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.

使用散射参数信号处理提供了早期检测、高诊断准确度、噪音鲁棒性和根因分析等优势，并证明通过将S参数的不同通道组合为学习算法的输入，可以显著提高加性噪声水平下的诊断性能。

通过处理S参数模式对氧化铟锡电极进行原位故障诊断