Wire bond reliability for power electronic modules - effect of bonding temperature

In this paper, thermal cycling reliability along with ANSYS analysis of the residual stress generated in heavy-gauge Al bond wires at different bonding temperatures is reported. 99.999% pure Al wires of 375 mum in diameter, were ultrasonically bonded to silicon dies coated with a 5mum thick Al metallisation at 25degC (room temperature), 100degC and 200degC, respectively (with the same bonding parameters). The wire bonded samples were then subjected to thermal cycling in air from -60degC to +150degC. The degradation rate of the wire bonds was assessed by means of bond shear test and via microstructural characterisation. Prior to thermal cycling, the shear strength of all of the wire bonds was approximately equal to the shear strength of pure aluminum and independent of bonding temperature. During thermal cycling, however, the shear strength of room temperature bonded samples was observed to decrease more rapidly (as compared to bonds formed at 100degC and 200degC) as a result of a high crack propagation rate across the bonding area. In addition, modification of the grain structure at the bonding interface was also observed with bonding temperature, leading to changes in the mechanical properties of the wire. The heat and pressure induced by the high temperature bonding is believed to promote grain recovery and recrystallisation, softening the wires through removal of the dislocations and plastic strain energy. Coarse grains formed at the bonding interface after bonding at elevated temperatures may also contribute to greater resistance for crack propagation, thus lowering the wire bond degradation rate

Optimizing the reliability of power electronics module isolation substrates

Optimal design of a power electronics module isolation substrate is assessed using a combination of finite element structural mechanics analysis and response surface optimisation technique. Primary failure modes in power electronics modules include the loss of structural integrity in the ceramic substrate materials due to stresses induced through thermal cycling. Analysis of the influence of ceramic substrate design parameters is undertaken using a design of experiments approach. Finite element analysis is used to determine the stress distribution for each design, and the results are used to construct a quadratic response surface function. A particle swarm optimisation algorithm is then used to determine the optimal substrate design. Analysis of response surface function gradients is used to perform sensitivity analysis and develop isolation substrate design rules. The influence of design uncertainties introduced through manufacturing tolerances is assessed using a Monte-Carlo algorithm, resulting in a stress distribution histogram. The probability of failure caused by the violation of design constraints has been analyzed. Six geometric design parameters are considered in this work and the most important design parameters have been identified. Overall analysis results can be used to enhance the design and reliability of the component.

Real-time temperature estimation for power MOSFETs considering thermal aging effects

This paper presents a novel real-time power-device temperature estimation method that monitors the power MOSFET's junction temperature shift arising from thermal aging effects and incorporates the updated electrothermal models of power modules into digital controllers. Currently, the real-time estimator is emerging as an important tool for active control of device junction temperature as well as online health monitoring for power electronic systems, but its thermal model fails to address the device's ongoing degradation. Because of a mismatch of coefficients of thermal expansion between layers of power devices, repetitive thermal cycling will cause cracks, voids, and even delamination within the device components, particularly in the solder and thermal grease layers. Consequently, the thermal resistance of power devices will increase, making it possible to use thermal resistance (and junction temperature) as key indicators for condition monitoring and control purposes. In this paper, the predicted device temperature via threshold voltage measurements is compared with the real-time estimated ones, and the difference is attributed to the aging of the device. The thermal models in digital controllers are frequently updated to correct the shift caused by thermal aging effects. Experimental results on three power MOSFETs confirm that the proposed methodologies are effective to incorporate the thermal aging effects in the power-device temperature estimator with good accuracy. The developed adaptive technologies can be applied to other power devices such as IGBTs and SiC MOSFETs, and have significant economic implications. 

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Facile synthesis of nanocrystalline LiFePO4/graphene composite as cathode material for high power lithium ion batteries

We describe a simple strategy, which is based on the idea of space confinement, for the synthesis of carbon coating on LiFePO₄ nanoparticles/graphene nanosheets composites in a water-in-oil emulsion system. The prepared composite displayed high performance as a cathode material for lithium-ion battery, such as high reversible lithium storage capacity (158 mA h g^-1 after 100 cycles), high coulombic efficiency (over 97%), excellent cycling stability and high rate capability (as high as 83 mA h g ^-1 at 60 C). Very significantly, the preparation method employed can be easily adapted and be extended as a general approach to sophisticated compositions and structures for the preparation of highly dispersed nanosized structure on graphene. 

The effects of altered heat stress on voluntary pacing strategies during prolonged cycling

Central Governor Model (CGM) suggests that perturbations in the rate of heat storage (AS) are centrally integrated to regulate exercise intensity in a feed-forward fashion to prevent excessive thermal strain. We directly tested the CGM by manipulating ambient temperature (Tam) at 20-minute intervals from 20°C to 35°C, and returning to 20°C, while cycling at a set rate of perceived exertion (RPE). The synchronicity of power output (PO) with changes in HS and Tam were quantified using Auto-Regressive Integrated Moving Averages analysis. PO fluctuated irregularly but was not significantly correlated to changes in thermo physiological status. Repeated measures indicated no changes in lactate accumulation. In conclusion, real time dynamic sensation of Tam and integration of HS does not directly influence voluntary pacing strategies during sub-maximal cycling at a constant RPE while non-significant changes in blood lactate suggest an absence of peripheral fatigue.

Kinetics of martensitic transitions in Cu-Al-Mn under thermal cycling: Analysis at multiple length scales

In this paper we study the evolution of the kinetic features of the martensitic transition in a Cu-Al-Mn single crystal under thermal cycling. The use of several experimental techniques including optical microscopy, calorimetry, and acoustic emission, has enabled us to perform an analysis at multiple scales. In particular, we have focused on the analysis of avalanche events (associated with the nucleation and growth of martensitic domains), which occur during the transition. There are significant differences between the kinetics at large and small length scales. On the one hand, at small length scales, small avalanche events tend to sum to give new larger events in subsequent loops. On the other hand, at large length scales the large domains tend to split into smaller ones on thermal cycling. We suggest that such different behavior is the necessary ingredient that leads the system to the final critical state corresponding to a power-law distribution of avalanches.

Life cycle analysis of UK coal fired power plants

This paper examines the life cycle GHG emissions from existing UK pulverized coal power plants. The life cycle of the electricity Generation plant includes construction, operation and decommissioning. The operation phase is extended to upstream and downstream processes. Upstream processes include the mining and transport of coal including methane leakage and the production and transport of limestone and ammonia, which are necessary for flue gas clean up. Downstream processes, on the other hand, include waste disposal and the recovery of land used for surface mining. The methodology used is material based process analysis that allows calculation of the total emissions for each process involved. A simple model for predicting the energy and material requirements of the power plant is developed. Preliminary calculations reveal that for a typical UK coal fired plant, the life cycle emissions amount to 990 g CO2-e/kWh of electricity generated, which compares well with previous UK studies. The majority of these emissions result from direct fuel combustion (882 g/kWh 89%) with methane leakage from mining operations accounting for 60% of indirect emissions. In total, mining operations (including methane leakage) account for 67.4% of indirect emissions, while limestone and other material production and transport account for 31.5%. The methodology developed is also applied to a typical IGCC power plant. It is found that IGCC life cycle emissions are 15% less than those from PC power plants. Furthermore, upon investigating the influence of power plant parameters on life cycle emissions, it is determined that, while the effect of changing the load factor is negligible, increasing efficiency from 35% to 38% can reduce emissions by 7.6%. The current study is funded by the UK National Environment Research Council (NERC) and is undertaken as part of the UK Carbon Capture and Storage Consortium (UKCCSC). Future work will investigate the life cycle emissions from other power generation technologies with and without carbon capture and storage. The current paper reveals that it might be possible that, when CCS is employed. the emissions during generation decrease to a level where the emissions from upstream processes (i.e. coal production and transport) become dominant, and so, the life cycle efficiency of the CCS system can be significantly reduced. The location of coal, coal composition and mining method are important in determining the overall impacts. In addition to studying the net emissions from CCS systems, future work will also investigate the feasibility and technoeconomics of these systems as a means of carbon abatement.

Life cycle GHG assessment of fossil fuel power plants with carbon capture and storage

The evaluation of life cycle greenhouse gas emissions from power generation with carbon capture and storage (CCS) is a critical factor in energy and policy analysis. The current paper examines life cycle emissions from three types of fossil-fuel-based power plants, namely supercritical pulverized coal (super-PC), natural gas combined cycle (NGCC) and integrated gasification combined cycle (IGCC), with and without CCS. Results show that, for a 90% CO2 capture efficiency, life cycle GHG emissions are reduced by 75-84% depending on what technology is used. With GHG emissions less than 170 g/kWh, IGCC technology is found to be favorable to NGCC with CCS. Sensitivity analysis reveals that, for coal power plants, varying the CO2 capture efficiency and the coal transport distance has a more pronounced effect on life cycle GHG emissions than changing the length of CO2 transport pipeline. Finally, it is concluded from the current study that while the global warming potential is reduced when MEA-based CO2 capture is employed, the increase in other air pollutants such as NOx and NH3 leads to higher eutrophication and acidification potentials.

Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications

BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.

Máxima fase estável de lactato sanguíneo e potência crítica em ciclistas bem treinados

O principal objetivo deste estudo foi comparar a intensidade correspondente à máxima fase estável de lactato (MLSS) e a potência crítica (PC) durante o ciclismo em indivíduos bem treinados. Seis ciclistas do sexo masculino (25,5 ± 4,4 anos, 68,8 ± 3,0kg, 173,0 ± 4,0cm) realizaram em diferentes dias os seguintes testes: exercício incremental até a exaustão para a determinação do pico de consumo de oxigênio (VO2pico) e sua respectiva intensidade (IVO2pico); cinco a sete testes de carga constante para a determinação da MLSS e da PC; e um exercício até a exaustão na PC. A MLSS foi considerada com a maior intensidade de exercício onde a concentração de lactato não aumentou mais do que 1mM entre o 10º e o 30º min de exercício. Os valores individuais de potência (95, 100 e 110% IVO2pico) e seu respectivo tempo máximo de exercício (Tlim) foram ajustados a partir do modelo hiperbólico de dois parâmetros para a determinação da PC. Embora altamente correlacionadas (r = 0,99; p = 0,0001), a PC (313,5 ± 32,3W) foi significantemente maior do que a MLLS (287,0 ± 37,8W) (p = 0,0002). A diferença percentual da PC em relação à MLSS foi de 9,5 ± 3,1%. No exercício realizado na PC, embora tenha existido componente lento do VO2 (CL = 400,8 ± 267,0 ml.min-1), o VO2pico não foi alcançado (91,1 ± 3,3 %). Com base nesses resultados pode-se concluir que a PC e a MLSS identificam diferentes intensidades de exercício, mesmo em atletas com elevada aptidão aeróbia. Entretanto, o percentual da diferença entre a MLLS e PC (9%) indica que relação entre esses dois índices pode depender da aptidão aeróbia. Durante o exercício realizado até a exaustão na PC, o CL que é desenvolvido não permite que o VO2pico seja alcançado.

Effect of aerobic training status on both maximal lactate steady state and critical power

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

PHYSIOLOGICAL and PERCEIVED EXERTION RESPONSES AT INTERMITTENT CRITICAL POWER and INTERMITTENT MAXIMAL LACTATE STEADY STATE

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

There is no anaerobic work capacity replenishment at critical power intensity: An indirect evidence

Aim. - This study aimed to test if investigate whether the anaerobic work capacity is replenished while exercising at critical power intensity. Then, a known exercise duration, which demands high anaerobic energy contribution, was compared to intermittent exercise duration with passive and active (cycling at critical power intensity) rest periods.Methods. - Nine participants performed five sessions of testing. From the 1st to the 3rd sessions, individuals cycled continuously at different workloads (P-high, P-intermediate and P-low) in order to estimate the critical power and the anaerobic work capacity. The 4th and 5th sessions were performed in order to determine the influence of anaerobic work capacity replenishment oil exercise duration. They consisted of manipulating the resting type (passive or active) between two cycling efforts. The total exercise duration was determined by the sum of the two cycling efforts duration.Results. - The exercise duration under passive resting condition (408.0 +/- 42.0 s) was longer (p<0.05) than known exercise duration at P-intermediate (T-intermediate = 305.8 +/- 30.5 s) and than exercise duration performed under active resting conditions (T-active = 304.4 +/- 30.7s). However, there was no significant difference between T-intermediate and T-active.Conclusion. - These results demonstrated indirect evidence that the anaerobic work capacity is not replenished while exercising at critical power intensity. (C) 2008 Elsevier Masson SAS. All rights reserved.