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Description
The maximum theoretical efficiency of a terrestrial non-concentrated silicon solar cell is 29.4%, as obtained from detailed balance analysis. Over 90% of the current silicon photovoltaics market is based on solar cells with diffused junctions (Al-BSF, PERC, PERL, etc.), which are limited in performance by increased non-radiative recombination in the doped regions. This limitation can be overcome through the use of passivating contacts, which prevent recombination at the absorber interfaces while providing the selectivity to efficiently separate the charge carriers generated in the absorber. This thesis aims at developing an understanding of how the material properties of the contact affect device performance through simulations.The partial specific contact resistance framework developed by Onno et al. aims to link material behavior to device performance specifically at open circuit. In this thesis, the framework is expanded to other operating points of a device, leading to a model for calculating the partial contact resistances at any current flow. The error in calculating these resistances is irrelevant to device performance resulting in an error in calculating fill factor from resistances below 0.1% when the fill factors of the cell are above 70%, i.e., for cells with good passivation and selectivity.
Further, silicon heterojunction (SHJ) and tunnel-oxide based solar cells are simulated in 1D finite-difference modeling package AFORS-HET. The effects of material property changes on device performance are investigated using novel contact materials like Al0.8Ga0.2As (hole contact for SHJ) and ITO (electron contact for tunnel-oxide cells). While changing the bandgap and electron affinity of the contact affect the height of the Schottky barrier and hence contact resistivity, increasing the doping of the contact will increase its selectivity. In the case of ITO, the contact needs to have a work function below 4.2 eV to be electron selective, which suggests that other low work function TCOs (like AZO) will be more applicable as alternative dopant-free electron contacts. The AFORS-HET model also shows that buried doped regions arising from boron diffusion in the absorber can damage passivation and decrease the open circuit voltage of the device.
Further, silicon heterojunction (SHJ) and tunnel-oxide based solar cells are simulated in 1D finite-difference modeling package AFORS-HET. The effects of material property changes on device performance are investigated using novel contact materials like Al0.8Ga0.2As (hole contact for SHJ) and ITO (electron contact for tunnel-oxide cells). While changing the bandgap and electron affinity of the contact affect the height of the Schottky barrier and hence contact resistivity, increasing the doping of the contact will increase its selectivity. In the case of ITO, the contact needs to have a work function below 4.2 eV to be electron selective, which suggests that other low work function TCOs (like AZO) will be more applicable as alternative dopant-free electron contacts. The AFORS-HET model also shows that buried doped regions arising from boron diffusion in the absorber can damage passivation and decrease the open circuit voltage of the device.
ContributorsDasgupta, Sagnik (Author) / Holman, Zachary (Thesis advisor) / Onno, Arthur (Committee member) / Wang, Qing Hua (Committee member) / Arizona State University (Publisher)
Created2020
Description
Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant.The implementation of a non-parametric method to predict the onset of brady- cardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribu- tion of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
ContributorsMitra, Sinjini (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Lateral programmable metallization cells (PMC) utilize the properties of electrodeposits grown over a solid electrolyte channel. Such devices have an active anode and an inert cathode separated by a long electrodeposit channel in a coplanar arrangement. The ability to transport large amount of metallic mass across the channel makes these devices attractive for various More-Than-Moore applications. Existing literature lacks a comprehensive study of electrodeposit growth kinetics in lateral PMCs. Moreover, the morphology of electrodeposit growth in larger, planar devices is also not understood. Despite the variety of applications, lateral PMCs are not embraced by the semiconductor industry due to incompatible materials and high operating voltages needed for such devices. In this work, a numerical model based on the basic processes in PMCs – cation drift and redox reactions – is proposed, and the effect of various materials parameters on the electrodeposit growth kinetics is reported. The morphology of the electrodeposit growth and kinetics of the electrodeposition process are also studied in devices based on Ag-Ge30Se70 materials system. It was observed that the electrodeposition process mainly consists of two regimes of growth – cation drift limited regime and mixed regime. The electrodeposition starts in cation drift limited regime at low electric fields and transitions into mixed regime as the field increases. The onset of mixed regime can be controlled by applied voltage which also affects the morphology of electrodeposit growth. The numerical model was then used to successfully predict the device kinetics and onset of mixed regime. The problem of materials incompatibility with semiconductor manufacturing was solved by proposing a novel device structure. A bilayer structure using semiconductor foundry friendly materials was suggested as a candidate for solid electrolyte. The bilayer structure consists of a low resistivity oxide shunt layer on top of a high resistivity ion carrying oxide layer. Devices using Cu2O as the low resistivity shunt on top of Cu doped WO3 oxide were fabricated. The bilayer devices provided orders of magnitude improvement in device performance in the context of operating voltage and switching time. Electrical and materials characterization revealed the structure of bilayers and the mechanism of electrodeposition in these devices.
ContributorsChamele, Ninad (Author) / Kozicki, Michael (Thesis advisor) / Barnaby, Hugh (Committee member) / Newman, Nathan (Committee member) / Gonzalez-Velo, Yago (Committee member) / Arizona State University (Publisher)
Created2020
Description
Wide Bandgap (WBG) semiconductor materials are shaping day-to-daytechnology by introducing powerful and more energy responsible devices. These
materials have opened the door for building basic semiconductor devices which are
superior in terms of handling high voltages, power and temperature which is not possible
using conventional silicon technology. As the research continues in the field of WBG
based devices, there is a potential chance that the semiconductor industry can save
billions of dollars deploying energy-efficient circuits in high power conversion
electronics. Diamond, silicon carbide and gallium nitride are the top three contenders
among which diamond can significantly outmatch others in a variety of properties.
This thesis describes a methodology to develop the ‘Simulation Program with
Integrated Circuit Emphasis’ (SPICE) model for diamond-based P-I-N diodes. The
developed model can predict the AC and DC response of fabricated P-I-N diodes. P-I-N
diodes are semiconductor devices commonly used to control RF and microwave signals.
It has found a very unique place in the list of available semiconductor devices in modern
electronics which interestingly shows resistance modulation property in high frequency
domain while handling a high-power signal at the same time. The developed SPICE
model for the diamond-based P-I-N diode in this project is then used to evaluate the
performance of a solid-state passive limiter in shunt configuration which protects the
sensitive instruments in ‘Radio Detection and Ranging’ (RADAR) systems
ContributorsJHA, VISHAL (Author) / Trevor, Trevor TT (Thesis advisor) / Barnaby, Hugh HB (Committee member) / Aberle, James JA (Committee member) / Arizona State University (Publisher)
Created2021
Description
A distributed wireless sensor network (WSN) is a network of a large number of lowcost,multi-functional sensors with power, bandwidth, and memory constraints, operating
in remote environments with sensing and communication capabilities. WSNs
are a source for a large amount of data and due to the inherent communication and
resource constraints, developing a distributed algorithms to perform statistical parameter
estimation and data analysis is necessary. In this work, consensus based
distributed algorithms are developed for distributed estimation and processing over
WSNs. Firstly, a distributed spectral clustering algorithm to group the sensors based
on the location attributes is developed. Next, a distributed max consensus algorithm
robust to additive noise in the network is designed. Furthermore, distributed spectral
radius estimation algorithms for analog, as well as, digital communication models
are developed. The proposed algorithms work for any connected graph topologies.
Theoretical bounds are derived and simulation results supporting the theory are also
presented.
ContributorsMuniraju, Gowtham (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Berisha, Visar (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2021
Description
Wide bandgap semiconductors, also known as WBG semiconductors are materials which have larger bandgaps than conventional semiconductors such as Si or GaAs. They permit devices to operate at much higher voltages, frequencies and temperatures. They are the key material used to make LEDs, lasers, radio frequency applications, military applications, and power electronics. Their intrinsic qualities make them promising for next-generation devices for general semiconductor use. Their ability to handle higher power density is particularly attractive for attempts to sustain Moore's law, as conventional technologies appear to be reaching a bottleneck. Apart from WBG materials, ultra-wide bandgap (UWBG) materials, such as Ga2O3, AlN, diamond, or BN, are also attractive since they have even more extreme properties. Although this field is relatively new, which still remains a lot of effort to study and investigate, people can still expect that these materials could be the main characters for more advanced applications in the near future.
In the dissertation, three topics with power devices made by WBG or UWBG semiconductors were introduced. In chapter 1, a generally background knowledge introduction is given. This helps the reader to learn current research focuses. In chapter 2, a comprehensive study of temperature-dependent characteristics of Ga2O3 SBDs with highly-doped substrate is demonstrated. A modified thermionic emission model over an inhomogeneous barrier with a voltage-dependent barrier height is investigated. Besides, the mechanism of surface leakage current is also discussed. These results are beneficial for future developments of low-loss β-Ga2O3 electronics and optoelectronics. In chapter 3, vertical GaN Schottky barrier diodes (SBDs) with floating metal rings (FMRs) as edge termination structures on bulk GaN substrates was introduced. This work represents a useful reference for the FMR termination design for GaN power devices. In chapter 4, AlGaN/GaN metal-insulator-semiconductor high electron mobility transistors (MISHEMTs) fabricated on Si substrates with a 10 nm boron nitride (BN) layer as gate dielectric was demonstrated. The material characterization was investigated by X-ray photoelectric spectroscopy (XPS) and UV photoelectron spectroscopy (UPS). And the gate leakage current mechanisms were also investigated by temperature-dependent current-voltage measurements.
Although still in its infancy, past and projected future progress of electronic designs will ultimately achieve this very goal that WBG and UWBG semiconductors will be indispensable for today and future’s science, technologies and society.
ContributorsYang, Tsung-Han (Author) / Zhao, Yuji (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Nemanich, Robert (Committee member) / Arizona State University (Publisher)
Created2021
Description
Crystalline silicon covers more than 85% of the global photovoltaics industry and has sustained a nearly 30% year-over-year growth rate. Continued cost and capital expenditure (CAPEX) reductions are needed to sustain this growth. Using thin silicon wafers well below the current industry standard of 160 µm can reduce manufacturing cost, CAPEX, and levelized cost of electricity. Additionally, thinner wafers enable more flexible and lighter module designs, making them more compelling in market segments like building-integrated photovoltaics, portable power, aerospace, and automotive industries. Advanced architectures and superior surface passivation schemes are needed to enable the use of very thin silicon wafers. Silicon heterojunction (SHJ) and SHJ with interdigitated back contact solar cells have demonstrated open-circuit voltages surpassing 720 mV and the potential to surpass 25% conversion efficiency. These factors have led to an increasing interest in exploring SHJ solar cells on thin wafers. In this work, the passivation capability of the thin intrinsic hydrogenated amorphous silicon layer is improved by controlling the deposition temperature and the silane-to-hydrogen dilution ratio. An effective way to parametrize surface recombination is by using surface saturation current density and a very low surface saturation density is achieved on textured wafers for wafer thicknesses ranging between 40 and 180 µm which is an order of magnitude lesser compared to the prevalent industry standards. Implied open-circuit voltages over 760 mV were accomplished on SHJ structures deposited on n-type silicon wafers with thicknesses below 50 µm. An analytical model is also described for a better understanding of the variation of the recombination fractions for varying substrate thicknesses. The potential of using very thin wafers is also established by manufacturing SHJ solar cells, using industrially pertinent processing steps, on 40 µm thin standalone wafers while achieving maximum efficiency of 20.7%. It is also demonstrated that 40 µm thin SHJ solar cells can be manufactured using these processes on large areas. An analysis of the percentage contribution of current, voltage, and resistive losses are also characterized for the SHJ devices fabricated in this work for varying substrate thicknesses.
ContributorsBalaji, Pradeep (Author) / Bowden, Stuart (Thesis advisor) / Alford, Terry (Thesis advisor) / Goryll, Michael (Committee member) / Augusto, Andre (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to the large scale of power systems, latency uncertainty in communication can cause severe problems in wide-area measurement systems. To resolve the issue, a significant amount of past work focuses on using emerging technologywhich is machine learning methods such as Q-learning to address latency issues in modern controls. Although such a method can deal with the stochastic characteristics of communication latency in the long run, the Q-learning methods tend to overestimate Q-values, leading to high bias. To solve the overestimation bias issue, the learning structure is redesigned with a twin-delayed deep deterministic policy gradient algorithm to handle the damping control issue under unknown latency in the power network. Meanwhile, a new reward function is proposed, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives including the latency sensitivity analysis under high renewable energy penetration and the comparison with other machine learning algorithms.
For example, if the proposed twin-delayed deep deterministic policy gradient algorithm is applied, the low-frequency oscillation significantly improved compared to existing algorithms.
Furthermore, under the mentorship of Dr. Yang Weng, the development of a big data analysis software project has been collaborating with the Salt River Project (SRP), a major power utility in Arizona. After a thorough examination of data for
the project, it is examined that SRP is suffering from a lot of smart meters data issues. An important goal of the project is to design big data software to monitor SRP smart meter data and to present indicators of abnormalities and special
events. Currently, the big data software interface has been developed for SRP, which has already been successfully adopted by other utilities, research institutes, and laboratories as well.
ContributorsKim, Gyoungjae (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Zhao, Yunpeng (Committee member) / Arizona State University (Publisher)
Created2021
Description
Artificial intelligence is one of the leading technologies that mimics the problem solving and decision making capabilities of the human brain. Machine learning algorithms, especially deep learning algorithms, are leading the way in terms of performance and robustness. They are used for various purposes, mainly for computer vision, speech recognition, and object detection. The algorithms are usually tested inaccuracy, and they utilize full floating-point precision (32 bits). The hardware would require a high amount of power and area to accommodate many parameters with full precision. In this exploratory work, the convolution autoencoder is quantized for the working of an event base camera. The model is designed so that the autoencoder can work on-chip, which would sufficiently decrease the latency in processing. Different quantization methods are used to quantize and binarize the weights and activations of this neural network model to be portable and power efficient. The sparsity term is added to make the model as robust and energy-efficient as possible. The network model was able to recoup the lost accuracy due to binarizing the weights and activation's to quantize the layers of the encoder selectively. This method of recouping the accuracy gives enough flexibility to introduce the network on the chip to get real-time processing from systems like event-based cameras. Lately, computer vision, especially object detection have made strides in their object detection accuracy. The algorithms can sufficiently detect and predict the objects in real-time. However, end-to-end detection of the algorithm is challenging due to the large parameter need and processing requirements. A change in the Non Maximum Suppression algorithm in SSD(Single Shot Detector)-Mobilenet-V1 resulted in less computational complexity without change in the quality of output metric. The Mean Average Precision(mAP) calculated suggests that this method can be implemented in the post-processing of other networks.
ContributorsKuzhively, Ajay Balu (Author) / Cao, Yu (Thesis advisor) / Seo, Jae-Sun (Committee member) / Fan, Delian (Committee member) / Arizona State University (Publisher)
Created2021
Description
Wide bandgap semiconductors are of much current interest due to their superior electrical properties. This dissertation describes electron microscopy characterization of GaN-on-GaN structures for high-power vertical device applications. Unintentionally-doped (UID) GaN layers grown homoepitaxially via metal-organic chemical vapor deposition on freestanding GaN substrates, were subjected to dry etching, and layers of UID-GaN/p-GaN were over-grown. The as-grown and regrown heterostructures were examined in cross-section using transmission electron microscopy (TEM). Two different etching treatments, fast-etch-only and multiple etches with decreasing power, were employed. The fast-etch-only devices showed GaN-on-GaN interface at etched location, and low device breakdown voltages were measured (~ 45-95V). In comparison, no interfaces were visible after multiple etching steps, and the corresponding breakdown voltages were much higher (~1200-1270V). These results emphasized importance of optimizing surface etching techniques for avoiding degraded device performance.
The morphology of GaN-on-GaN devices after reverse-bias electrical stressing to breakdown was investigated. All failed devices had irreversible structural damage, showing large surface craters (~15-35 microns deep) with lengthy surface cracks. Cross-sectional TEM of failed devices showed high densities of threading dislocations (TDs) around the cracks and near crater surfaces. Progressive ion-milling across damaged devices revealed high densities of TDs and the presence of voids beneath cracks: these features were not observed in unstressed devices. The morphology of GaN substrates grown by hydride vapor-phase epitaxy (HVPE) and by ammonothermal methods were correlated with reverse-bias results. HVPE substrates showed arrays of surface features when observed by X-ray topography (XRT). All fabricated devices that overlapped with these features had typical reverse-bias voltages less than 100V at a leakage current limit of 10-6 A. In contrast, devices not overlapping with such features reached voltages greater than 300V. After etching, HVPE substrate surfaces showed defect clusters and macro-pits, whereas XRT images of ammonothermal substrate revealed no visible features. However, some devices fabricated on ammonothermal substrate failed at low voltages. Devices on HVPE and ammonothermal substrates with low breakdown voltages showed crater-like surface damage and revealed TDs (~25µm deep) and voids; such features were not observed in devices reaching higher voltages. These results should assist in developing protocols to fabricate reliable high-voltage devices.
ContributorsPeri, Prudhvi Ram (Author) / Smith, David J. (Thesis advisor) / Alford, Terry (Committee member) / Mccartney, Martha R (Committee member) / Nemanich, Robert (Committee member) / Zhao, Yuji (Committee member) / Arizona State University (Publisher)
Created2021