  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 diagnosis and root cause analysis 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 diagnosis methods have limitations in
determining the root causes of the defects, often requiring destructive
evaluations and secondary material characterization techniques. In this study,
a fault diagnosis method with root cause analysis is proposed using scattering
parameter (S-parameter) patterns, offering early detection, high diagnostic
accuracy, and noise robustness. A comprehensive S-parameter pattern database is
obtained according to various defect states of the ITO electrodes. 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 of the S-parameter
patterns.
