in air as well as in controlled atmosphere of argon, nitrogen, breathing grade dry air and ultra-zero dry air. The electrochemical products examined by Fourier transform infrared spectroscopy revealed the deposition might require the reduction of pyridine to facilitate the reduction of the lithium salt. Purity of the lithium film was determined by inductively coupled plasma mass spectrometry.
ContributorsPode, Gayatri (Author) / Newman, Nathan (Thesis advisor) / Marshall, Daniel (Committee member) / Tao, Meng (Committee member) / Arizona State University (Publisher)
Created2019
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015
Description
To date, the most popular and dominant material for commercial solar cells is
crystalline silicon (or wafer-Si). It has the highest cell efficiency and cell lifetime out
of all commercial solar cells. Although the potential of crystalline-Si solar cells in
supplying energy demands is enormous, their future growth will likely be constrained
by two major bottlenecks. The first is the high electricity input to produce
crystalline-Si solar cells and modules, and the second is the limited supply of silver
(Ag) reserves. These bottlenecks prevent crystalline-Si solar cells from reaching
terawatt-scale deployment, which means the electricity produced by crystalline-Si
solar cells would never fulfill a noticeable portion of our energy demands in the future.
In order to solve the issue of Ag limitation for the front metal grid, aluminum (Al)
electroplating has been developed as an alternative metallization technique in the
fabrication of crystalline-Si solar cells. The plating is carried out in a
near-room-temperature ionic liquid by means of galvanostatic electrolysis. It has been
found that dense, adherent Al deposits with resistivity in the high 10^–6 ohm-cm range
can be reproducibly obtained directly on Si substrates and nickel seed layers. An
all-Al Si solar cell, with an electroplated Al front electrode and a screen-printed Al
back electrode, has been successfully demonstrated based on commercial p-type
monocrystalline-Si solar cells, and its efficiency is approaching 15%. Further
optimization of the cell fabrication process, in particular a suitable patterning
technique for the front silicon nitride layer, is expected to increase the efficiency of
the cell to ~18%. This shows the potential of Al electroplating in cell metallization is
promising and replacing Ag with Al as the front finger electrode is feasible.
crystalline silicon (or wafer-Si). It has the highest cell efficiency and cell lifetime out
of all commercial solar cells. Although the potential of crystalline-Si solar cells in
supplying energy demands is enormous, their future growth will likely be constrained
by two major bottlenecks. The first is the high electricity input to produce
crystalline-Si solar cells and modules, and the second is the limited supply of silver
(Ag) reserves. These bottlenecks prevent crystalline-Si solar cells from reaching
terawatt-scale deployment, which means the electricity produced by crystalline-Si
solar cells would never fulfill a noticeable portion of our energy demands in the future.
In order to solve the issue of Ag limitation for the front metal grid, aluminum (Al)
electroplating has been developed as an alternative metallization technique in the
fabrication of crystalline-Si solar cells. The plating is carried out in a
near-room-temperature ionic liquid by means of galvanostatic electrolysis. It has been
found that dense, adherent Al deposits with resistivity in the high 10^–6 ohm-cm range
can be reproducibly obtained directly on Si substrates and nickel seed layers. An
all-Al Si solar cell, with an electroplated Al front electrode and a screen-printed Al
back electrode, has been successfully demonstrated based on commercial p-type
monocrystalline-Si solar cells, and its efficiency is approaching 15%. Further
optimization of the cell fabrication process, in particular a suitable patterning
technique for the front silicon nitride layer, is expected to increase the efficiency of
the cell to ~18%. This shows the potential of Al electroplating in cell metallization is
promising and replacing Ag with Al as the front finger electrode is feasible.
ContributorsSun, Wen-Cheng (Author) / Tao, Meng (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
A general review of film growth with various mechanisms is given. Additives and their potential effects on film properties are also discussed. Experimental light-induced aluminum (Al) plating tool design is discussed. Light-induced electroplating of Al as the front electrode on the n-type emitter of silicon (Si) solar cells is proposed as a substitute for screen-printed Silver (Ag). The advantages and disadvantages of Al over copper (Cu) as a suitable Ag replacement are examined. Optimization of the power given to a green laser for silicon nitride (SiNx) anitreflection coating patterning is performed. Laser damage and contamination removal conditions on post-patterned cell surfaces are identified. Plating and post-annealing temperature effects on Al morphology and film resistivity are explored. Morphology and resistivity improvement of the Al film are also investigated through several plating additives. The lowest resistivity of 3.1 µΩ-cm is given by nicotinic acid. Laser induced damage to the cell emitter experimentally limits the contact resistivity between light-induced Al and Si to approximately 69 mΩ-cm2. Phosphorus pentachloride (PCl5) is introduced into the plating bath and improved the the contact resistivity between light induced Al and Si to a range of 0.1-1 mΩ-cm2. Secondary ion mass spectroscopy (SIMS) was performed on a film deposited with PCl5 and showed a phosphorus peak, indicating emitter phosphorus concentration may be the reason for the low contact resistivity between light-induced Al and Si. SEM also shows that PCl5 improves Al film density and plating throwing power. Post plating annealing performed at a temperature of 500°C allows Al to spike through the thin n-type emitter causing cell failure. Atmospheric moisture causes poor process reproducibility.
ContributorsRicci, Lewis (Author) / Tao, Meng (Thesis advisor) / Goryll, Michael (Committee member) / Kozicki, Michael (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cornhole, traditionally seen as tailgate entertainment, has rapidly risen in popularity since the launching of the American Cornhole League in 2016. However, it lacks robust quality control over large tournaments, since many of the matches are scored and refereed by the players themselves. In the past, there have been issues where entire competition brackets have had to be scrapped and replayed because scores were not handled correctly. The sport is in need of a supplementary scoring solution that can provide quality control and accuracy over large matches where there aren’t enough referees present to score games. Drawing from the ACL regulations as well as personal experience and testimony from ACL Pro players, a list of requirements was generated for a potential automatic scoring system. Then, a market analysis of existing scoring solutions was done, and it found that there are no solutions on the market that can automatically score a cornhole game. Using the problem requirements and previous attempts to solve the scoring problem, a list of concepts was generated and evaluated against each other to determine which scoring system design should be developed. After determining that the chosen concept was the best way to approach the problem, the problem requirements and cornhole rules were further refined into a set of physical assumptions and constraints about the game itself. This informed the choice, structure, and implementation of the algorithms that score the bags. The prototype concept was tested on their own, and areas of improvement were found. Lastly, based on the results of the tests and what was learned from the engineering process, a roadmap was set out for the future development of the automatic scoring system into a full, market-ready product.
ContributorsGillespie, Reagan (Author) / Sugar, Thomas (Thesis director) / Li, Baoxin (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2023-05