Matching Items (35)

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Building Invariant, Robust And Stable Machine Learning Systems Using Geometry and Topology

Description

Over the past decade, machine learning research has made great strides and significant impact in several fields. Its success is greatly attributed to the development of effective machine learning algorithms

Over the past decade, machine learning research has made great strides and significant impact in several fields. Its success is greatly attributed to the development of effective machine learning algorithms like deep neural networks (a.k.a. deep learning), availability of large-scale databases and access to specialized hardware like Graphic Processing Units. When designing and training machine learning systems, researchers often assume access to large quantities of data that capture different possible variations. Variations in the data is needed to incorporate desired invariance and robustness properties in the machine learning system, especially in the case of deep learning algorithms. However, it is very difficult to gather such data in a real-world setting. For example, in certain medical/healthcare applications, it is very challenging to have access to data from all possible scenarios or with the necessary amount of variations as required to train the system. Additionally, the over-parameterized and unconstrained nature of deep neural networks can cause them to be poorly trained and in many cases over-confident which, in turn, can hamper their reliability and generalizability. This dissertation is a compendium of my research efforts to address the above challenges. I propose building invariant feature representations by wedding concepts from topological data analysis and Riemannian geometry, that automatically incorporate the desired invariance properties for different computer vision applications. I discuss how deep learning can be used to address some of the common challenges faced when working with topological data analysis methods. I describe alternative learning strategies based on unsupervised learning and transfer learning to address issues like dataset shifts and limited training data. Finally, I discuss my preliminary work on applying simple orthogonal constraints on deep learning feature representations to help develop more reliable and better calibrated models.

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Date Created
  • 2020

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Reliability of Photovoltaic Cells with Plated Copper Electrodes

Description

An ongoing effort in the photovoltaic (PV) industry is to reduce the major manufacturing cost components of solar cells, the great majority of which are based on crystalline silicon (c-Si).

An ongoing effort in the photovoltaic (PV) industry is to reduce the major manufacturing cost components of solar cells, the great majority of which are based on crystalline silicon (c-Si). This includes the substitution of screenprinted silver (Ag) cell contacts with alternative copper (Cu)-based contacts, usually applied with plating. Plated Cu contact schemes have been under study for many years with only minor traction in industrial production. One of the more commonly-cited barriers to the adoption of Cu-based contacts for photovoltaics is long-term reliability, as Cu is a significant contaminant in c-Si, forming precipitates that degrade performance via degradation of diode character and reduction of minority carrier lifetime. Cu contamination from contacts might cause degradation during field deployment if Cu is able to ingress into c-Si. Furthermore, Cu contamination is also known to cause a form of light-induced degradation (LID) which further degrades carrier lifetime when cells are exposed to light.

Prior literature on Cu-contact reliability tended to focus on accelerated testing at the cell and wafer level that may not be entirely replicative of real-world environmental stresses in PV modules. This thesis is aimed at advancing the understanding of Cu-contact reliability from the perspective of quasi-commercial modules under more realistic stresses. In this thesis, c-Si solar cells with Cu-plated contacts are fabricated, made into PV modules, and subjected to environmental stress in an attempt to induce hypothesized failure modes and understand any new vulnerabilities that Cu contacts might introduce. In particular, damp heat stress is applied to conventional, p-type c-Si modules and high efficiency, n-type c-Si heterojunction modules. I present evidence of Cu-induced diode degradation that also depends on PV module materials, as well as degradation unrelated to Cu, and in either case suggest engineering solutions to the observed degradation. In a forensic search for degradation mechanisms, I present novel evidence of Cu outdiffusion from contact layers and encapsulant-driven contact corrosion as potential key factors. Finally, outdoor exposures to light uncover peculiarities in Cu-plated samples, but do not point to especially serious vulnerabilities.

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Created

Date Created
  • 2020

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Towards adaptive micro-robotic neural interfaces: autonomous navigation of microelectrodes in the brain for optimal neural recording

Description

Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting

Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.

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Created

Date Created
  • 2013

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Accelerated Aging in Devices and Circuits

Description

The aging mechanism in devices is prone to uncertainties due to dynamic stress conditions. In AMS circuits these can lead to momentary fluctuations in circuit voltage that may be missed

The aging mechanism in devices is prone to uncertainties due to dynamic stress conditions. In AMS circuits these can lead to momentary fluctuations in circuit voltage that may be missed by a compact model and hence cause unpredictable failure. Firstly, multiple aging effects in the devices may have underlying correlations. The generation of new traps during TDDB may significantly accelerate BTI, since these traps are close to the dielectric-Si interface in scaled technology. Secondly, the prevalent reliability analysis lacks a direct validation of the lifetime of devices and circuits. The aging mechanism of BTI causes gradual degradation of the device leading to threshold voltage shift and increasing the failure rate. In the 28nm HKMG technology, contribution of BTI to NMOS degradation has become significant at high temperature as compared to Channel Hot Carrier (CHC). This requires revising the End of Lifetime (EOL) calculation based on contribution from induvial aging effects especially in feedback loops. Conventionally, aging in devices is extrapolated from a short-term measurement, but this practice results in unreliable prediction of EOL caused by variability in initial parameters and stress conditions. To mitigate the extrapolation issues and improve predictability, this work aims at providing a new approach to test the device to EOL in a fast and controllable manner. The contributions of this thesis include: (1) based on stochastic trapping/de-trapping mechanism, new compact BTI models are developed and verified with 14nm FinFET and 28nm HKMG data. Moreover, these models are implemented into circuit simulation, illustrating a significant increase in failure rate due to accelerated BTI, (2) developing a model to predict accelerated aging under special conditions like feedback loops and stacked inverters, (3) introducing a feedback loop based test methodology called Adaptive Accelerated Aging (AAA) that can generate accurate aging data till EOL, (4) presenting simulation and experimental data for the models and providing test setup for multiple stress conditions, including those for achieving EOL in 1 hour device as well as ring oscillator (RO) circuit for validation of the proposed methodology, and (5) scaling these models for finding a guard band for VLSI design circuits that can provide realistic aging impact.

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Created

Date Created
  • 2017

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Modeling of Copper Migration In CdTe Photovoltaic Devices

Description

Thin-film modules of all technologies often suffer from performance degradation over time. Some of the performance changes are reversible and some are not, which makes deployment, testing, and energy-yield prediction

Thin-film modules of all technologies often suffer from performance degradation over time. Some of the performance changes are reversible and some are not, which makes deployment, testing, and energy-yield prediction more challenging. The most commonly alleged causes of instability in CdTe device, such as “migration of Cu,” have been investigated rigorously over the past fifteen years. As all defects, intrinsic or extrinsic, interact with the electrical potential and free carriers so that charged defects may drift in the electric field and changing ionization state with excess free carriers. Such complexity of interactions in CdTe makes understanding of temporal changes in device performance even more challenging. The goal of the work in this dissertation is, thus, to eliminate the ambiguity between the observed performance changes under stress and their physical root cause by enabling a depth of modeling that takes account of diffusion and drift at the atomistic level coupled to the electronic subsystem responsible for a PV device’s function. The 1D Unified Solver, developed as part of this effort, enables us to analyze PV devices at a greater depth.

In this dissertation, the implementation of a drift-diffusion model defect migration simulator, development of an implicit reaction scheme for total mass conservation, and a couple of other numerical schemes to improve the overall flexibility and robustness of this coupled Unified Solver is discussed. Preliminary results on Cu (with or without Cl-treatment) annealing simulations in both single-crystal CdTe wafer and poly-crystalline CdTe devices show promising agreement to experimental findings, providing a new perspective in the research of improving doping concentration hence the open-circuit voltage of CdTe technology. Furthermore, on the reliability side, in agreement of previous experimental reports, simulation results suggest possibility of Cu depletion in short-circuited cells stressed at elevated temperature. The developed solver also successfully demonstrated that mobile donor migration can be used to explain solar cell performance changes under different stress conditions.

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Created

Date Created
  • 2017

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Accelerated Reliability Testing of Fresh and Field-Aged Photovoltaic Modules: Encapsulant Browning and Solder Bond Degradation

Description

The popularity of solar photovoltaic (PV) energy is growing across the globe with more than 500 GW installed in 2018 with a capacity of 640 GW in 2019. Improved PV

The popularity of solar photovoltaic (PV) energy is growing across the globe with more than 500 GW installed in 2018 with a capacity of 640 GW in 2019. Improved PV module reliability minimizes the levelized cost of energy. Studying and accelerating encapsulant browning and solder bond degradation—two of the most commonly observed degradation modes in the field—in a lab requires replicating the stress conditions that induce the same field degradation modes in a controlled accelerated environment to reduce testing time.

Accelerated testing is vital in learning about the reliability of solar PV modules. The unique streamlined approach taken saves time and resources with a statistically significant number of samples being tested in one chamber under multiple experimental stress conditions that closely mirror field conditions that induce encapsulant browning and solder bond degradation. With short circuit current (Isc) and series resistance (Rs) degradation data sets at multiple temperatures, the activation energies (Ea) for encapsulant browning and solder bond degradation was calculated.

Regular degradation was replaced by the wear-out stages of encapsulant browning and solder bond degradation by subjecting two types of field-aged modules to further accelerated testing. For browning, the Ea calculated through the Arrhenius model was 0.37 ± 0.17 eV and 0.71 ± 0.07 eV. For solder bond degradation, the Arrhenius model was used to calculate an Ea of 0.12 ± 0.05 eV for solder with 2wt% Ag and 0.35 ± 0.04 eV for Sn60Pb40 solder.

To study the effect of types of encapsulant, backsheet, and solder on encapsulant browning and solder bond degradation, 9-cut-cell samples maximizing available data points while minimizing resources underwent accelerated tests described for modules. A ring-like browning feature was observed in samples with UV pass EVA above and UV cut EVA below the cells. The backsheet permeability influences the extent of oxygen photo-bleaching. In samples with solder bond degradation, increased bright spots and cell darkening resulted in increased Rs. Combining image processing with fluorescence imaging and electroluminescence imaging would yield great insight into the two degradation modes.

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Date Created
  • 2020

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Investigation of 1,900 individual field aged photovoltaic modules for potential induced degradation (PID) in a positive biased power plant

Description

Photovoltaic (PV) modules undergo performance degradation depending on climatic conditions, applications, and system configurations. The performance degradation prediction of PV modules is primarily based on Accelerated Life Testing (ALT) procedures.

Photovoltaic (PV) modules undergo performance degradation depending on climatic conditions, applications, and system configurations. The performance degradation prediction of PV modules is primarily based on Accelerated Life Testing (ALT) procedures. In order to further strengthen the ALT process, additional investigation of the power degradation of field aged PV modules in various configurations is required. A detailed investigation of 1,900 field aged (12-18 years) PV modules deployed in a power plant application was conducted for this study. Analysis was based on the current-voltage (I-V) measurement of all the 1,900 modules individually. I-V curve data of individual modules formed the basis for calculating the performance degradation of the modules. The percentage performance degradation and rates of degradation were compared to an earlier study done at the same plant. The current research was primarily focused on identifying the extent of potential induced degradation (PID) of individual modules with reference to the negative ground potential. To investigate this, the arrangement and connection of the individual modules/strings was examined in detail. The study also examined the extent of underperformance of every series string due to performance mismatch of individual modules in that string. The power loss due to individual module degradation and module mismatch at string level was then compared to the rated value.

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Created

Date Created
  • 2011

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Next generation photovoltaic modules: visualizing deflection and analyzing stress

Description

Stress-related failure such as cracking are an important photovoltaic (PV) reliability issue since it accounts for a high percentage of power losses in the midlife-failure and wear-out failure regimes. Cell

Stress-related failure such as cracking are an important photovoltaic (PV) reliability issue since it accounts for a high percentage of power losses in the midlife-failure and wear-out failure regimes. Cell cracking can only be correlated with module degradation when cracks are of detectable size and detrimental to the performance. Several techniques have been explored to access the deflection and stress status on solar cell, but they have disadvantages such as high surface sensitivity.

This dissertation presents a new and non-destructive method for mapping the deflection on encapsulated solar cells using X-ray topography (XRT). This method is based on Bragg diffraction imaging, where only the areas that meet diffraction conditions will present contrast. By taking XRT images of the solar cell at various sample positions and applying an in-house developed algorithm framework, the cell‘s deflection map is obtained. Error analysis has demonstrated that the errors from the experiment and the data processing are below 4.4 and 3.3%.

Von Karman plate theory has been applied to access the stress state of the solar cells. Under the assumptions that the samples experience pure bending and plain stress conditions, the principal stresses are obtained from the cell deflection data. Results from a statistical analysis using a Weibull distribution suggest that 0.1% of the data points can contribute to critical failure. Both the soldering and lamination processes put large amounts of stress on solar cells. Even though glass/glass packaging symmetry is preferred over glass/backsheet, the solar cells inside the glass/glass packaging experience significantly more stress. Through a series of in-situ four-point bending test, the assumptions behind Von Karman theory are validated for cases where the neutral plane is displaced by the tensile and compressive stresses.

The deflection and stress mapping method is applied to two next generation PV concepts named Flex-circuit and PVMirror. The Flex-circuit module concept replaces traditional metal ribbons with Al foils for electrical contact and PVMirror concept utilizes a curved PV module design with a dichroic film for thermal storage and electrical output. The XRT framework proposed in this dissertation successfully characterized the impact of various novel interconnection and packaging solutions.

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Created

Date Created
  • 2019

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State Estimation for Enhanced Monitoring, Reliability, Restoration and Control of Smart Distribution Systems

Description

The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small

The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small customer sited distributed generation (DG), and better utilize assets. The Smart Grid environment is envisioned to include distributed generation, flexible and controllable loads, bidirectional communications using smart meters and other technologies. Sensory technology may be utilized as a tool that enhances operation including operation of the distribution system. Addressing this point, a distribution system state estimation algorithm is developed in this thesis. The state estimation algorithm developed here utilizes distribution system modeling techniques to calculate a vector of state variables for a given set of measurements. Measurements include active and reactive power flows, voltage and current magnitudes, phasor voltages with magnitude and angle information. The state estimator is envisioned as a tool embedded in distribution substation computers as part of distribution management systems (DMS); the estimator acts as a supervisory layer for a number of applications including automation (DA), energy management, control and switching. The distribution system state estimator is developed in full three-phase detail, and the effect of mutual coupling and single-phase laterals and loads on the solution is calculated. The network model comprises a full three-phase admittance matrix and a subset of equations that relates measurements to system states. Network equations and variables are represented in rectangular form. Thus a linear calculation procedure may be employed. When initialized to the vector of measured quantities and approximated non-metered load values, the calculation procedure is non-iterative. This dissertation presents background information used to develop the state estimation algorithm, considerations for distribution system modeling, and the formulation of the state estimator. Estimator performance for various power system test beds is investigated. Sample applications of the estimator to Smart Grid systems are presented. Applications include monitoring, enabling demand response (DR), voltage unbalance mitigation, and enhancing voltage control. Illustrations of these applications are shown. Also, examples of enhanced reliability and restoration using a sensory based automation infrastructure are shown.

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Created

Date Created
  • 2012

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A Unified 2D Solver for Modeling Carrier and Defect Dynamics in Electronic and Photovoltaic Devices

Description

Semiconductor devices often face reliability issues due to their operational con-

ditions causing performance degradation over time. One of the root causes of such

degradation is due to point defect dynamics and

Semiconductor devices often face reliability issues due to their operational con-

ditions causing performance degradation over time. One of the root causes of such

degradation is due to point defect dynamics and time dependent changes in their

chemical nature. Previously developed Unified Solver was successful in explaining

the copper (Cu) metastability issues in cadmium telluride (CdTe) solar cells. The

point defect formalism employed there could not be extended to chlorine or arsenic

due to numerical instabilities with the dopant chemical reactions. To overcome these

shortcomings, an advanced version of the Unified Solver called PVRD-FASP tool was

developed. This dissertation presents details about PVRD-FASP tool, the theoretical

framework for point defect chemical formalism, challenges faced with numerical al-

gorithms, improvements for the user interface, application and/or validation of the

tool with carefully chosen simulations, and open source availability of the tool for the

scientific community.

Treating point defects and charge carriers on an equal footing in the new formalism

allows to incorporate chemical reaction rate term as generation-recombination(G-R)

term in continuity equation. Due to the stiff differential equations involved, a reaction

solver based on forward Euler method with Newton step is proposed in this work.

The Jacobian required for Newton step is analytically calculated in an elegant way

improving speed, stability and accuracy of the tool. A novel non-linear correction

scheme is proposed and implemented to resolve charge conservation issue.

The proposed formalism is validated in 0-D with time evolution of free carriers

simulation and with doping limits of Cu in CdTe simulation. Excellent agreement of

light JV curves calculated with PVRD-FASP and Silvaco Atlas tool for a 1-D CdTe

solar cell validates reaction formalism and tool accuracy. A closer match with the Cu

SIMS profiles of Cu activated CdTe samples at four different anneal recipes to the

simulation results show practical applicability. A 1D simulation of full stack CdTe

device with Cu activation at 350C 3min anneal recipe and light JV curve simulation

demonstrates the tool capabilities in performing process and device simulations. CdTe

device simulation for understanding differences between traps and recombination

centers in grain boundaries demonstrate 2D capabilities.

Contributors

Agent

Created

Date Created
  • 2019