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Description
Point-of-Care diagnostics is one of the most popular fields of research in bio-medicine today because of its portability, speed of response, convenience and quality assurance. One of the most important steps in such a device is to prepare and purify the sample by extracting the nucleic acids, for which small spherical magnetic particles called magnetic beads are often used in laboratories. Even though magnetic beads have the ability to isolate DNA or RNA from bio-samples in their purified form, integrating these into a microfluidic point-of-need testing kit is still a bit of a challenge. In this thesis, the possibility of integrating paramagnetic beads instead of silica-coated dynabeads, has been evaluated with respect to a point-of-need SARS-CoV-2 virus testing kit. This project is a comparative study between five different sizes of carboxyl-coated paramagnetic beads with reference to silica-coated dynabeads, and how each of them behave in a microcapillary chip in presence of magnetic fields of different strengths. The diameters and velocities of the beads have been calculated using different types of microscopic imaging techniques. The washing and elution steps of an extraction process have been recreated using syringe pump, microcapillary channels and permanent magnets, based on which those parameters of the beads have been studied which are essential for extraction behaviour. The yield efficiency of the beads have also been analysed by using these to extract Salmon DNA. Overall, furthering this research will improve the sensitivity and specificity for any low-cost nucleic-acid based point-of-care testing device.
ContributorsBiswas, Shilpita (Author) / Christen, Jennifer B (Thesis advisor) / Ozev, Sule (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to high DRAM access latency and energy, several convolutional neural network(CNN) accelerators face performance and energy efficiency challenges, which are critical for embedded implementations. As these applications exploit larger datasets, memory accesses of these emerging applications are increasing. As a result, it is difficult to predict the combined dynamic random access memory (DRAM) workload behavior, which can sabotage memory optimizations in software. To understand the impact of external memory access on CNN accelerators which reduces the high DRAMaccess latency and energy, simulators such as RAMULATOR and VAMPIRE have been proposed in prior work. In this work, we utilize these simulators to benchmark external memory access in CNN accelerators. Experiments are performed generating trace files based on the number of parameters and data precision and also using trace file generated for CNN Accelerator Altera Arria 10 GX 1150 FPGA data to complete the end to end workflow using the mentioned simulators. Besides that, certain modifications were made in the default VAMPIRE code to implement certain functionalities such as PREA(Precharge All) and REF(Refresh). Then, precalculated energies were computed for DDR3, DDR4, and HBM based on the micron model to mention it in the dram specification file inputted to the VAMPIRE tool. An experimental study was performed and a comparison is made between DDR3, DDR4, and HBM, it was proved that DDR4 is nearly 31% more energy-efficient than DDR3 and HBMis 54% energy-efficient than DDR3. Performed modeling and experimental analysis on a large set of data and then split it into a set of data and compared the results of the small sets multiplied with the number of sets and the large data set and concluded that the results were nearly the same. Finally, a GUI is developed by wrapping both the simulators. GUI provides user-friendly access and can analyze the parameters without much prior knowledge and understanding of the working.
ContributorsPannala, Manvitha (Author) / Cao, Yu (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2021
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
This dissertation studies the methods to enhance the performance of foldable robots manufactured by laminated techniques. This class of robots are unique in their manufacturing process, which involves cutting and staking up thin layers of different materials with various stiffness. While inheriting the advantages of soft robots -- low weight, affordable manufacturing cost and a fast prototyping process -- a wider range of actuators is available to these mechanisms, while modeling their behavior requires less computational cost.The fundamental question this dissertation strives to answer is how to decode and leverage the effect of material stiffness in these robots. These robots' stiffness is relatively limited due to their slender design, specifically at larger scales. While compliant robots may have inherent advantages such as being safer to work around, this low rigidity makes modeling more complex. This complexity is mostly contained in material deformation since the conventional actuators such as servo motors can be easily leveraged in these robots. As a result, when introduced to real-world environments, efficient modeling and control of these robots are more achievable than conventional soft robots.
Various approaches have been taken to design, model, and control a variety of laminate robot platforms by investigating the effect of material deformation in prototypes while they interact with their working environments. The results obtained show that data-driven approaches such as experimental identification and machine learning techniques are more reliable in modeling and control of these mechanisms. Also, machine learning techniques for training robots in non-ideal experimental setups that encounter the uncertainties of real-world environments can be leveraged to find effective gaits with high performance. Our studies on the effect of stiffness of thin, curved sheets of materials has evolved into introducing a new class of soft elements which we call Soft, Curved, Reconfigurable, Anisotropic Mechanisms (SCRAMs). Like bio-mechanical systems, SCRAMs are capable of re-configuring the stiffness of curved surfaces to enhance their performance and adaptability. Finally, the findings of this thesis show promising opportunities for foldable robots to become an alternative for conventional soft robots since they still offer similar advantages in a fraction of computational expense.
ContributorsSharifzadeh, Mohammad (Author) / Aukes, Daniel (Thesis advisor) / Sugar, Thomas (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Most hardware today is based on von Neumann architecture separating memory from logic. Valuable processing time is lost in shuttling information back and forth between the two units, a problem called von Neumann bottleneck. As transistors are scaled further down, this bottleneck will make it harder to deliver performance in computing power. Adding to this is the increasing complexity of artificial intelligence logic. Thus, there is a need for a faster and more efficient method of computing. Neuromorphic systems deliver this by emulating the massively parallel and fault-tolerant computing capabilities of the human brain where the action potential is triggered by multiple inputs at once (spatial) or an input that builds up over time (temporal). Highly scalable memristors are key in these systems- they can maintain their internal resistive state based on previous current/voltage values thus mimicking the way the strength of two synapses in the brain can vary. The brain-inspired algorithms are implemented by vector matrix multiplications (VMMs) to provide neuronal outputs. High-density conductive bridging random access memory (CBRAM) crossbar arrays (CBAs) can perform VMMs parallelly with ultra-low energy.This research explores a simple planarization technique that could be potentially extended to integrate front-end-of-line (FEOL) processing of complementary metal oxide semiconductor (CMOS) circuitry with back-end-of-line (BEOL) processing of CBRAM CBAs for one-transistor one-resistor (1T1R) Neuromorphic CMOS chips where the transistor is part of the CMOS circuitry and the CBRAM forms the resistor. It is a photoresist (PR) and spin-on glass (SOG) based planarization recipe to planarize CBRAM electrode patterns on a silicon substrate. In this research, however, the planarization is only applied to mechanical grade (MG) silicon wafers without any CMOS layers on them. The planarization achieved was of a very high order (few tens of nanometers). Additionally, the recipe is cost-effective, provides good quality films and simple as only two types of process technologies are involved- lithography and dry etching.
Subsequent processing would involve depositing the CBRAM layers onto the planarized electrodes to form the resistor. Finally, the entire process flow is to be replicated onto wafers with CMOS layers to form the 1T1R circuit.
ContributorsBiswas, Prabaha (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Velo, Yago Gonzalez (Committee member) / Arizona State University (Publisher)
Created2021
Description
Realization of efficient, high-bandgap photovoltaic cells produced using economically viable methods is a technological advance that could change the way we generate and use energy, and thereby accelerate the development of human civilization. There is a need to engineer a semiconductor material for solar cells, particularly multijunction cells, that has high (1.6-2.0 eV) bandgap, has relatively inactive defects, is thermodynamically stable under normal operating conditions with the potential for cost-effective thin-film growth in mass production.This work focuses on a material system made of gallium, indium, and phosphorus – the ternary semiconductor GaInP. GaInP based photovoltaic cells in single-crystal form have demonstrated excellent power conversion efficiency, however, growth of single-crystal GaInP is prohibitively expensive. While growth of polycrystalline GaInP is expected to lower production costs, polycrystalline GaInP is also expected to have a high density of electronically active defects, about which little is reported in scientific literature. This work presents the first study of synthesis, and structural and optoelectronic characterization of polycrystalline GaInP thin films.
In addition, this work models the best performance of polycrystalline solar cells achievable with a given grain size with grain-boundary/surface recombination velocity as a variable parameter. The effects of defect characteristics at the surface and layer properties such as doping and thickness on interface recombination velocity are also modeled.
Recombination velocities at the free surface of single-crystal GaInP and after deposition of various dielectric layers on GaInP are determined experimentally using time-resolved photoluminescence decay measurements. In addition, experimental values of bulk lifetime and surface recombination velocity in well-passivated single crystal AlInP-GaInP based double heterostructures are also measured for comparison to polycrystalline material systems.
A novel passivation method – aluminum-assisted post-deposition treatment or Al-PDT – was developed which shows promise as a general passivation and material improvement technique for polycrystalline thin films. In the GaInP system, this aluminum post-deposition treatment has demonstrated improvement in the minority carrier lifetime to 44 ns at 80 K. During development of the passivation process, aluminum diffusivity in GaInP was measured using TEM-EDS line scans. Introduction, development, and refinement of this novel passivation mechanism in polycrystalline GaInP could initiate the development of a new family of passivation treatments, potentially improving the optoelectronic response of other polycrystalline compound semiconductors as well.
ContributorsChikhalkar, Abhinav (Author) / King, Richard R (Thesis advisor) / Honsberg, Christiana (Committee member) / Newman, Nathan (Committee member) / Tongay, Sefaattin (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
Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.
ContributorsDabir, Anirudh (Author) / Zhuang, Houlong (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Additively Manufactured Thin-wall Inconel 718 specimens commonly find application in heat exchangers and Thermal Protection Systems (TPS) for space vehicles. The wall thicknesses in applications for these components typically range between 0.03-2.5mm. Laser Powder Bed Fusion (PBF) Fatigue standards assume thickness over 5mm and consider Hot Isostatic Pressing (HIP) as conventional heat treatment. This study aims at investigating the dependence of High Cycle Fatigue (HCF) behavior on wall thickness and Hot Isostatic Pressing (HIP) for as-built Additively Manufactured Thin Wall Inconel 718 alloys. To address this aim, high cycle fatigue tests were performed on specimens of seven different thicknesses (0.3mm,0.35mm, 0.5mm, 0.75mm, 1mm, 1.5mm, and 2mm) using a Servohydraulic FatigueTesting Machine. Only half of the specimen underwent HIP, creating data for bothHIP and No-HIP specimens. Upon analyzing the collected data, it was noticed that the specimens that underwent HIP had similar fatigue behavior to that of sheet metal specimens. In addition, it was also noticed that the presence of Porosity in No-HIP specimens makes them more sensitive to changes in stress. A clear decrease in fatigue strength with the decrease in thickness was observed for all specimens.
ContributorsSaxena, Anushree (Author) / Bhate, Dhruv (Thesis advisor) / Liu, Yongming (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the development of autonomous ground vehicles (AGVs), how to guarantee vehicle lateral stability is one of the most critical aspects. Based on nonlinear vehicle lateral and tire dynamics, new driving requirements of AGVs demand further studies and analyses of vehicle lateral stability control strategies. To achieve comprehensive analyses and stability-guaranteed vehicle lateral driving control, this dissertation presents three main contributions.First, a new method is proposed to estimate and analyze vehicle lateral driving stability regions, which provide a direct and intuitive demonstration for stability control of AGVs. Based on a four-wheel vehicle model and a nonlinear 2D analytical LuGre tire model, a local linearization method is applied to estimate vehicle lateral driving stability regions by analyzing vehicle local stability at each operation point on a phase plane. The obtained stability regions are conservative because both vehicle and tire stability are simultaneously considered. Such a conservative feature is specifically important for characterizing the stability properties of AGVs.
Second, to analyze vehicle stability, two novel features of the estimated vehicle lateral driving stability regions are studied. First, a shifting vector is formulated to explicitly describe the shifting feature of the lateral stability regions with respect to the vehicle steering angles. Second, dynamic margins of the stability regions are formulated and applied to avoid the penetration of vehicle state trajectory with respect to the region boundaries. With these two features, the shiftable stability regions are feasible for real-time stability analysis.
Third, to keep the vehicle states (lateral velocity and yaw rate) always stay in the shiftable stability regions, different control methods are developed and evaluated. Based on different vehicle control configurations, two dynamic sliding mode controllers (SMC) are designed. To better control vehicle stability without suffering chattering issues in SMC, a non-overshooting model predictive control is proposed and applied. To further save computational burden for real-time implementation, time-varying control-dependent invariant sets and time-varying control-dependent barrier functions are proposed and adopted in a stability-guaranteed vehicle control problem.
Finally, to validate the correctness and effectiveness of the proposed theories, definitions, and control methods, illustrative simulations and experimental results are presented and discussed.
ContributorsHuang, Yiwen (Author) / Chen, Yan (Thesis advisor) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yong, Sze Zheng (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021