Non-destructive fault diagnosis of electronic interconnects by learning signal patterns of reflection coefficient in the frequency domain

• Published in: Microelectronics and reliability 2024-11, Vol.162, p.115518, Article 115518
• Main Authors: Kang, Tae Yeob, Lee, Haebom, Suh, Sungho
• Format: Article
• Language: English
• Subjects: Fault diagnosis; Interconnects; Non-destructive testing; Reflection coefficient; Signal pattern
• ISSN: 0026-2714
• DOI: 10.1016/j.microrel.2024.115518

Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. Traditional methods, which rely on electronic signals as prognostic factors, often struggle to accurately identify the root causes of defects without resorting to destructive testing. Furthermore, these methods are vulnerable to noise interference, which can result in false alarms. To address these limitations, in this paper, we propose a novel, non-destructive approach for early fault detection and accurate diagnosis of interconnect defects, with improved noise resilience. Our approach uniquely utilizes the signal patterns of the reflection coefficient across a range of frequencies, enabling both root cause identification and severity assessment. This approach departs from conventional time-series analysis and effectively transforms the signal data into a format suitable for advanced learning algorithms. Additionally, we introduce a novel severity rating ensemble learning (SREL) approach, which enhances diagnostic accuracy and robustness in noisy environments. Experimental results demonstrate that the proposed method is effective for fault detection and diagnosis and has the potential to extend to real-world industrial applications. •Root cause analysis is challenging with previous signal-based methods.•We obtain frequency-domain reflection coefficients of the interconnects with defects.•Reflection coefficient shows distinguishing patterns according to the defect states.•A non-destructive fault diagnosis method is proposed by learning signal patterns.•Severity rating ensemble learning improves diagnostic accuracy in noisy conditions.