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

• Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging [70.66500060987312]
  Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules. This work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions.
  arXiv  Detail & Related papers  (2024-09-11T11:12:26Z)
• AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems [44.99833362998488]
  intermittent nature of photovoltaic (PV) solar energy leads to power losses of 10-70% and an average energy production decrease of 25%. Current fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm.
  arXiv  Detail & Related papers  (2024-08-19T23:52:06Z)
• Enhanced Fault Detection and Cause Identification Using Integrated Attention Mechanism [0.3749861135832073]
  This study introduces a novel methodology for fault detection and cause identification within the Tennessee Eastman Process (TEP) by integrating a Bidirectional Long Short-Term Memory (BiLSTM) neural network with an Integrated Attention Mechanism (IAM) The IAM combines the strengths of scaled dot product attention, residual attention, and dynamic attention to capture intricate patterns and dependencies crucial for TEP fault detection. The BiLSTM network processes these features bidirectionally to capture long-range dependencies, and the IAM further refines the output, leading to improved fault detection results.
  arXiv  Detail & Related papers  (2024-07-31T12:01:57Z)
• 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)
• Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning [46.75992018094998]
  This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. This paper presents the Multilevel Collaborative Attention Knowledge (MLCAK) method.
  arXiv  Detail & Related papers  (2024-05-22T06:10:54Z)
• 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)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain [1.8843687952462742]
  We propose a novel, non-destructive approach for early fault detection and accurate diagnosis of interconnect defects. Our approach utilizes the signal patterns of the coefficient reflection across a range of frequencies, enabling both root cause identification and severity assessment. Experimental results demonstrate that the proposed method is effective for fault detection and diagnosis and has the potential to extend to real-world industrial applications.
  arXiv  Detail & Related papers  (2023-04-20T10:51:21Z)
• 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)
• 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.