Breakdown Model Research Articles

Significantly enhanced dielectric breakdown strength and energy density of multilayer ceramic capacitors with high efficiency by electrodes structure design

Multilayer ceramic capacitors (MLCCs) are attracting great interest recently, especially in energy-storage applications due to their high volumetric capacitance, high power density, and fast charge-discharge capability. However, the low dielectric breakdown strength of ferroelectric ceramics always leads to a low discharge energy density, which limits their applications in high-voltage energy-storage systems. In this work, a phase-field electromechanical breakdown model is introduced to give a fundamental understanding of the dielectric breakdown behavior of MLCCs and provide a resource-efficient design strategy for the structure of MLCCs to enhance their dielectric breakdown strength and discharge energy density. Three types of margin lengths of 100 μm, 200 μm, and 400 μm are designed and applied on the MLCCs consisting of ten dielectric layers with the single-layer thickness of 11 μm, to confirm and practice our phase-field breakdown model. A large discharge energy density of 7.8 J cm−3 can be achieved under the applied electric field of 790 kV/cm, together with a high efficiency of 88% in a 400 μm-margin-length MLCC.

Hot-Carrier Dynamics and Chemistry in Dielectric Polymers.

Dielectric polymers are widely used in electronics and energy technologies, but their performance is severely limited by the electrical breakdown under a high electric field. Dielectric breakdown is commonly understood as an avalanche of processes such as carrier multiplication and defect generation that are triggered by field-accelerated hot electrons and holes. However, how these processes are initiated remains elusive. Here, nonadiabatic quantum molecular dynamics simulations reveal microscopic processes induced by hot electrons and holes in a slab of an archetypal dielectric polymer, polyethylene, under an electric field of 600 MV/m. We found that electronic-excitation energy is rapidly dissipated within tens of femtoseconds because of strong electron-phonon scattering, which is consistent with quantum-mechanical perturbation calculations. This in turn excites other electron-hole pairs to cause carrier multiplication. We also found that the key to chemical damage is localization of holes that travel to a slab surface and weaken carbon-carbon bonds on the surface. Such quantitative information can be incorporated into first-principles-informed, predictive modeling of dielectric breakdown.

A solvable model of Landau quantization breakdown

Physics of two-dimensional electron gases under perpendicular magnetic field often displays three distinct stages when increasing the field amplitude: a low field regime with classical magnetotransport, followed at intermediate field by a Shubnikov-de Haas phase where the transport coefficients present quantum oscillations, and, ultimately, the emergence at high field of the quantum Hall effect with perfect quantization of the Hall resistance. A rigorous demonstration of this general paradigm is still limited by the difficulty in solving models of quantum Hall bars with macroscopic lateral dimensions and smooth disorder. We propose here the exact solution of a simple model exhibiting similarly two sharp transitions that are triggered by the competition of cyclotron motion and potential-induced drift. As a function of increasing magnetic field, one observes indeed three distinct phases showing respectively fully broken, partially smeared, or perfect Landau level quantization. This model is based on a non-rotationally invariant, inverted two-dimensional harmonic potential, from which a full quantum solution is obtained using 4D phase space quantization. The developed formalism unifies all three possible regimes under a single analytical theory, as well as arbitrary quadratic potentials, for all magnetic field values.

The role of saliva in oral processing: Reconsidering the breakdown path paradigm.

We discuss food oral processing research over the last two decades and consider strategies for quantifying the food breakdown model, originally conceptualized by Hutchings and Lillford. The key innovation in their seminal 1988 paper was shifting the focus from intact food properties, measured in the lab, toward strategies to capture the dynamic nature of eating. This has stimulated great progress in the field, but a key aspect missing in oral processing research is the conversion of the Hutchings and Lillford breakdown path conceptual model into quantifiable parameters considered in the context of physiological factors such as saliva and oral movements. To address these shortcomings, we propose the following analysis: Hutchings's and Lillford's definitions of "Structure" and "Lubrication" are incomplete and they comprise many and varied physicochemical properties. We offer, here, a deeper analysis of each parameter, and propose strategies for researchers to consider in their quantification as an update of the Hutchings and Lillford Breakdown path.

Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm

The costs of preventive maintenance have been extensively studied by scholars across all preventive optimization model disciplines. However, one phenomenon fails to be fully studied: breakdown and breakdown maintenance costs. We set out to fill this gap in this study. This study considered the cost of equipment preventive maintenance, and the breakdown maintenance cost caused by an accidental breakdown. In order to more accurately establish the reliability model of equipment breakdown, the three-parameter Weibull distribution was applied to set up the reliability model of equipment and the differential evolution algorithm was adopted to optimize the parameters. On this basis, preventive maintenance was regarded as imperfect maintenance in the study of preventive maintenance strategies for single equipment. In consideration of the combined influence of preventive maintenance and breakdown maintenance, a maintenance strategy in which preventive maintenance times N served as the decision variable was obtained to build a mathematical model on benefit expectation of single equipment in unit time. Based on the research of single equipment, a further study was performed on the multi-equipment series system. Moreover, two strategies were given, both of which use preventive maintenance times N as the decision variable. The first strategy is to take the average cost rate of the system under long-term operation as the optimization objective, while the second strategy is to apply single component maintenance strategy in series system. In the numerical example study, a series manufacturing system composed of two devices was chosen as the research object. Interestingly, we discussed the effect of initial conditions of two-parameter DE on output results. After acquiring the optimal initialization parameters, the failure rate function was achieved by the DE to estimate the parameters of three-parameter Weibull distribution. Meanwhile, the maintenance times N was optimized according to the two strategies respectively. The best result were selected from the results of both strategies based on availability. Thus, the validity and practicability of the proposed research methods are verified.

A History of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience

Recent years have seen major political crises throughout the world, and foreign policy analysts nearly universally expect to see rising tensions within (and between) countries in the next 5–20 years. Being able to predict future crises and to assess the resilience of different countries to various shocks is of foremost importance in averting the potentially huge human costs of state collapse and civil war. The premise of this paper is that a transdisciplinary approach to forecasting social breakdown, recovery, and resilience is entirely feasible, as a result of recent breakthroughs in statistical analysis of large-scale historical data, the qualitative insights of historical and semiotic investigations, and agent-based models that translate between micro-dynamics of interacting individuals and the collective macro-level events emerging from these interactions. Our goal is to construct a series of probabilistic scenarios of social breakdown and recovery, based on historical crises and outcomes, which can aid the analysis of potential outcomes of future crises. We call this approach—similar in spirit to ensemble forecasting in weather prediction—multipath forecasting (MPF). This paper aims to set out the methodological premises and basic stages envisaged to realize this goal within a transdisciplinary research collaboration: first, the statistical analysis of a massive database of past instances of crisis to determine how actual outcomes (the severity of disruption and violence, the speed of resolution) depend on inputs (economic, political, and cultural factors); second, the encoding of these analytical insights into probabilistic, empirically informed computational models of societal breakdown and recovery—the MPF engine; third, testing the MPF engine to “predict” the trajectories and outcomes of another set of past social upheavals, which were not used in building the model. This “historical retrodiction” is an innovation that will allow us to further refine the MPF technology. Ultimately our vision is to use MPF to help write what we call “a history of possible futures,” in which the near- and medium-term paths of societies are probabilistically forecast.

A stochastic model based on fiber breakage and matrix creep for the stress-rupture failure of unidirectional continuous fiber composites

Stress rupture is a time-dependent failure mode occurring in unidirectional fiber composites under sustained tensile loads, resulting in highly variable lifetimes. Stress-rupture is of particular concern in composite overwrapped pressure vessels (COPVs) since it is unpredictable, and has catastrophic consequences. At the micromechanical level, stress rupture begins with the breakdown of individual fibers at random flaws, followed by local load-transfer to intact neighbors through shear stress in the matrix. Over time, the matrix creeps in shear causing lengthening overload zones around fiber breaks, resulting in even more fiber breaks, and eventually, formation of a catastrophically unstable break cluster. Current reliability models are direct extensions of classic stochastic breakdown models for a single fiber, and do not reflect such micromechanical activity. These models are adequate for modeling composite stress rupture under a constant load, however, they may be unrealistic under more complex loading profiles, such as a constant load that follows a brief ‘proof test’ at a load level up to 1.5 times this constant load. For carbon fiber/epoxy COPVs, current models predict a reliability, conditioned on survival of a proof test, that is always higher than the reliability without such a proof test. Concern exists that this is incorrect, and that a proof test may result in reduced reliability over time. While the failure probability during a proof test may be very low, overwrap damage occurs nonetheless in the form of a large number of fibers breaks that would not occur otherwise based on fiber Weibull strength statistics. This phenomenon of increased fiber breakage during a proof test is captured in the model we develop and that specifically builds on the micromechanical failure process described above. For typical proof-test load ratios, the model predicts conditional reliabilities for lifetime that are typically much lower than those calculated in the absence of a proof test.

Investigation of Switching-Induced Local Defects in Oxide-Based CBRAM Using Expanded Analytical Model of TDDB

The degradation behavior of Ag/SiO2-based conductive bridging RAM (CBRAM) is analyzed by conventional and expanded time-dependent dielectric breakdown (TDDB) models. By comparing the total cycling stress with time-to-breakdown ( ${t}_{\text {bd}}$ ) of the SiO2 used as a solid-electrolyte switching layer, it is clarified that the cycling stress does not reach the level at which dielectric breakdown of the SiO2 is triggered. On the other hand, we found that the ${t}_{\text {bd}}$ distribution after several cycles of set/reset stress agrees well with the simulated ${t}_{\text {bd}}$ distribution derived from expanded TDDB models, in which we assumed that a large amount of defects exists locally in the device area. This approach is useful for understanding the degradation in solid-electrolyte oxide of CBRAM.

Computer Model of Synapse Loss During an Alzheimer's Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1-The Way to Chaos and Information Transfer.

The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

A digital construction framework integrating building information modeling and reverse engineering technologies for renovation projects

Abstract This study proposes a digital construction framework that integrates building information modeling (BIM) and reverse engineering (RE) to improve information utilization in different phases and thus reduce mistakes and reworks in renovation projects during urban renewal. Three-dimensional (3D) laser scanning is used to enable the RE process. This framework also incorporates supporting technologies (virtual reality, 3D printing, and prefabrication) for a better understanding of design and construction as well as tools (work breakdown structure and model breakdown structure) for enhanced organization and management quality. Implementing this proposed framework in a renovated shopping center in Hainan, China optimized efficiency of the renovation process by 15%, eliminated design changes by 30% and reworks by 25%, and finally saved two months and 7.41% of cost regarding the steel structure canopy. Thus, this framework can proactively reduce occurrences of mistakes and reworks during the renovation process, greatly improving the effectiveness of urban renewal.

Divergent behaviour of Th and U during anatexis: Implications for the thermal evolution of orogenic crust

AbstractMobilization and migration of the heat‐producing elements (HPE) during anatexis is a critical process in the development of orogenic systems, the evolution of continental crust and the stabilization of cratons. In many crustal rocks the accessory minerals are the dominant hosts of Th and U, and the behaviour of these minerals during partial melting controls the concentrations of these elements in draining melt and residue. We use phase equilibrium modelling to evaluate if loss of melt saturated in the essential structural constituents of the accessory minerals can explain the concentrations of Th and U in residual metasedimentary migmatites and granulites along two well‐characterized crustal transects in the Ivrea zone, Italy and at Mt Stafford, Australia. While an equilibrium model of accessory mineral breakdown and melt loss approximates the depletion of U in the residual crust along both transects, it does not explain the relative enrichment of Th. We propose that the high Th concentrations in residual crust may be explained by either inhibition of monazite dissolution by kinetic factors or near‐peak growth of new high Th grains and overgrowth rims on undissolved monazite due to migration of melt through the orogenic crust. Retention of the HPE in the middle and deep orogenic crust may allow metasedimentary granulites to overcome the enthalpy barrier of melting to achieve ultrahigh temperature conditions and may be partly responsible for the slow cooling of many granulite terranes. Lastly, although the mantle was warmer and crustal heat production was higher in the past, peak temperatures and apparent thermal gradients of high‐temperature (HT)–ultrahigh temperature (UHT) granulite terranes have not decreased significantly since the Neoarchean. However, the pressure of HP granulite facies metamorphism has increased gradually from the Archean to the Phanerozoic, which suggests that the lithosphere became stronger as secular cooling of the mantle enabled plate collisions to form thicker orogens. Thus, as the lithosphere became stronger, the proportion of HT–UHT metamorphism associated with thin lithosphere and mantle heat has decreased, whereas the proportion associated with the formation of thick crust and radiogenic heat has increased.

An intriguing analogy of Kolmogorov\u2019s scaling law in a hierarchical mass\u2013spring\u2013damper model

In Richardson’s cascade description of turbulence, large vortices break up to form smaller ones, transferring the kinetic energy of the flow from large to small scales. This energy is dissipated at the smallest scales due to viscosity. We study energy cascade in a phenomenological model of vortex breakdown. The model is a binary tree of spring-connected masses, with dampers acting on the lowest level. The masses and stiffnesses between levels change according to a power law. The different levels represent different scales, enabling the definition of “mass wavenumbers.” The eigenvalue distribution of the model exhibits a devil’s staircase self-similarity. The energy spectrum of the model (defined as the energy distribution among the different mass wavenumbers) is derived in the asymptotic limit. A decimation procedure is applied to replace the model with an equivalent chain oscillator. For a significant range of the stiffness decay parameter, the energy spectrum is qualitatively similar to the Kolmogorov spectrum of 3D homogeneous, isotropic turbulence and we find the stiffness parameter for which the energy spectrum has the well-known -,5/3 scaling exponent.

Analytical Models of Breakdown Voltage and Specific On-Resistance for Vertical GaN Unipolar Devices

In this paper, the analytical models of breakdown voltage design parameters and minimum specific on-resistance in vertical gallium nitride unipolar devices are proposed. Considering the discrepancy of the impact ionization coefficients (IIC) reported in previous literatures from the Monte Carlo simulations and experiments, the analytical models of avalanche breakdown of gallium nitride devices both in punch-through and non-punch-through conditions are presented, i.e., the relationship between the breakdown voltage, drift doping concentration, drift thickness and critical electric field, which shows high accuracy with the results from numerical simulations. The comparison with the reported experimental results demonstrates the comparatively higher accuracy of the IIC data from Baliga. Taking into account the incomplete ionization in dopant impurities in GaN, the tradeoff between specific on-resistance and breakdown voltage is also given, demonstrating the optimized punch-through design has minimum specific on-resistance, which can be reduced by up to 12% for breakdown voltage from 1 kV to 15 kV. Meanwhile, the corresponding drift thickness can be reduced by from 18% to 20%, which can largely reduce the fabrication cost of epitaxial growth. The proposed optimized relationship is in good accordance with the results from MEDICI simulations.

Calculation of gain coefficient in Dwyer relativistic discharge feedback model of thunderstorm runway breakdown

Some of modern models used to describe TGF phenomenon and lightning ignition assume the existence of positive positron or gamma ray feedback. In this article we present some results of Monte-Carlo simulation of electromagnetic avalanches in atmospheric conditions and calculation of gain coefficient for both positron and gamma-ray feedback. The calculations were maid for realistic fields and acceleration cell sizes (up to 200 kV/m and 400 m respectively). The calculated gain coefficients for realistic fields and cell sizes are less than 10% which strongly discourages use of positron and gamma feedback model to describe TGF phenomenon and other gamma-production mechanisms in atmosphere under normal conditions. The simulation also shows possibility of selfsupporting positron feedback for extreme conditions like very high electric fields and very large cells.

The varying characteristics of C5F10O decomposition components at 300 K - 3500 K with a chemical kinetic model

The variation characteristics of C5F10O decomposition components at 300 K – 3500 K are studied with a chemical kinetic model considering neutral reactions. Three chemical kinetic models of SF6 breakdown are built to verify the model adopted (model C) and the results show that ion/electron participated reactions slightly change the molar fractions, which are lower than 10-14 with little influence on the insulating performance of SF6. Therefore, model C is reasonable to study the varying characteristics of C5F10O decomposition components and thus the molar fractions of C5F10O decomposition products under different temperature decreasing curves and pressures are calculated. The results show that the molar fraction of C5F10O grows to approximately one as temperature declining to 1,500 K and the temperature decreasing rates have similar affects on the relaxation time of the reactions generating C5F10O. And high pressure promotes species recombining to C5F10O by increasing the molar fractions of C5F10O decomposition components. The results can be used to evaluate C5F10O insulating performance.

Paragrammatism and agrammatism: a cortical double dissociation revealed by lesion-symptom mapping

Paragrammatism and agrammatism: a cortical double dissociation revealed by lesion-symptom mapping

An Investigation of Electric Field and Breakdown Voltage Models for a Deep Trench Superjunction SiC VDMOS

The theoretical analysis of breakdown model for a deep trench superjunction (DT-SJ) SiC VDMOS is presented in this paper. The vertical electric field distribution is derived by the electric field decomposition. Then, a fitting dependence of the critical electric field on the doping concentration for the device is obtained, based on which, the model of breakdown voltage is given for the DT-SJ SiC VDMOS. Analytical results are compared with simulative results with the same thicknesses of drift region from $8~\mu \text{m}$ to $16~\mu \text{m}$ and the doping concentrations from $4\times 10^{16}$ cm $^{-3}$ to $8\times 10^{16}$ cm $^{-3}$ . It is numerically demonstrated that the errors between model and simulation are less than 3% when N pillar and P pillar have the same width of $1\mu \text{m}$ .

A History of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience

Recent years have seen major political crises throughout the world, and foreign policy analysts nearly universally expect to see rising tensions within (and between) countries in the next 5–20 years. Being able to predict future crises and to assess the resilience of different countries to various shocks is of foremost importance in averting the potentially huge human costs of state collapse and civil war. The premise of this paper is that a transdisciplinary approach to forecasting social breakdown, recovery, and resilience is entirely feasible, as a result of recent breakthroughs in statistical analysis of large-scale historical data, the qualitative insights of historical and semiotic investigations, and agent-based models that translate between micro-dynamics of interacting individuals and the collective macro-level events emerging from these interactions. Our goal is to construct a series of probabilistic scenarios of social breakdown and recovery, based on historical crises and outcomes, which can aid the analysis of potential outcomes of future crises. We call this approach—similar in spirit to ensemble forecasting in weather prediction—multipath forecasting (MPF). This paper aims to set out the methodological premises and basic stages envisaged to realize this goal within a transdisciplinary research collaboration: first, the statistical analysis of a massive database of past instances of crisis to determine how actual outcomes (the severity of disruption and violence, the speed of resolution) depend on inputs (economic, political, and cultural factors); second, the encoding of these analytical insights into probabilistic, empirically informed computational models of societal breakdown and recovery—the MPF engine; third, testing the MPF engine to “predict” the trajectories and outcomes of another set of past social upheavals, which were not used in building the model. This “historical retrodiction” is an innovation that will allow us to further refine the MPF technology. Ultimately our vision is to use MPF to help write what we call “a history of possible futures,” in which the near- and medium-term paths of societies are probabilistically forecast.

Phase‐Field Modeling of Electromechanical Breakdown in Multilayer Ceramic Capacitors

AbstractMultilayer ceramic capacitors (MLCCs) are drawing increasing attention in the application of energy storage devices due to their high volumetric capacitance and improved energy density. However, electromechanical breakdown always occurs, especially under high operation voltage, which limits their application in high‐voltage circuit. In this work, a phase‐field electromechanical breakdown model is developed to give a fundamental understanding on the coupled electromechanical effect on the dielectric breakdown of MLCCs. The thickness‐dependent dielectric breakdown strength is well simulated through this phase‐field model. It is found that the dielectric breakdown strength and the patterns of final breakdown path is significantly influenced by the defect concentration and applied stress. An enhanced breakdown strength of MLCC is proved under either compressive or shear stress state. The model not only provides a mechanistic understanding toward the electromechanical breakdown in MLCCs, but also applies to all kinds of dielectrics and serves as guidance toward the design of high‐voltage dielectrics.

A Homeostatic Model of Subjective Cognitive Decline

Subjective Cognitive Decline (SCD) is possibly one of the earliest detectable signs of dementia, but we do not know which mental processes lead to elevated concern. In this narrative review, we will summarize the previous literature on the biomarkers and functional neuroanatomy of SCD. In order to extend upon the prevailing theory of SCD, compensatory hyperactivation, we will introduce a new model: the breakdown of homeostasis in the prediction error minimization system. A cognitive prediction error is a discrepancy between an implicit cognitive prediction and the corresponding outcome. Experiencing frequent prediction errors may be a primary source of elevated subjective concern. Our homeostasis breakdown model provides an explanation for the progression from both normal cognition to SCD and from SCD to advanced dementia stages.