Using Predictive Analytics Approaches to Investigate Climatic Reliability and Humidity Robustness issues of PCBA under Different Conditions

Abstract

Nowadays, climatic reliability and humidity robustness of electronic devices has become significant issue for both consumer and industrial electronics due to various reasons. One reason for this increased problem is the widespread use of electronics in many locations. The climatic reliability of electronics is attributed to the interaction of external climatic conditions and printed circuit board assembly (PCBA) characteristics as the main part of each electronic device, which compromise the performance of electronics due to the electrochemical failure process. In order to improve the reliability of electronics, requires a detailed understanding of the synergetic and interaction effects of various controllable factors, such as humidity, temperature, pitch distance, voltage, contamination types, and contamination levels. Moreover, it is crucial for reliability assessment to understand the relative importance of factors and their levels to take remedial action at an earlier stage based on selecting the best PCBA material, soldering process, and optimizing the design in desired tasks for particular applications and climatic conditions. This study presents the most suitable approach and prediction model based on the input datasets by using a combination of statistical analysis, probabilistic approaches, and machine learning algorithms to predict leakage current (LC), time to failure (TTF), failure state, and highly risky conditions, that could provide a better perspective of PCBA reliability and helps to reduce electronic waste due to failure.