Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review

Kim, Tongwha; Behdinan, Kamran

doi:10.1007/s10845-022-01994-1

Cited by 34 publications

(5 citation statements)

References 104 publications

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“…With the advancement of technology, defect detection techniques have shifted from traditional digital image processing to methods based on deep learning [4]. These new approaches automatically extract image features, streamline processes, and improve efficiency and accuracy, making them effective tools for industrial quality control [5].…”

Section: Related Workmentioning

confidence: 99%

Wafer defect detection based on improved YOLOv8

Diao,

Zou,

Zhang

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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As the semiconductor industry rapidly develops, wafer defect detection becomes increasingly important, given that wafers are a key raw material [1]. Wafer defects can significantly affect chip reliability and performance, and timely detection and management of these defects are crucial for improving chip yield. This paper delves into the challenges of wafer defect detection and introduces an improved YOLOv8[2] deep learning architecture: YOLOv8-AM. In this architecture, the Neck layer incorporates the AFPN (Adaptive Feature Pyramid Network) to replace the original structure, enhancing the fusion capabilities of different feature layers. Additionally, to boost network robustness, the MLIM (Multi-Layer Interaction Module) based on self-attention mechanisms is introduced, strengthening the learning of both shallow and deep features. The combination of AFPN and MLIM creates a new Neck layer, augmenting the model's learning ability and context information aggregation. Experiments show that YOLOv8-AM, with a reduced parameter count of 2.12M, achieves high detection precision (95.228%) and recall rate (89.249%), with a mean Average Precision (mAP50) of 88.598% on wafer defect datasets. Compared to mainstream defect detection algorithms, this model performs better, has fewer parameters, and is more suitable for industrial deployments.

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Section: Related Workmentioning

confidence: 99%

Wafer defect detection based on improved YOLOv8

Diao,

Zou,

Zhang

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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“…Several studies have demonstrated the effectiveness of deep learning for silicon wafer defects [26,27] with a focus on single-type defects [28,29,30,31,32]. However, there is a need to investigate more mixed-type defects, which are becoming common with the increasing complexity of fabrication processes.…”

Section: Defect Segmentationmentioning

confidence: 99%

An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification

Batool,

Shapiai,

Mostafa

et al. 2023

IEEE Access

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Silicon wafer defect classification is crucial in improving fabrication and chip production. While deep learning methods have been successful in single-defect wafer classification, the increasing complexity of the fabrication process has introduced the challenge of multiple defects on wafers, which requires more robust feature learning and classification techniques. Attention mechanisms have been used to enhance feature learning for multiple wafer defects. However, they have limited use in a few mixed-type defect categories, and their performance declines as the number of mixed patterns increases. This work proposes an attention-augmented convolutional neural networks (A2CNN) model for enhanced discriminative feature learning of complex defects. The A2CNN model emphasizes the features in the channel and spatial dimensions. Additionally, the model adopts the focal loss function to reduce misclassification and a global average pooling layer to enhance the network's generalization by reducing overfitting. The A2CNN model is evaluated on the MixedWM38 wafer defect dataset using 10-fold cross-validation. It achieves impressive results, with accuracy, precision, recall, and F1-score reported as 98.66%, 99.0%, 98.55%, and 98.82% respectively. Compared to existing works, the A2CNN model performs better by effectively learning valuable information for complex mixed-type wafer defects.

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“…In particular, as AI (Artificial Intelligence) technology has expanded a lot in recent years, there are many ways to create meaningful data by processing and converging large amounts of data. In particular, various methods of classifying and analyzing patterns through pattern clustering based on various characteristics using unsupervised learning-related algorithms have been published [1][2][3][4][5][6]. Basically, one of the advantages of unsupervised learning has been widely used in the field because it can be used in that it can extract patterns of hidden information based on characteristics from large amounts of data without labels.…”

Section: Introductionmentioning

confidence: 99%

Advanced pattern defect detection and analysis with artificial intelligence

Yeo,

Jang,

Guk

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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As semiconductor process technology needs to be advanced, the difficulty of patterning increases and unexpected pattern defects occur. In fact, it is very important to quickly identify and resolve these pattern defects in advance, but there is a limit to measuring all of these pattern defects that occur in wafers. In particular, in order to overcome metrological limitations, it may be best to extract actual potential pattern defects by classifying various types of patterns that actually exist. We measured how to classify weak patterns by sampling them based on previously known weak pattern group libraries, simulation-based weak patterns, and unique patterns. Through this method, engineers can subjectively judge how to find weak patterns, and it can be difficult because there is a possibility that the probability of weak patterns is low depending on the limited measurement capacity. In this paper, unsupervised learning is used to cluster and classify by pattern type based on the various features of pattern. Then, based on reliable wafer data for various classified pattern types, the degree of vulnerability to defect was quantified for each classified cluster to give a ranking for extracting a weak pattern group for each cluster, and the weak pattern was extracted based on this to confirm a high weak pattern detection rate. In addition, it provides effective solutions to extract weak patterns from various databases (DBs) and specifically to give reliability to visualization methods.

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Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review

Cited by 34 publications

References 104 publications

Wafer defect detection based on improved YOLOv8

Wafer defect detection based on improved YOLOv8

An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification

Advanced pattern defect detection and analysis with artificial intelligence

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