Automated Assessment of Die-Attach Polymer Performance via Non-Destructive Infrared Thermography and Machine Learning-Driven Correlation

**Abstract:** Current die-attach polymer (DAP) reliability assessment relies heavily on destructive testing methods like thermal cycling and humidity tests, leading to significant time and resource costs. This paper proposes a novel, non-destructive methodology for evaluating DAP performance utilizing infrared thermography (IRT) coupled with machine learning (ML) algorithms. We leverage established principles of heat transfer and failure mechanisms in microelectronics, combining quantitative IRT data with algorithmic analysis to predict DAP degradation and failure with high accuracy. This approach offers a significantly faster, cost-effective, and less wasteful alternative to traditional methods, accelerating development cycles and improving quality control within the semiconductor industry. Preliminary results demonstrate a dramatic improvement in inspection speed and reliability prediction over current manual visual inspection techniques, representing a potential paradigm shift in DAP reliability assessment.