In situ Fault Diagnosis of Indium Tin Oxide Electrodes by Processing
  S-Parameter Patterns
Tae Yeob Kang Haebom Lee Sungho Suh
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.