Using Machine Learning Algorithms to Predict Failure on the PCB Surface under Corrosive Conditions

Sajjad Bahrebar*, Sajad Homayoun, Rajan Ambat

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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 languageEnglish
Article number110500
JournalCorrosion Science
Volume206
Number of pages21
ISSN0010-938X
DOIs
Publication statusPublished - 2022

Keywords

  • Machine learning algorithm
  • Classification
  • Regression
  • Predictive analytics
  • PCB failure
  • Leakage current

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