Machine Learning Reinforced Acoustic Signal Analysis for Enhancing Non-Destructive Defect Localization and Reliable Identification

Abstract

The paper presents the approach of enhancing time-domain signal analysis using machine learning techniques for analyzing acoustic echo signals and the subsequent derivation of condition-related class assignments for failure analysis. The examples provided here include two types of flip-chips with defects intentionally induced by thermal stressing. Besides investigating the general applicability and the benefit of the approach the current study also investigated the applicability of different deep learning model-architectures and compared their performances, accuracies, and robustness with respect to external impacts such as noise, jitter or physical defocusing. For independent verification selected defects which have either been identified by an experienced operator or the ML algorithm or both, have been further analyzed and validated by FIB/SEM cross sectional analysis.