Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers

• URL: http://arxiv.org/abs/2310.10705v4
• Date: Wed, 20 Mar 2024 15:26:55 GMT
• Title: Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers
• Authors: Kamal Taha,
• Abstract summary: This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. An innovative taxonomy of methodologies that we present provides a detailed classification of algorithms into more refined categories and techniques.
• Score: 0.8702432681310399
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of research demonstrating the effectiveness of ML in wafer defect identification, there is a noticeable absence of comprehensive reviews on this subject. This survey attempts to fill this void by amalgamating available literature and providing an in-depth analysis of the advantages, limitations, and potential applications of various ML classification algorithms in the realm of wafer defect detection. An innovative taxonomy of methodologies that we present provides a detailed classification of algorithms into more refined categories and techniques. This taxonomy follows a three-tier structure, starting from broad methodology categories and ending with specific techniques. It aids researchers in comprehending the complex relationships between different algorithms and their techniques. We employ a rigorous Observational and experimental evaluation to rank these varying techniques. For the Observational evaluation, we assess techniques based on a set of four criteria. The experimental evaluation ranks the algorithms employing the same techniques, sub-categories, and categories. Also the paper illuminates the future prospects of ML classification techniques for wafer defect identification, underscoring potential advancements and opportunities for further research in this field

Related papers

• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey [0.8702432681310399]
  The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques.
  arXiv  Detail & Related papers  (2024-02-17T15:00:45Z)
• Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey [2.6319554262325924]
  This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization.
  arXiv  Detail & Related papers  (2024-02-01T23:51:29Z)
• Text Classification: A Review, Empirical, and Experimental Evaluation [2.341806147715478]
  Existing survey papers categorize algorithms for text classification into broad classes. We introduce a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification.
  arXiv  Detail & Related papers  (2024-01-11T08:17:42Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)
• Anomalous Example Detection in Deep Learning: A Survey [98.2295889723002]
  This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for Deep Learning applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
  arXiv  Detail & Related papers  (2020-03-16T02:47:23Z)
• Imbalanced classification: a paradigm-based review [21.578692329486643]
  Multiple resampling techniques have been proposed to address the class imbalance issues. There is no general guidance on when to use each technique. We provide a paradigm-based review of the common resampling techniques for binary classification under imbalanced class sizes.
  arXiv  Detail & Related papers  (2020-02-11T18:34:48Z)

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.