  With the increasing operating frequencies and clock speeds in processors,
interconnects affect both the reliability and performance of entire electronic
systems. Fault detection and diagnosis of the interconnects are crucial for
prognostics and health management (PHM) of electronics. However, traditional
approaches using electrical signals as prognostic factors often face challenges
in distinguishing defect root causes, necessitating additional destructive
evaluations, and are prone to noise interference, leading to potential false
alarms. To address these limitations, this paper introduces a novel approach
for non-destructive detection and diagnosis of defects in Cu interconnects,
offering early detection, enhanced diagnostic accuracy, and noise resilience.
Our approach uniquely analyzes both the root cause and severity of interconnect
defects by leveraging graph patterns of reflection coefficient, a technique
distinct from traditional time series signal analysis. We experimentally
demonstrate that the graph patterns possess the capability for fault diagnosis
and serve as effective input data for learning algorithms. Additionally, we
introduce a novel severity rating ensemble learning (SREL) approach, which
significantly enhances diagnostic accuracy and noise robustness. Experimental
results demonstrate that the proposed method outperforms conventional machine
learning methods and multi-class convolutional neural networks (CNN), achieving
a maximum accuracy of 99.3%, especially under elevated noise levels.
