An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns

TL;DR

This paper presents a non-destructive fault detection and root cause analysis method for ITO electrodes using S-parameter patterns combined with deep learning, achieving high accuracy and noise robustness in defect diagnosis.

Contribution

It introduces a novel approach integrating S-parameter pattern analysis with deep learning models for fault diagnosis and root cause analysis of ITO electrodes.

Findings

Deep learning models accurately identify defect types and severity.

Combining multiple S-parameter channels improves noise robustness.

t-SNE visualization confirms effective pattern separation.

Abstract

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.