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- Genre: Academic theses
- Creators: Papandreou-Suppappola, Antonia
- Member of: Theses and Dissertations
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
The high cut-off frequency of deep sub-micron CMOS technologies has enabled the integration of radio frequency (RF) transceivers with digital circuits. However, the challenging point is the integration of RF power amplifiers, mainly due to the low breakdown voltage of CMOS transistors. Silicon-on-insulator (SOI) metal semiconductor field effect transistors (MESFETs) have been introduced to remedy the limited headroom concern in CMOS technologies. The MESFETs presented in this thesis have been fabricated on different SOI-CMOS processes without making any change to the standard fabrication steps and offer 2-30 times higher breakdown voltage than the MOSFETs on the same process. This thesis explains the design steps of high efficiency and wideband RF transmitters using the proposed SOI-CMOS compatible MESFETs. This task involves DC and RF characterization of MESFET devices, along with providing a compact Spice model for simulation purposes. This thesis presents the design of several SOI-MESFET RF power amplifiers operating at 433, 900 and 1800 MHz with ~40% bandwidth. Measurement results show a peak power added efficiency (PAE) of 55% and a peak output power of 22.5 dBm. The RF-PAs were designed to operate in Class-AB mode to minimize the linearity degradation. Class-AB power amplifiers lead to poor power added efficiency, especially when fed with signals with high peak to average power ratio (PAPR) such as wideband code division multiple access (W-CDMA). Polar transmitters have been introduced to improve the efficiency of RF-PAs at backed-off powers. A MESFET based envelope tracking (ET) polar transmitter was designed and measured. A low drop-out voltage regulator (LDO) was used as the supply modulator of this polar transmitter. MESFETs are depletion mode devices; therefore, they can be configured in a source follower configuration to have better stability and higher bandwidth that MOSFET based LDOs. Measurement results show 350 MHz bandwidth while driving a 10 pF capacitive load. A novel polar transmitter is introduced in this thesis to alleviate some of the limitations associated with polar transmitters. The proposed architecture uses the backgate terminal of a partially depleted transistor on SOI process, which relaxes the bandwidth and efficiency requirements of the envelope amplifier in a polar transmitter. The measurement results of the proposed transmitter demonstrate more than three times PAE improvement at 6-dB backed-off output power, compared to the traditional RF transmitters.
ContributorsGhajar, Mohammad Reza (Author) / Thornton, Trevor (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Bakkaloglu, Bertan (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.
ContributorsSantucci, Robert (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioðlu, Cihan (Committee member) / Bakkaloglu, Bertan (Committee member) / Tsakalis, Kostas (Committee member) / Arizona State University (Publisher)
Created2013
Description
This work presents two balanced power amplifier (PA) architectures, one at X-band and the other at K-band. The presented balanced PAs are designed for use in small satellite and cube satellite applications.The presented X-band PA employs wideband hybrid couplers to split input power to two commercial off-the-shelf (COTS) Gallium Nitride (GaN) monolithic microwave integrated circuit (MMIC) PAs and combine their output powers. The presented X-band balanced PA manufactured on a Rogers 4003C substrate yields increased small signal gain and saturated output power under continuous wave (CW) operation compared to the single MMIC PA used in the design under pulsed operation. The presented PA operates from 7.5 GHz to 11.5 GHz, has a maximum small signal gain of 36.3 dB, a maximum saturated power out of 40.0 dBm, and a maximum power added efficiency (PAE) of 38%.
Both a Wilkinson and a Gysel splitter and combiner are designed for use at K-band and their performance is compared. The presented K-band balanced PA uses Gysel power dividers and combiners with a GaN MMIC PA that is soon to be released in production.
ContributorsPearson, Katherine Elizabeth (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2023
Description
The advancement and marked increase in the use of computing devices in health care for large scale and personal medical use has transformed the field of medicine and health care into a data rich domain. This surge in the availability of data has allowed domain experts to investigate, study and discover inherent patterns in diseases from new perspectives and in turn, further the field of medicine. Storage and analysis of this data in real time aids in enhancing the response time and efficiency of doctors and health care specialists. However, due to the time critical nature of most life- threatening diseases, there is a growing need to make informed decisions prior to the occurrence of any fatal outcome. Alongside time sensitivity, analyzing data specific to diseases and their effects on an individual basis leads to more efficient prognosis and rapid deployment of cures. The primary challenge in addressing both of these issues arises from the time varying and time sensitive nature of the data being studied and in the ability to successfully predict anomalous events using only observed data.This dissertation introduces adaptive machine learning algorithms that aid in the prediction of anomalous situations arising due to abnormalities present in patients diagnosed with certain types of diseases. Emphasis is given to the adaptation and development of algorithms based on an individual basis to further the accuracy of all predictions made. The main objectives are to learn the underlying representation of the data using empirical methods and enhance it using domain knowledge. The learned model is then utilized as a guide for statistical machine learning methods to predict the occurrence of anomalous events in the near future. Further enhancement of the learned model is achieved by means of tuning the objective function of the algorithm to incorporate domain knowledge. Along with anomaly forecasting using multi-modal data, this dissertation also investigates the use of univariate time series data towards the prediction of onset of diseases using Bayesian nonparametrics.
ContributorsDas, Subhasish (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Indic, Premananda (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Multi-segment manipulators and mobile robot collectives are examples of multi-agent robotic systems, in which each segment or robot can be considered an agent. Fundamental motion control problems for such systems include the stabilization of one or more agents to target configurations or trajectories while preventing inter-agent collisions, agent collisions with obstacles, and deadlocks. Despite extensive research on these control problems, there are still challenges in designing controllers that (1) are scalable with the number of agents; (2) have theoretical guarantees on collision-free agent navigation; and (3) can be used when the states of the agents and the environment are only partially observable. Existing centralized and distributed control architectures have limited scalability due to their computational complexity and communication requirements, while decentralized control architectures are often effective only under impractical assumptions that do not hold in real-world implementations. The main objective of this dissertation is to develop and evaluate decentralized approaches for multi-agent motion control that enable agents to use their onboard sensors and computational resources to decide how to move through their environment, with limited or absent inter-agent communication and external supervision. Specifically, control approaches are designed for multi-segment manipulators and mobile robot collectives to achieve position and pose (position and orientation) stabilization, trajectory tracking, and collision and deadlock avoidance. These control approaches are validated in both simulations and physical experiments to show that they can be implemented in real-time while remaining computationally tractable. First, kinematic controllers are proposed for position stabilization and trajectory tracking control of two- or three-dimensional hyper-redundant multi-segment manipulators. Next, robust and gradient-based feedback controllers are presented for individual holonomic and nonholonomic mobile robots that achieve position stabilization, trajectory tracking control, and obstacle avoidance. Then, nonlinear Model Predictive Control methods are developed for collision-free, deadlock-free pose stabilization and trajectory tracking control of multiple nonholonomic mobile robots in known and unknown environments with obstacles, both static and dynamic. Finally, a feedforward proportional-derivative controller is defined for collision-free velocity tracking of a moving ground target by multiple unmanned aerial vehicles.
ContributorsSalimi Lafmejani, Amir (Author) / Berman, Spring (Thesis advisor) / Tsakalis, Konstantinos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2022
Description
Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical transitions and transient states in nonlinear dynamics is a complex problem. I developed a solution called parameter-aware reservoir computing, which uses machine learning to track how system dynamics change with a driving parameter. I show that the transition point can be accurately predicted while trained in a sustained functioning regime before the transition. Notably, it can also predict if the system will enter a transient state, the distribution of transient lifetimes, and their average before a final collapse, which are crucial for management. I introduce a machine-learning-based digital twin for monitoring and predicting the evolution of externally driven nonlinear dynamical systems, where reservoir computing is exploited. Extensive tests on various models, encompassing optics, ecology, and climate, verify the approach’s effectiveness. The digital twins can extrapolate unknown system dynamics, continually forecast and monitor under non-stationary external driving, infer hidden variables, adapt to different driving waveforms, and extrapolate bifurcation behaviors across varying system sizes. Integrating engineered gene circuits into host cells poses a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback. I conducted systematic studies on hundreds of circuit structures exhibiting various functionalities, and identified a comprehensive categorization of growth-induced failures. I discerned three dynamical mechanisms behind these circuit failures. Moreover, my comprehensive computations reveal a scaling law between the circuit robustness and the intensity of growth feedback. A class of circuits with optimal robustness is also identified. Chimera states, a phenomenon of symmetry-breaking in oscillator networks, traditionally have transient lifetimes that grow exponentially with system size. However, my research on high-dimensional oscillators leads to the discovery of ’short-lived’ chimera states. Their lifetime increases logarithmically with system size and decreases logarithmically with random perturbations, indicating a unique fragility. To understand these states, I use a transverse stability analysis supported by simulations.
ContributorsKong, Lingwei (Author) / Lai, Ying-Cheng (Thesis advisor) / Tian, Xiaojun (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
The continuing advancement of modulation standards with newer generations of cellular technology, promises ever increasing data rate and bandwidth efficiency. However, these modulation schemes present high peak to average power ratio (PAPR) even after applying crest factor reduction. Being the most power-hungry component in the radio frequency (RF) transmitter, power amplifiers (PA) for infrastructure applications, need to operate efficiently at the presence of these high PAPR signals while maintaining reasonable linearity performance which could be improved by moderate digital pre-distortion (DPD) techniques. This strict requirement of operating efficiently at average power level while being capable of delivering the peak power, made the load modulated PAs such as Doherty PA, Outphasing PA, various Envelope Tracking PAs, Polar transmitters and most recently the load modulated balanced PA, the prime candidates for such application. However, due to its simpler architecture and ability to deliver RF power efficiently with good linearity performance has made Doherty PA (DPA) the most popular solution and has been deployed almost exclusively for wireless infrastructure application all over the world.
Although DPAs has been very successful at amplifying the high PAPR signals, most recent advancements in cellular technology has opted for higher PAPR based signals at wider bandwidth. This lead to increased research and development work to innovate advanced Doherty architectures which are more efficient at back-off (BO) power levels compared to traditional DPAs. In this dissertation, three such advanced Doherty architectures and/or techniques are proposed to achieve high efficiency at further BO power level compared to traditional architecture using symmetrical devices for carrier and peaking PAs. Gallium Nitride (GaN) based high-electron-mobility (HEMT) technology has been used to design and fabricate the DPAs to validate the proposed advanced techniques for higher efficiency with good linearity performance at BO power levels.
Although DPAs has been very successful at amplifying the high PAPR signals, most recent advancements in cellular technology has opted for higher PAPR based signals at wider bandwidth. This lead to increased research and development work to innovate advanced Doherty architectures which are more efficient at back-off (BO) power levels compared to traditional DPAs. In this dissertation, three such advanced Doherty architectures and/or techniques are proposed to achieve high efficiency at further BO power level compared to traditional architecture using symmetrical devices for carrier and peaking PAs. Gallium Nitride (GaN) based high-electron-mobility (HEMT) technology has been used to design and fabricate the DPAs to validate the proposed advanced techniques for higher efficiency with good linearity performance at BO power levels.
ContributorsRuhul Hasin, Muhammad (Author) / Kitchen, Jennifer (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Bakkaloglu, Bertan (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2018
Description
There is an ever-increasing demand for higher bandwidth and data rate ensuing from exploding number of radio frequency integrated systems and devices. As stated in the Shannon-Hartley theorem, the maximum achievable data rate of a communication channel is linearly proportional to the system bandwidth. This is the main driving force behind pushing wireless systems towards millimeter-wave frequency range, where larger bandwidth is available at a higher carrier frequency. Observing the Moor’s law, highly scaled complementary metal–oxide–semiconductor (CMOS) technologies provide fast transistors with a high unity power gain frequency which enables operating at millimeter-wave frequency range. CMOS is the compelling choice for digital and signal processing modules which concurrently offers high computation speed, low power consumption, and mass integration at a high manufacturing yield. One of the main shortcomings of the sub-micron CMOS technologies is the low breakdown voltage of the transistors that limits the dynamic range of the radio frequency (RF) power blocks, especially with the power amplifiers. Low voltage swing restricts the achievable output power which translates into low signal to noise ratio and degraded linearity. Extensive research has been done on proposing new design and IC fabrication techniques with the goal of generating higher output power in CMOS technology. The prominent drawbacks of these solutions are an increased die area, higher cost per design, and lower overall efficiency due to lossy passive components. In this dissertation, CMOS compatible metal–semiconductor field-effect transistor (MESFETs) are utilized to put forward a new solution to enhance the power amplifier’s breakdown voltage, gain and maximum output power. Requiring no change to the conventional CMOS process flow, this low cost approach allows direct incorporation of high voltage power MESFETs into silicon. High voltage MESFETs were employed in a cascode structure to push the amplifier’s cutoff frequency and unity power gain frequency to the 5G and K-band frequency range. This dissertation begins with CMOS compatible MESFET modeling and fabrication steps, and culminates in the discussion of amplifier design and optimization methodology, parasitic de-embedding steps, simulation and measurement results, and high resistivity RF substrate characterization.
ContributorsHabibiMehr, Payam (Author) / Thornton, Trevor John (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Formicone, Gabriele (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2019
Description
Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
ContributorsKoneripalli Seetharam, Kaushik (Author) / Turaga, Pavan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019