☆ 4.7 Article

Wavelet integrated attention network with multi-resolution frequency learning for mixed-type wafer defect recognition

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

Related references

Note: Only part of the references are listed.
Article Chemistry, Multidisciplinary

Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps

Jaegyeong Cha et al.

Summary: A proposed improved U-Net model utilizing a residual attention block for segmenting mixed defects in semiconductors has been shown to outperform existing studies in experimental results.

APPLIED SCIENCES-BASEL (2022)

Add to Collection
Article Computer Science, Interdisciplinary Applications

Semi-automatic wafer map pattern classification with convolutional neural networks

Suhee Yoon et al.

Summary: This paper proposes a semi-automatic wafer map pattern classification method that selectively utilizes a CNN based on its predictive uncertainty, achieving high accuracy while involving a process engineer.

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

Add to Collection
Article Computer Science, Interdisciplinary Applications

Mixup-based classification of mixed-type defect patterns in wafer bin maps

Wooksoo Shin et al.

Summary: This study proposes a method to use WBM data with only a single defect to train convolutional neural networks (CNNs) for classifying mixed-type defects. The method generates mixed-type defect patterns on the fly for model training, improving the classification performance compared to previous methods. The effectiveness and applicability of the proposed method are demonstrated through experiments on a real-world WBM benchmark.

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

Add to Collection
Article Automation & Control Systems

Multiple Granularities Generative Adversarial Network for Recognition of Wafer Map Defects

Jianbo Yu et al.

Summary: Wafer map defect recognition (WMDR) is crucial in the integrated circuit manufacturing system. Multi-granularity GAN (MGGAN) is proposed to enhance the performance in recognizing wafer map defects, addressing issues like class imbalance and lack of labeled data. Experimental results show that MGGAN has significantly improved image enhancement and generation compared to traditional deep neural networks.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Add to Collection
Article Automation & Control Systems

An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network

Deniz Korkmaz et al.

Summary: Photovoltaic power generation is a remarkable energy type that provides clean and sustainable energy. This study proposes an efficient PV fault detection method using thermographic images to classify different types of PV module anomalies. The method utilizes a multi-scale convolutional neural network with three branches based on transfer learning strategy, and achieves higher classification accuracy and robustness compared to other deep learning methods and existing studies.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2022)

Add to Collection
Article Engineering, Manufacturing

Detection and Recognition of Mixed-Type Defect Patterns in Wafer Bin Maps via Tensor Voting

Rui Wang et al.

Summary: This study proposes a novel method for the detection and recognition of mixed-type defect patterns in wafer bin maps. By separating mixed-type patterns into clusters using tensor voting and extracting region and curve patterns based on the structural saliency information of the voting process, our method is robust and flexible in dealing with complex defect patterns.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2022)

Add to Collection
Article Computer Science, Interdisciplinary Applications

WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects

Subhrajit Nag et al.

Summary: With the increasing integration density and design intricacy of semiconductor wafers, the number and complexity of defects in them are also on the rise. This paper presents a novel network called WSCN that performs simultaneous classification and segmentation of both single and mixed-type wafer defects. WSCN uses a shared encoder for training, resulting in a smaller model size and computational cost compared to previous works, and achieves convergence in fewer training epochs.

COMPUTERS IN INDUSTRY (2022)

Add to Collection
Article Engineering, Multidisciplinary

Defect classification on semiconductor wafers using Fisher vector and visual vocabularies coding

Jose L. Gomez-Sirvent et al.

Summary: The study found that using visual vocabularies for defect classification on semiconductor wafers is effective, with high accuracy and computational efficiency demonstrated through experiments.

MEASUREMENT (2022)

Add to Collection
Article Automation & Control Systems

Wafer map defect recognition based on deep transfer learning-based densely connected convolutional network and deep forest

Jianbo Yu et al.

Summary: The study introduces a wafer map defect recognition (WMDR) model based on deep transfer learning and deep forest, which proves to effectively enhance wafer defect recognition performance and outperform other well-known CNNs and classifiers.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Add to Collection
Article Engineering, Manufacturing

A Graph-Theoretic Approach for Spatial Filtering and Its Impact on Mixed-Type Spatial Pattern Recognition in Wafer Bin Maps

Ahmed Aziz Ezzat et al.

Summary: The study focuses on the importance of spatial pattern recognition in statistical quality control in semiconductor manufacturing, with a particular interest in mixed-type spatial pattern recognition. A new graph-theoretic method called adjacency-clustering is proposed for spatial filtering, resulting in significant improvements in both internal and external cluster validation metrics. The method shows larger gains for more complex-shaped patterns.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2021)

Add to Collection
Article Computer Science, Information Systems

Evaluation of the machine learning classifier in wafer defects classification

Jessnor Arif Mat Jizat et al.

Summary: This paper evaluates machine learning classifiers for wafer defect detection application in order to establish the best classifier. Different algorithms and parameters were tested with key metrics, using 855 images for training and validation.

ICT EXPRESS (2021)

Add to Collection
Article Engineering, Mechanical

AI classification of wafer map defect patterns by using dual-channel convolutional neural network

Shouhong Chen et al.

Summary: A new method based on image-based multi-source and two-channel convolutional neural network for wafer map classification was proposed in this study, effectively recognizing the defect pattern of wafer maps with an overall classification accuracy of 96.4% confirmed through experiments.

ENGINEERING FAILURE ANALYSIS (2021)

Add to Collection
Article Automation & Control Systems

Two-Dimensional Principal Component Analysis-Based Convolutional Autoencoder for Wafer Map Defect Detection

Jianbo Yu et al.

Summary: This article proposes a novel deep neural network, PCACAE, for wafer map defect recognition in semiconductor manufacturing process. Experimental results demonstrate that PCACAE outperforms other well-known convolutional neural networks in WMPR.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification

Qiufu Li et al.

Summary: By integrating discrete wavelet transform (DWT) with convolutional neural networks (CNN), the proposed approach enhances the noise robustness and adversarial robustness, achieving higher accuracy in image classification tasks. Decomposing feature maps using DWT into low-frequency and high-frequency components, the model generates robust high-level features and eliminates data noises, leading to improved performance on ImageNet dataset and adversarial attacks.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

Add to Collection
Article Engineering, Industrial

A generalised uncertain decision tree for defect classification of multiple wafer maps

Byunghoon Kim et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes

Cheng Hao Jin et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2020)

Add to Collection
Article Engineering, Manufacturing

A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes

Muhammad Saqlain et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2020)

Add to Collection
Article Engineering, Electrical & Electronic

A Novel Method Based on Deep Convolutional Neural Networks for Wafer Semiconductor Surface Defect Inspection

Guojun Wen et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

Add to Collection
Article Engineering, Manufacturing

Deformable Convolutional Networks for Efficient Mixed-Type Wafer Defect Pattern Recognition

Junliang Wang et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2020)

Add to Collection
Article Engineering, Manufacturing

Semi-Supervised Multi-Label Learning for Classification of Wafer Bin Maps With Mixed-Type Defect Patterns

Hyuck Lee et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2020)

Add to Collection
Article Engineering, Manufacturing

Memory-Augmented Convolutional Neural Networks With Triplet Loss for Imbalanced Wafer Defect Pattern Classification

Yunseung Hyun et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2020)

Add to Collection
Proceedings Paper Computer Science, Artificial Intelligence

Mixed Pattern Recognition Methodology on Wafer Maps with Pre-trained Convolutional Neural Networks

Yunseon Byun et al.

ICAART: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2 (2020)

Add to Collection
Article Computer Science, Information Systems

A Light-Weighted CNN Model for Wafer Structural Defect Detection

Xiaoyan Chen et al.

IEEE ACCESS (2020)

Add to Collection
Article Engineering, Manufacturing

Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class

Sejune Cheon et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2019)

Add to Collection
Article Engineering, Manufacturing

AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition

Junliang Wang et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2019)

Add to Collection
Article Engineering, Industrial

A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation

Lars Moench et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2018)

Add to Collection
Article Engineering, Industrial

Detection and clustering of mixed-type defect patterns in wafer bin maps

Jinho Kim et al.

IISE TRANSACTIONS (2018)

Add to Collection
Article Engineering, Manufacturing

Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks

Kiryong Kyeong et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2018)

Add to Collection
Article Engineering, Manufacturing

Decision Tree Ensemble-Based Wafer Map Failure Pattern Recognition Based on Radon Transform-Based Features

Minghao Piao et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2018)

Add to Collection
Article Automation & Control Systems

Simplified Subspaced Regression Network for Identification of Defect Patterns in Semiconductor Wafer Maps

Fatima Adly et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2015)

Add to Collection
Article Engineering, Electrical & Electronic

Effects of wafer noise on the detection of 20-nm defects using optical volumetric inspection

Bryan M. Barnes et al.

JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS (2015)

Add to Collection
Article Automation & Control Systems

Defect cluster recognition system for fabricated semiconductor wafers

Melanie Po-Leen Ooi et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2013)

Add to Collection
Article Engineering, Industrial

Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers

Gyunghyun Choi et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2012)

Add to Collection
Article Engineering, Manufacturing

Detection of Spatial Defect Patterns Generated in Semiconductor Fabrication Processes

Tao Yuan et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2011)

Add to Collection
Article Engineering, Manufacturing

A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations

Lars Moench et al.

JOURNAL OF SCHEDULING (2011)

Add to Collection
Article Computer Science, Artificial Intelligence

Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing

Te-Sheng Li et al.

EXPERT SYSTEMS WITH APPLICATIONS (2009)

Add to Collection
Article Computer Science, Artificial Intelligence

Wafer defect pattern recognition by multi-class support vector machines by using a novel defect cluster index

Li-Chang Chao et al.

EXPERT SYSTEMS WITH APPLICATIONS (2009)

Add to Collection
Article Engineering, Electrical & Electronic

Defect detection on semiconductor wafer surfaces

NG Shankar et al.

MICROELECTRONIC ENGINEERING (2005)

Add to Collection
Article Computer Science, Artificial Intelligence

Learning multi-label scene classification

MR Boutell et al.

PATTERN RECOGNITION (2004)

Add to Collection
Webinars Posters Questions Hubs Funding Journals Papers Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.