In the field of optoelectronics, indium tin oxide (ITO) electrodes play a crucial role in various applications, such as displays, sensors, and solar cells. Effective fault detection and diagnosis of the ITO electrodes are essential to ensure the performance and reliability of the devices. However, traditional visual inspection is challenging with transparent ITO electrodes, and existing fault detection methods have limitations in determining the root causes of the defects, often requiring destructive evaluations. In this study, an in situ fault diagnosis method is proposed using scattering parameter (S-parameter) signal processing, offering early detection, high diagnostic accuracy, noise robustness, and root cause analysis. A comprehensive S-parameter pattern database is obtained according to defect states. Deep learning (DL) approaches, including multilayer perceptron (MLP), convolutional neural network (CNN), and transformer, are then used to simultaneously analyze the cause and severity of defects. Notably, it is demonstrated that the diagnostic performance under additive noise levels can be significantly enhanced by combining different channels of the S-parameters as input to the learning algorithms, as confirmed through the t-distributed stochastic neighbor embedding (t-SNE) dimension reduction visualization.

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.