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- Creators: Papandreou-Suppappola, Antonia
- Creators: Goryll, Michael
- Member of: ASU Electronic Theses and Dissertations
Description
With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is crowding the unlicensed spectrum. Licensed network such as LTE also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work we present a reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE-LAA agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. We turned the reinforcement learning into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.
ContributorsSHIH, PO-KAN (Author) / Moraffah, Bahman (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Dasarathy, Gautam (Committee member) / Shih, YiChang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Unlike conventional solar cells, modern high efficiency passivated contacts solar cells like silicon heterojunction (SHJ) cells have excellent surface passivation and use high bulk lifetime wafers which increase the operating injection level of these devices. These solar cell architectures can benefit from having lower doped substrates, with undoped solar cells becoming an attractive option. There has been very limited literature on high bulk resistivity substrates (>>10 Ωcm). This thesis work provides a comprehensive assessment of the potential of high resistivity/undoped substrates for high performance and more reliable silicon solar cells by demonstrating the results from modeling as well as characterization of SHJ solar cells fabricated with high resistivity/undoped substrates under real-world illumination and temperature conditions that the cells/modules experience in the field. In this work, the results from the analytical model demonstrated the effects of various defects, variation in doping and temperature on the performance of silicon solar cells. Experimentally, SHJ cells with bulk resistivities in the range of 1 Ωcm to >15k Ωcm were fabricated, and cell efficiencies over 20% were measured at standard testing conditions (STC) across the entire range of bulk resistivities. The illumination response (0.1-1 sun) and temperature coefficients (25-90 °C) were shown to be independent of the bulk resistivity. No light induced degradation was observed in the n-type SHJ cells of all resistivity ranges whereas high resistivity p-type SHJ cells showed less degradation compared to that of commercial resistivity range (<10 Ωcm). Very high reverse breakdown voltages (over 1 kV) were demonstrated for SHJ cells fabricated with high resistivity wafers. Using simulation, the importance of having cells in the modules with breakdown voltage higher than the series string voltage for safe and reliable operation of the photovoltaic (PV) system was highlighted.
The ingot yield can be improved by moving towards high resistivity ranges to manufacture high efficiency reliable solar cells by utilizing the entire ingot and eliminating the need to adhere to narrow resistivity range. Thus, the novel findings from this work can have profound impact on ingot and module manufacturing resulting in significant cost savings as well as improvement in the system reliability.
ContributorsSrinivasa, Apoorva (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Committee member) / King, Richard (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2021
Description
The work presented in this manuscript has the overarching theme of radiation. The two forms of radiation of interest are neutrons, i.e. nuclear, and electric fields. The ability to detect such forms of radiation have significant security implications that could also be extended to very practical industrial applications. The goal is therefore to detect, and even image, such radiation sources.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
ContributorsChung, Hugh E (Author) / Allee, David R. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2015
Description
To keep up with the increasing demand for solar energy, higher efficiencies are necessary while keeping cost at a minimum. The easiest theoretical way to achieve that is using silicon-based multi-junction solar cells. However, there are major challenges in effectively implementing such a system. Much work has been done recently to integrate III-V with Si for multi-junction solar cell purposes. The focus of this paper is to explore GaP-based dilute nitrides as a possible top cell candidate for Si-based multi-junctions. The direct growth of dilute nitrides in a lattice-matched configuration epitaxially in literature is reviewed. The problems associated with such growths are outlined and pathways to mitigate these problems are presented. The need for a GaP buffer layer between the dilute nitride film and Si is established. Defects in GaP/Si system are explored in detail and a study on pit formation during such growth is performed. Effective suppression of pits in GaP surface grown on Si is achieved. Issues facing GaP-based dilute nitrides in terms of material properties are outlined. Review of these challenges is done and some possible future areas of interest to improve material quality are established. Finally, the growth process of dilute nitrides using Molecular Beam Epitaxy tool is explained. Results for GaNP grown on Si pre and post growth treatments are detailed.
ContributorsMurali, Srinath (Author) / Honsberg, Christiana (Thesis advisor) / Goodnick, Stephen (Committee member) / King, Richard (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
The continuous time-tagging of photon arrival times for high count rate sources isnecessary for applications such as optical communications, quantum key encryption,
and astronomical measurements. Detection of Hanbury-Brown and Twiss (HBT) single
photon correlations from thermal sources, such as stars, requires a combination of high
dynamic range, long integration times, and low systematics in the photon detection
and time tagging system. The continuous nature of the measurements and the need
for highly accurate timing resolution requires a customized time-to-digital converter
(TDC). A custom built, two-channel, field programmable gate array (FPGA)-based
TDC capable of continuously time tagging single photons with sub clock cycle timing
resolution was characterized. Auto-correlation and cross-correlation measurements
were used to constrain spurious systematic effects in the pulse count data as a function
of system variables. These variables included, but were not limited to, incident
photon count rate, incoming signal attenuation, and measurements of fixed signals.
Additionally, a generalized likelihood ratio test using maximum likelihood estimators
(MLEs) was derived as a means to detect and estimate correlated photon signal
parameters. The derived GLRT was capable of detecting correlated photon signals in
a laboratory setting with a high degree of statistical confidence. A proof is presented
in which the MLE for the amplitude of the correlated photon signal is shown to be the
minimum variance unbiased estimator (MVUE). The fully characterized TDC was used
in preliminary measurements of astronomical sources using ground based telescopes.
Finally, preliminary theoretical groundwork is established for the deep space optical
communications system of the proposed Breakthrough Starshot project, in which
low-mass craft will travel to the Alpha Centauri system to collect scientific data from
Proxima B. This theoretical groundwork utilizes recent and upcoming space based
optical communication systems as starting points for the Starshot communication
system.
ContributorsHodges, Todd Michael William (Author) / Mauskopf, Philip (Thesis advisor) / Trichopoulos, George (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
Description
Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for 30% of all numbers in the dataset and digits that start with the number 9 accounting for 5% of all numbers in the dataset. This phenomenon, known as Benford's Law, is highly repeatable and appears in lists of numbers from electricity bills, stock prices, tax returns, house prices, death rates, lengths of rivers, and naturally occurring images. This paper will demonstrate that human speech spectra also follow Benford's Law. This observation is used to motivate a new set of features that can be efficiently extracted from speech and demonstrate that these features can be used to classify between human speech and synthetic speech.
ContributorsHsu, Leo (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2022
Description
The propagation of waves in solids, especially when characterized by dispersion, remains a topic of profound interest in the field of signal processing. Dispersion represents a phenomenon where wave speed becomes a function of frequency and results in multiple oscillatory modes. Such signals find application in structural healthmonitoring for identifying potential damage sensitive features in complex materials.
Consequently, it becomes important to find matched time-frequency representations
for characterizing the properties of the multiple frequency-dependent modes of propagation in dispersive material. Various time-frequency representations have been
used for dispersive signal analysis. However, some of them suffered from poor timefrequency localization or were designed to match only specific dispersion modes with
known characteristics, or could not reconstruct individual dispersive modes. This
thesis proposes a new time-frequency representation, the nonlinear synchrosqueezing
transform (NSST) that is designed to offer high localization to signals with nonlinear time-frequency group delay signatures. The NSST follows the technique used
by reassignment and synchrosqueezing methods to reassign time-frequency points of
the short-time Fourier transform and wavelet transform to specific localized regions
in the time-frequency plane. As the NSST is designed to match signals with third
order polynomial phase functions in the frequency domain, we derive matched group
delay estimators for the time-frequency point reassignment. This leads to a highly localized representation for nonlinear time-frequency characteristics that also allow for
the reconstruction of individual dispersive modes from multicomponent signals. For
the reconstruction process, we propose a novel unsupervised learning approach that
does not require prior information on the variation or number of modes in the signal.
We also propose a Bayesian group delay mode merging approach for reconstructing
modes that overlap in time and frequency. In addition to using simulated signals, we demonstrate the performance of the new NSST, together with mode extraction, using
real experimental data of ultrasonic guided waves propagating through a composite
plate.
ContributorsIkram, Javaid (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chattopadhyay, Aditi (Thesis advisor) / Bertoni, Mariana (Committee member) / Sinha, Kanu (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation centers on the development of Bayesian methods for learning differ- ent types of variation in switching nonlinear gene regulatory networks (GRNs). A new nonlinear and dynamic multivariate GRN model is introduced to account for different sources of variability in GRNs. The new model is aimed at more precisely capturing the complexity of GRN interactions through the introduction of time-varying kinetic order parameters, while allowing for variability in multiple model parameters. This model is used as the drift function in the development of several stochastic GRN mod- els based on Langevin dynamics. Six models are introduced which capture intrinsic and extrinsic noise in GRNs, thereby providing a full characterization of a stochastic regulatory system. A Bayesian hierarchical approach is developed for learning the Langevin model which best describes the noise dynamics at each time step. The trajectory of the state, which are the gene expression values, as well as the indicator corresponding to the correct noise model are estimated via sequential Monte Carlo (SMC) with a high degree of accuracy. To address the problem of time-varying regulatory interactions, a Bayesian hierarchical model is introduced for learning variation in switching GRN architectures with unknown measurement noise covariance. The trajectory of the state and the indicator corresponding to the network configuration at each time point are estimated using SMC. This work is extended to a fully Bayesian hierarchical model to account for uncertainty in the process noise covariance associated with each network architecture. An SMC algorithm with local Gibbs sampling is developed to estimate the trajectory of the state and the indicator correspond- ing to the network configuration at each time point with a high degree of accuracy. The results demonstrate the efficacy of Bayesian methods for learning information in switching nonlinear GRNs.
ContributorsVélez-Cruz, Nayely (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this dissertation, the nanofabrication process is characterized for fabrication of nanostructure on surface of silicon and gallium phosphide using silica nanosphere lithography (SNL) and metal assisted chemical etching (MACE) process. The SNL process allows fast process time and well defined silica nanosphere monolayer by spin-coating process after mixing N,N-dimethyl-formamide (DMF) solvent. The MACE process achieves the high aspect ratio structure fabrication using the reaction between metal and wet chemical. The nanostructures are fabricated on Si surface for enhanced light management, but, without proper surface passivation those gains hardly impact the performance of the solar cell. The surface passivation of nanostructures is challenging, not only due to larger surface areas and aspect ratios, but also has a direct result of the nanofabrication processes. In this research, the surface passivation of silicon nanostructures is improved by modifying the silica nanosphere lithography (SNL) and the metal assisted chemical etching (MACE) processes, frequently used to fabricate nanostructures. The implementation of a protective silicon oxide layer is proposed prior to the lithography process to mitigate the impact of the plasma etching during the SNL. Additionally, several adhesion layers are studied, chromium (Cr), nickel (Ni) and titanium (Ti) with gold (Au), used in the MACE process. The metal contamination is one of main damage and Ti makes the mitigation of metal contamination. Finally, a new chemical etching step is introduced, using potassium hydroxide at room temperature, to smooth the surface of the nanostructures after the MACE process. This chemical treatment allows to improve passivation by surface area control and removing surface defects. In this research, I demonstrate the Aluminum Oxide (Al2O3) passivation on nanostructure using atomic layer deposition (ALD) process. 10nm of Al2O3 layer makes effective passivation on nanostructure with optimized post annealing in forming gas (N2/H2) environment. However, 10nm thickness is not suitable for hetero structure because of carrier transportation. For carrier transportation, ultrathin Al2O3 (≤ 1nm) layer is used for passivation, but effective passivation is not achieved because of insufficient hydrogen contents. This issue is solved to use additional ultrathin SiO2 (1nm) below Al2O3 layer and hydrogenation from doped a-Si:H.
Moreover, the nanostructure is creased on gallium phosphide (GaP) by SNL and MACE process. The fabrication process is modified by control of metal layer and MACE solution.
ContributorsKim, Sangpyeong (Author) / Honsberg, Christiana (Thesis advisor) / Bowden, Stuart (Committee member) / Goryll, Michael (Committee member) / Augusto, Andre (Committee member) / Arizona State University (Publisher)
Created2021