Failure analysis defect localization of a metal stringer defect on a monolithic step-up DC-DC converter

Galarce, Rowin V.; Lupena, Francis Nikolai; Jimenez, Benedict; Teo, Siew Mei

doi:10.1109/ipfa.2016.7564269

2016 IEEE 23rd International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) 2016

DOI: 10.1109/ipfa.2016.7564269

|View full text |Cite

Failure analysis defect localization of a metal stringer defect on a monolithic step-up DC-DC converter

Rowin V. Galarce¹,

Francis Nikolai Lupena²,

Benedict Jimenez³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Online Monitoring of Power Converter Degradation Using Deep Neural Network

et al. 2021

View full text Add to dashboard Cite

Power semiconductor devices in the power converters used for motor drives are susceptible to wear-out and failure, especially when operated in harsh environments. Therefore, detection of degradation of power devices is crucial for ensuring the reliable performance of power converters. In this paper, a deep learning approach for online classification of the health states of the snubber resistors in the Insulated Gate Bipolar Transistors (IGBTs) in a three-phase Brushless DC (BLDC) motor drive is proposed. The method can locate one out of the six IGBTs experiencing a snubber resistor degradation problem by measuring the voltage waveforms of the three shunt resistors using voltage sensors. The range of the degradation of the snubber resistors for successful classification is also investigated. The off-the-shelf deep Convolutional Neural Network (CNN) architecture ResNet50 is used for transfer learning to determine which snubber resistor has degraded. The dataset for evaluating the above classification scheme of IGBT degradation is obtained by measuring the shunt voltage waveforms with varying snubber resistance and reference current. Then, the three-phase voltage waveforms are converted into greyscale images and RGB spectrogram images, which are later fed into the deep CNN. Experiments are carried out on the greyscale image dataset and the spectrogram image dataset using four-fold cross-validation. The results show that the proposed scheme can classify seven classes (one class for normal condition and six classes for abnormal condition in one of the six IGBTs in a three-phase BLDC drive) with over 95% average accuracy within a specific range of snubber resistance. Using grayscale images and using spectrogram-based RGB images yields similar accuracy.

show abstract

Online Monitoring of Power Converter Degradation Using Deep Neural Network

et al. 2021

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Failure analysis defect localization of a metal stringer defect on a monolithic step-up DC-DC converter

Cited by 1 publication

References 0 publications

Online Monitoring of Power Converter Degradation Using Deep Neural Network

Online Monitoring of Power Converter Degradation Using Deep Neural Network

Contact Info

Product

Resources

About