In power electronic applications, transistors are a vital component. They are, however, susceptible to failures due to degradation of the interconnections and the chip itself. This paper presents a non-destructive approach for failure detection and location in power electronic devices using time-domain reflectometry. The proposed measurement and data generation method is applied to a silicon-carbide power transistor where several characteristics (R, L, C, open, short) and the location of the failure is simulated and characterized. Moreover, the method is also used to find the intrinsic properties of the transistor such as parasitic inductance and capacitance. The data generated is mapped to physical equations, however, the reflected signal of the time-domain reflectometry can be noisy due to multiple discontinuities in the transmission path. Therefore, the simulation and measurement data can be used to train hybrid machine learning models for parameter extraction which automates the failure analysis in Industry4.0 processes to ensure a smart and reliable manufacturing process.
%0 Generic
%1 sharma2021nondestructive
%A Sharma, Kanuj
%A Kamm, Simon
%A Afanasenko, Valentyna
%A Barón, Kevin Muñoz
%A Kallfass, Ingmar
%B 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France, 23-27 August 2021
%D 2021
%K 2021ias ias
%P 423-428
%R 10.1109/CASE49439.2021.9551614
%T Non-Destructive Failure Analysis of Power Devices via Time- Domain Reflectometry
%U https://www.ias.uni-stuttgart.de/dokumente/publikationen/2021_Non-Destructive_Failure_Analysis_of_Power_Devices_via_Time-_Domain_Reflectometry.pdf
%X In power electronic applications, transistors are a vital component. They are, however, susceptible to failures due to degradation of the interconnections and the chip itself. This paper presents a non-destructive approach for failure detection and location in power electronic devices using time-domain reflectometry. The proposed measurement and data generation method is applied to a silicon-carbide power transistor where several characteristics (R, L, C, open, short) and the location of the failure is simulated and characterized. Moreover, the method is also used to find the intrinsic properties of the transistor such as parasitic inductance and capacitance. The data generated is mapped to physical equations, however, the reflected signal of the time-domain reflectometry can be noisy due to multiple discontinuities in the transmission path. Therefore, the simulation and measurement data can be used to train hybrid machine learning models for parameter extraction which automates the failure analysis in Industry4.0 processes to ensure a smart and reliable manufacturing process.
@conference{sharma2021nondestructive,
abstract = {In power electronic applications, transistors are a vital component. They are, however, susceptible to failures due to degradation of the interconnections and the chip itself. This paper presents a non-destructive approach for failure detection and location in power electronic devices using time-domain reflectometry. The proposed measurement and data generation method is applied to a silicon-carbide power transistor where several characteristics (R, L, C, open, short) and the location of the failure is simulated and characterized. Moreover, the method is also used to find the intrinsic properties of the transistor such as parasitic inductance and capacitance. The data generated is mapped to physical equations, however, the reflected signal of the time-domain reflectometry can be noisy due to multiple discontinuities in the transmission path. Therefore, the simulation and measurement data can be used to train hybrid machine learning models for parameter extraction which automates the failure analysis in Industry4.0 processes to ensure a smart and reliable manufacturing process.},
added-at = {2021-11-03T15:10:49.000+0100},
author = {Sharma, Kanuj and Kamm, Simon and Afanasenko, Valentyna and Barón, Kevin Muñoz and Kallfass, Ingmar},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2bf02adb89d1ebe1e0638645a6de30675/taylansngerli},
booktitle = {2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France, 23-27 August 2021},
doi = {10.1109/CASE49439.2021.9551614},
interhash = {1f251d919dd158f6f40fb71085d74817},
intrahash = {bf02adb89d1ebe1e0638645a6de30675},
keywords = {2021ias ias},
pages = {423-428},
timestamp = {2021-11-04T10:36:37.000+0100},
title = {Non-Destructive Failure Analysis of Power Devices via Time- Domain Reflectometry},
url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2021_Non-Destructive_Failure_Analysis_of_Power_Devices_via_Time-_Domain_Reflectometry.pdf},
year = 2021
}