On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator
Abstract The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today,...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2016
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| Online Access: | https://eprints.nottingham.ac.uk/35881/ |
| _version_ | 1848795181484081152 |
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| author | Arjmand, Elaheh |
| author_facet | Arjmand, Elaheh |
| author_sort | Arjmand, Elaheh |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Abstract
The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today, wire bond lift-off and heel cracking are often considered the most lifetime limiting factors of power electronic modules as a result of cyclic thermomechanical stresses. Therefore, it is important for power electronic packaging manufacturers to address this issue at the design stage and on the manufacturing line. Techniques for the non-destructive, real-time evaluation and control of wire bond quality have been proposed to detect defects in manufacture and predict reliability prior to in-service exposure. This approach has the potential to improve the accuracy of lifetime prediction for the manufactured product.
In this thesis, a non-destructive technique for detecting bond quality by the application of a semi-supervised classification algorithm to process signals obtained from an ultrasonic generator is presented. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area as measured by X-ray tomography. Samples classified during bonding were subjected to both passive and active cycling and the distribution of bond life amongst the different classes analysed. It is demonstrated that the as-bonded quality classification is closely correlated with cycling life and can therefore be used as a non-destructive tool for monitoring bond quality and predicting useful service life. |
| first_indexed | 2025-11-14T19:28:00Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-35881 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:28:00Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-358812025-02-28T11:50:30Z https://eprints.nottingham.ac.uk/35881/ On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator Arjmand, Elaheh Abstract The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today, wire bond lift-off and heel cracking are often considered the most lifetime limiting factors of power electronic modules as a result of cyclic thermomechanical stresses. Therefore, it is important for power electronic packaging manufacturers to address this issue at the design stage and on the manufacturing line. Techniques for the non-destructive, real-time evaluation and control of wire bond quality have been proposed to detect defects in manufacture and predict reliability prior to in-service exposure. This approach has the potential to improve the accuracy of lifetime prediction for the manufactured product. In this thesis, a non-destructive technique for detecting bond quality by the application of a semi-supervised classification algorithm to process signals obtained from an ultrasonic generator is presented. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area as measured by X-ray tomography. Samples classified during bonding were subjected to both passive and active cycling and the distribution of bond life amongst the different classes analysed. It is demonstrated that the as-bonded quality classification is closely correlated with cycling life and can therefore be used as a non-destructive tool for monitoring bond quality and predicting useful service life. 2016-12-13 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/35881/1/Elaheh-Arjmand-thesis.pdf Arjmand, Elaheh (2016) On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator. PhD thesis, University of Nottingham. Heavy wire bonding power electronic reliability ultrasonic signal X-ray tomography |
| spellingShingle | Heavy wire bonding power electronic reliability ultrasonic signal X-ray tomography Arjmand, Elaheh On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator |
| title | On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator |
| title_full | On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator |
| title_fullStr | On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator |
| title_full_unstemmed | On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator |
| title_short | On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator |
| title_sort | on-line quality monitoring and lifetime prediction of thick al wire bonds using signals obtained from ultrasonic generator |
| topic | Heavy wire bonding power electronic reliability ultrasonic signal X-ray tomography |
| url | https://eprints.nottingham.ac.uk/35881/ |