In Situ Diagnostics and Prognostics of Wire Bonding Faults in IGBT Modules for Electric Vehicle Drives

Ji, Bing; Pickert, Volker; Cao, Wenping; Zahawi, Bashar

doi:10.1109/tpel.2013.2251358

Cited by 277 publications

(134 citation statements)

References 55 publications

(12 reference statements)

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“…This is perhaps why there are a variety of aging trends reported in the literature when examining the forward voltage of a device. For example, increases in the forward voltage in IGBTs of 5%, 7%, and 20% have been reported before module failure occurs [7,9,11,15]. At the same time, decreases of up to 25% were present in other studies [7,16,18].…”

Section: Background: Reliability and Condition Monitoring Of Power Sementioning

confidence: 89%

“…The majority of research has focused on monitoring an electrical parameter that indicates degradation. Examples of the most commonly monitored parameters are: the forward voltage in IGBTs (or the on-state resistance in MOSFETs) [9][10][11] and the evolution of a module's thermal resistance (Rth) [12][13][14][15][16]. These parameters have been studied since the 1990s.…”

Section: Background: Reliability and Condition Monitoring Of Power Sementioning

confidence: 99%

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Simultaneous On-State Voltage and Bond-Wire Resistance Monitoring of Silicon Carbide MOSFETs

2017

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Abstract:In fast switching power semiconductors, the use of a fourth terminal to provide the reference potential for the gate signal-known as a kelvin-source terminal-is becoming common. The introduction of this terminal presents opportunities for condition monitoring systems. This article demonstrates how the voltage between the kelvin-source and power-source can be used to specifically monitor bond-wire degradation. Meanwhile, the drain to kelvin-source voltage can be monitored to track defects in the semiconductor die or gate driver. Through an accelerated aging test on 20 A Silicon Carbide Metal-Oxide-Semiconductor-Field-Effect Transistors (MOSFETs), it is shown that there are opposing trends in the evolution of the on-state resistances of both the bond-wires and the MOSFET die. In summary, after 50,000 temperature cycles, the resistance of the bond-wires increased by up to 2 mΩ, while the on-state resistance of the MOSFET dies decreased by approximately 1 mΩ. The conventional failure precursor (monitoring a single forward voltage) cannot distinguish between semiconductor die or bond-wire degradation. Therefore, the ability to monitor both these parameters due to the presence of an auxiliary-source terminal can provide more detailed information regarding the aging process of a device.

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Section: Background: Reliability and Condition Monitoring Of Power Sementioning

confidence: 89%

Section: Background: Reliability and Condition Monitoring Of Power Sementioning

confidence: 99%

Simultaneous On-State Voltage and Bond-Wire Resistance Monitoring of Silicon Carbide MOSFETs

2017

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“…Although, prognostic approaches can also be employed in system diagnostics, but the main challenge occurs in situations where the fault was sudden, which means prognostics will fail to make a viable prediction. Due to IVHM needs, requirements to automotive diagnostic protocols is increased during last few years because future vehicles are highly equipped with low power electronics in almost any equipment (even components) such as electronic fuel injection, automatic gear box, etc [13]. Having more electronic featured components and systems will require additional care toward resilient trustworthy cyber-physical systems as safety, security and privacy issues may affected by, for instance, by hackers, hardware and software Trojans [4] [14].…”

Section: Reliability Information From Ivhm Perspectivementioning

confidence: 99%

“…Nowadays, it is well understood that one of the most vulnerable component that current EVs' are suffering from their failures is IGBT that brings limitation on safety and economic issues of EVs [13]. Alghassi et al has discussed different failures mechanisms related to IGBT and they have also presented prognosis model for the dominant failure at a component level in [21] [22].…”

Section: A Dc-dc Convertermentioning

confidence: 99%

Reliability enhanced EV using pattern recognition techniques

Samie

Perinpanayagam

Alghassi

et al. 2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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This is a copy of the author's accepted version of a paper subsequently published in the proceedings of 2014 IEEE International Electric Vehicle Conference (IEVC). IEEE, pp. 1-8. It is available online at:http://dx.doi.org/10.1109/IEVC.2014.7056199The WestminsterResearch online digital archive at the University of Westminster aims to make the research output of the University available to a wider audience. Copyright and Moral Rights remain with the authors and/or copyright owners.© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Whilst further distribution of specific materials from within this archive is forbidden, you may freely distribute the URL of WestminsterResearch: (http://westminsterresearch.wmin.ac.uk/). Abstract-The following paper will contribute to the development of novel data transmission techniques from an IVHM perspective so that Electrical Vehicles (EV) will be able to communicate semantically by directly pointing out to the worst failure/threat scenarios. This is achieved by constructing an image-based data communication in which the data that is monitored by a vast number of different sensors are collected as images; and then, the meaningful failure/threat objects are transmitted among a number of EVs. The meanings of these objects that are clarified for each EV by a set of training patterns are semantically linked from one to other EVs through the similarities that the EVs share. This is a similar approach to wellknown image compression and retrieval techniques, but the difference is that the training patterns, codebook, and codewords within the different EVs are not the same. Hence, the initial image that is compressed at the transmitter side does not exactly match the image retrieved at the receiver's side; as it concerns both EVs semantically that mainly addresses the worst risky scenarios. As an advantage, connected EVs would require less number of communication channels to talk together while also reducing data bandwidth as it only sends the similarity rates and tags of patterns instead of sending the whole initial image that is constructed from various sensors, including cameras. As a case study, this concept is applied to DC-DC converters which refer to a system that presents one of the major problems for EVs.

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“…IGBT is one of the most widely used power modules in power electronic converters, and its reliability has drawn much attention since it is one of the most fragile components in power converters [1][2][3]. Researches have shown that the thermal behavior of IGBT has a significant effect on the reliability of the power conversion system.…”

Section: Introductionmentioning

confidence: 99%

Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

Wu¹,

Zhou²,

Du³

et al. 2014

Journal of Electrical Engineering and Technology

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-In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.

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In Situ Diagnostics and Prognostics of Wire Bonding Faults in IGBT Modules for Electric Vehicle Drives

Cited by 277 publications

References 55 publications

Simultaneous On-State Voltage and Bond-Wire Resistance Monitoring of Silicon Carbide MOSFETs

Simultaneous On-State Voltage and Bond-Wire Resistance Monitoring of Silicon Carbide MOSFETs

Reliability enhanced EV using pattern recognition techniques

Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

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