Abstract
A printed circuit board (PCB) surface can fail by corrosion due to
various environmental factors. This paper focuses on machine learning
(ML) techniques to build predictive models to forecast PCB surface
failure due to electrochemical migration (ECM) and leakage current (LC)
levels under corrosive conditions containing the combination of six
critical factors. The modeling methodology in this paper used common
supervised ML algorithms by accomplishing significant evaluation metrics
to show the performance of each algorithm. The conclusion of this study
presents that ML algorithms can create predictive models to forecast
PCB failures and estimate LC values effectively and quickly
Original language | English |
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Article number | 110500 |
Journal | Corrosion Science |
Volume | 206 |
Number of pages | 21 |
ISSN | 0010-938X |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Machine learning algorithm
- Classification
- Regression
- Predictive analytics
- PCB failure
- Leakage current