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

• URL: http://arxiv.org/abs/2308.11639v2
• Date: Mon, 10 Jun 2024 16:29:37 GMT
• Title: An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns
• Authors: Tae Yeob Kang, Haebom Lee, Sungho Suh,
• Abstract summary: Indium tin oxide (ITO) electrodes play a crucial role in optoelectronics, such as displays, sensors, and solar cells. Traditional visual inspection is challenging with transparent ITO electrodes. Deep learning approaches, including multilayer perceptron (MLP), convolutional neural network (CNN), and transformer, are then used to simultaneously analyze the cause and severity of defects.
• Score: 1.8843687952462742
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Unsupervised Anomaly Detection Using Diffusion Trend Analysis [48.19821513256158]
  We propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation. The proposed method is validated on an open dataset for industrial anomaly detection.
  arXiv  Detail & Related papers  (2024-07-12T01:50:07Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Unified Uncertainty Estimation for Cognitive Diagnosis Models [70.46998436898205]
  We propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. We decompose the uncertainty of diagnostic parameters into data aspect and model aspect. Our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.
  arXiv  Detail & Related papers  (2024-03-09T13:48:20Z)
• Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks [0.0]
  Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems. We propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS.
  arXiv  Detail & Related papers  (2023-07-17T13:21:02Z)
• Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects [1.4502611532302039]
  This paper introduces a novel approach for non-destructive detection and diagnosis of defects in Cu interconnects. Our approach uniquely analyzes both the root cause and severity of interconnect defects by leveraging graph patterns of coefficient. We experimentally demonstrate that the graph patterns possess the capability for fault diagnosis and serve as effective input data for learning algorithms.
  arXiv  Detail & Related papers  (2023-04-20T10:51:21Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Deep Scattering Spectrum germaneness to Fault Detection and Diagnosis for Component-level Prognostics and Health Management (PHM) [0.0]
  This work focuses on the study of the Deep Scattering Spectrum (DSS)'s relevance to fault detection and daignosis for mechanical components of industrail robots. We used multiple industrial robots and distinct mechanical faults to build an approach for classifying the faults. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis.
  arXiv  Detail & Related papers  (2022-10-18T13:25:02Z)
• Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations [114.17826109037048]
  Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning. theoretical aspects, e.g., identifiability and properties of statistical estimation are still obscure. This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory.
  arXiv  Detail & Related papers  (2022-10-12T06:46:38Z)
• Post-hoc Interpretability based Parameter Selection for Data Oriented Nuclear Reactor Accident Diagnosis System [0.0]
  This study proposes a method of choosing thermal hydraulics parameters of a nuclear power plant, using the theory of post-hoc interpretability theory in deep learning. The TRES-CNN based diagnostic model successfully predicts the position and size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time consumption while training the model compared the process using total 38 parameters.
  arXiv  Detail & Related papers  (2022-08-03T01:53:11Z)
• Identification of different types of high-frequency defects in superconducting qubits [0.0]
  Parasitic two-level-system (TLS) defects are one of the major factors limiting the coherence times of superconducting qubits. Here we present an experimental method of TLS defect spectroscopy using a strong qubit drive.
  arXiv  Detail & Related papers  (2021-12-10T08:55:00Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.