A Machine Learning Model for the Detection of Solder Voids with Adjacent Sensors

被引：0

作者：

Jahn, Nils ^{[1
]}

Sina, Patrick ^{[1
]}

Pfost, Martin ^{[1
]}

机构：

[1] TU Dortmund Univ, Chair Energy Convers, Dortmund, Germany

来源：

2023 29TH INTERNATIONAL WORKSHOP ON THERMAL INVESTIGATIONS OF ICS AND SYSTEMS, THERMINIC | 2023年

关键词：

prognostics and health management; machine learning; classi.cation task; reliability; solder;

D O I：

10.1109/THERMINIC60375.2023.10325910

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

In this work, a machine-learning model is trained for the detection of voids in the solder layer underneath a power semiconductor based on the readings of temperature sensors adjacent to the device. For this, multiple con.gurations of a nearest-neighbor classi.cation model are trained and tested with data obtained from the measurement and simulation of a test arrangement with arti.cial faults. A grid search with cross-validation is used to propose an optimized model for the given task, based on di.erent scoring metrics. Additionally, the success of the classi.cation is evaluated in the context of the application case.

引用

页数：6

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