Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Non-Destructive Testing Techniques · Material Properties and Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
Show source-provided references
1. Climate-Protective Packaging: Using Basic Physics to Solve Climatic Challenges for Electronics in Demanding Applications10.1109/MIE.2014.2330912 ↗
2. A system engineering approach using FMEA and bayesian network for risk analysis-a case study
3. Water film formation on the PCBA surface and failure occurrence in electronics
4. Investigation of critical factors effect to predict leakage current and time to failure due to ECM on PCB under humidity10.1016/j.microrel.2021.114418 ↗
5. Bâzu · 2011
6. Reliability assessment of power conditioner considering maintenance in a PEM fuel cell system10.1016/j.microrel.2018.07.085 ↗
7. Relative effect of solder flux chemistry on the humidity related failures in electronics10.1108/SSMT-11-2014-0022 ↗
8. Corrosion in Electronics at Device Level10.1149/1.3321952 ↗
9. Failure modes and failure analysis
10. Corrosion Reliability of Electronic Systems
11. An Overview of Artificial Intelligence Applications for Power Electronics10.1109/TPEL.2020.3024914 ↗
12. Intelligent Factory Agents with Predictive Analytics for Asset Management
13. Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool10.1016/j.jpedsurg.2020.11.008 ↗
14. Failure prediction using machine learning in a virtualised HPC system and application10.1007/s10586-019-02917-1 ↗
15. H. Agrawal, J. Chandiwala, S. Agrawal, Y. Goyal, Heart Failure Prediction using Machine Learning with Exploratory Data Analysis, in: 2021 Int. Conf. Intell. Technol. CONIT 2021, IEEE, 2021: pp. 1–6. https://doi.org/10.1109/CONIT51480.2021.9498561.10.1109/CONIT51480.2021.9498561 ↗
16. Machine learning for survival analysis: A survey10.1145/3214306 ↗
17. An Overview of Machine Learning
18. Mohammed · 2016
19. I. Hammad, K. El-Sankary, J. Gu, A comparative study on machine learning algorithms for the control of a wall following robot, in: IEEE Int. Conf. Robot. Biomimetics, ROBIO 2019, IEEE, 2019: pp. 2995–3000. https://doi.org/10.1109/ROBIO49542.2019.8961836.10.1109/ROBIO49542.2019.8961836 ↗
20. Types of Machine Learning Algorithms
21. Machine Learning: Algorithms, Real-World Applications and Research Directions10.1007/s42979-021-00592-x ↗
22. Supervised Machine Learning: A Brief Primer10.1016/j.beth.2020.05.002 ↗
23. H.K. Gianey, R. Choudhary, Comprehensive Review On Supervised Machine Learning Algorithms, in: Proc. - 2017 Int. Conf. Mach. Learn. Data Sci. MLDS 2017, IEEE, 2018: pp. 38–43. https://doi.org/10.1109/MLDS.2017.11.10.1109/MLDS.2017.11 ↗
24. A. Singh, N. Thakur, A. Sharma, A review of supervised machine learning algorithms, in: Proc. 10th INDIACom; 2016 3rd Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2016, 2016: pp. 1310–1315. 〈https://ieeexplore.ieee.org/abstract/document/7724478〉 (accessed January 6, 2022).
25. Supervised Machine Learning Algorithms: Classification and Comparison10.14445/22312803/IJCTT-V48P126 ↗
26. A review of supervised machine learning algorithms and their applications to ecological data10.1016/j.ecolmodel.2012.03.001 ↗
27. Supervised Classification Algorithms in Machine Learning: A Survey and Review10.1007/978-981-13-7403-6_11 ↗
28. Wastewater plant reliability prediction using the machine learning classification algorithms10.3390/sym13081518 ↗
29. Software reliability prediction using machine learning techniques10.1007/978-981-10-0448-3_12 ↗
30. D. Singh, M. Kumar, K.V. Arya, S. Kumar, Aircraft Engine Reliability Analysis using Machine Learning Algorithms, in: 2020 IEEE 15th Int. Conf. Ind. Inf. Syst. ICIIS 2020 - Proc., IEEE, 2020: pp. 443–448. https://doi.org/10.1109/ICIIS51140.2020.9342675.10.1109/ICIIS51140.2020.9342675 ↗
31. Kumari · Eff. Predict. COVID-19 Using Supervised Mach. Learn. Ensemble Model. · 2022 · 537
32. Regression Analysis of COVID-19 using Machine Learning Algorithms
33. Prediction of Player Price in IPL Auction Using Machine Learning Regression Algorithms
34. Hannes Prosthesis Control Based on Regression Machine Learning Algorithms
35. Komarudin, Visiting time prediction using machine learning regression algorithm
36. Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry10.1007/s00500-020-05348-y ↗
37. Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning10.1016/j.corsci.2020.108697 ↗
38. A framework for inspection of dies attachment on PCB utilizing machine learning techniques
39. Feature-learning-based printed circuit board inspection via speeded-up robust features and random forest10.3390/app8060932 ↗
40. Machine learning framework for predicting reliability of solder joints10.1108/SSMT-04-2019-0013 ↗
41. An Implementation of Health Prediction in SMT Solder Joint via Machine Learning
42. Exploring the Data-Driven Modeling Methods for Electrochemical Migration Failure of Printed Circuit Board
43. A review on machine learning algorithms to predict daylighting inside buildings10.1016/j.solener.2020.03.104 ↗
44. Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation
45. Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics10.3390/molecules24152811 ↗
46. Grid search-based hyperparameter tuning and classification of microarray cancer data
47. Electrochemical migration of tin in electronics and microstructure of the dendrites10.1016/j.corsci.2011.01.009 ↗
48. Effect of pulsed voltage on electrochemical migration of tin in electronics10.1007/s10854-015-3454-9 ↗
49. On the electrochemical migration mechanism of tin in electronics10.1016/j.corsci.2011.06.015 ↗
50. Effects of Moisture and Temperature on Membrane Switches in Laptop Keyboards10.1109/TDMR.2018.2866776 ↗
51. Investigation of moisture uptake into printed circuit board laminate and solder mask materials10.1007/s10854-016-6292-5 ↗
52. Corrosion in Electronics
53. Effect of elevated temperature on PCB responses and solder interconnect reliability under vibration loading
54. Parametric Study of Solder Flux Hygroscopicity: Impact of Weak Organic Acids on Water Layer Formation and Corrosion of Electronics10.1007/s11664-018-6311-9 ↗
55. Residue-Assisted Water Layer Build-Up Under Transient Climatic Conditions and Failure Occurrences in Electronics10.1109/TCPMT.2020.3005933 ↗
56. Song · 2012 · 2012 · 1643
57. The effects of environmental contaminants on electronic power supplies
58. Effect of solder mask surface chemistry and morphology on the water layer formation under humid conditions10.1109/TCPMT.2018.2792047 ↗
59. Hörber · 2019 · 1807
60. Transformation of reflow solder flux residue under humid conditions10.1016/j.microrel.2021.114195 ↗
61. Analysis of surface insulation resistance related failures in electronics by circuit simulation10.1108/CW-09-2016-0040 ↗
62. Reliability of Printed Circuit Boards Processed Using No-Clean Flux Technology in Temperature–Humidity–Bias Conditions10.1109/TDMR.2008.922908 ↗
63. Fundamentals of Machine Learning
64. Application of machine learning in rheumatic disease research10.3904/kjim.2018.349 ↗
65. Prescriptive analytics: Literature review and research challenges
66. Python-based scikit-learn machine learning models for thermal and electrical performance prediction of high-capacity lithium-ion battery
67. On hyperparameter optimization of machine learning algorithms: Theory and practice10.1016/j.neucom.2020.07.061 ↗
68. H.J.P. Weerts, A.C. Mueller, J. Vanschoren, Importance of Tuning Hyperparameters of Machine Learning Algorithms, Arxiv.Org. (2020). 〈https://arxiv.org/abs/2007.07588〉 (accessed January 9, 2022).
69. Machine learning modeling of time-dependent corrosion rates of carbon steel in presence of corrosion inhibitors10.1016/j.corsci.2021.109904 ↗
70. Scikit-learn: Machine learning in Python
71. Efficient k-nearest neighbor search based on clustering and adaptive k values10.1016/j.patcog.2021.108356 ↗
72. Random Forest10.1007/978-1-4842-3564-5_5 ↗
73. Mach. Learn. Found., Springer International Publishing
74. Support-vector networks
75. On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications10.1016/j.chemolab.2018.11.011 ↗
76. Performance comparison of CNN, QNN and BNN deep neural networks for real-time object detection using ZYNQ FPGA node10.1016/j.mejo.2021.105319 ↗
77. Artificial Neural Network
78. Machine Learning Principles
79. An empirical study of software reliability prediction using machine learning techniques10.1007/s13198-012-0123-8 ↗
80. Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm10.1016/j.aei.2020.101126 ↗
81. A regression-based approach for estimating preliminary dimensioning of reinforced concrete cantilever retaining walls10.1007/s00158-019-02470-w ↗
82. Solder Flux Residues and Humidity-Related Failures in Electronics: Relative Effects of Weak Organic Acids Used in No-Clean Flux Systems10.1007/s11664-014-3609-0 ↗
83. No-Clean Solder Flux Chemistry and Temperature Effects on Humidity-Related Reliability of Electronics10.1007/s11664-018-06862-4 ↗
84. Decomposition of no-clean solder flux systems and their effects on the corrosion reliability of electronics10.1007/s10854-015-3712-x ↗
85. Thermal decomposition of binary mixtures of organic activators used in no-clean fluxes and impact on PCBA corrosion reliability10.1108/SSMT-05-2019-0020 ↗
86. Amino Acids as Activators for Wave Solder Flux Systems: Investigation of Solderability and Humidity Effects10.1109/TCPMT.2022.3167389 ↗
87. Cross-Validation, Encycl. Bioinforma. Comput
88. Error estimation based on variance analysis of k-fold cross-validation10.1016/j.patcog.2017.03.025 ↗
89. Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results
90. Time to Failure Prediction on a Printed Circuit Board Surface Under Humidity Using Probabilistic Analysis
91. Effective Rate of Minority Class Over-Sampling for Maximizing the Imbalanced Dataset Model Performance10.1007/978-981-16-6285-0_2 ↗
92. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation
93. Visualising data distributions with kernel density estimation and reduced chi-squared statistic10.1016/j.gsf.2017.05.002 ↗
94. Humidity-related failures in electronics: effect of binary mixtures of weak organic acid activators10.1007/s10854-018-9896-0 ↗
95. V. Verdingovas, Climatic Reliability of Electronics: Early Prediction and Control of Contamination and humidity effects, Technical University of Denmark (DTU), 2015.
96. Impact of Process-Related Flux Contamination on the Electronics Reliability Issues Under Detrimental Climatic Conditions
97. Electrochemical Reliability as a Function of Component Standoff
98. Characterizing the weak organic acids used in low solids fluxes10.1007/s11664-999-0171-2 ↗
99. V. Verdingovas, M.S. Jellesen, R. Rizzo, H. Conseil, R. Ambat, Impact of hygroscopicity and composition of solder flux residue on the reliability of PCBA under corrosive conditions, in: 2013. 〈https://findit.dtu.dk/en/catalog/537f109374bed2fd2100e8ee〉 (accessed May 24, 2022).
100. Impact of electric field at rough copper lines on failure time due to electrochemical migration in PCBs10.1016/j.microrel.2021.114035 ↗