Machine Learning-Based Detection Method for Wafer Test Induced Defects

Cheng, Ken Chau-Cheung; Chen, Leon Li-Yang; Li, Jiwei; Li, Katherine Shu-Min; Tsai, Nova Cheng-Yen; Wang, Sying-Jyan; Huang, Aihua; Chou, Leon; Lee, Chen-Shiun; Chen, Jwu E.; Liang, Hsing-Chung; Hsu, Chun-Lung

doi:10.1109/tsm.2021.3065405

Cited by 33 publications

(18 citation statements)

References 14 publications

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“…It is challenging for a single technique to find all defects in a magnetic ring due to the diversity of defects. Meanwhile, machine vision techniques are employed to overcome these drawbacks since they make it simpler to implement automated production and information technology across a range of industries [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang

Ren

Wan

et al. 2022

Sensors

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Magnetic rings are widely used in automotive, home appliances, and consumer electronics. Due to the materials used, processing techniques, and other factors, there will be top cracks, internal cracks, adhesion, and other defects on individual magnetic rings during the manufacturing process. To find such defects, the most sophisticated YOLOv5 target identification algorithm is frequently utilized. However, it has problems such as high computation, sluggish detection, and a large model size. This work suggests an enhanced lightweight YOLOv5 (MR-YOLO) approach for the identification of magnetic ring surface defects to address these issues. To decrease the floating-point operation (FLOP) in the feature channel fusion process and enhance the performance of feature expression, the YOLOv5 neck network was added to the Mobilenetv3 module. To improve the robustness of the algorithm, a Mosaic data enhancement technique was applied. Moreover, in order to increase the network’s interest in minor defects, the SE attention module is inserted into the backbone network to replace the SPPF module with substantially more calculations. Finally, to further increase the new network’s accuracy and training speed, we substituted the original CIoU-Ioss for SIoU-Loss. According to the test, the FLOP and Params of the modified network model decreased by 59.4% and 47.9%, respectively; the reasoning speed increased by 16.6%, the model’s size decreased by 48.1%, and the mAP only lost by 0.3%. The effectiveness and superiority of this method are proved by an analysis and comparison of examples.

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Section: Introductionmentioning

confidence: 99%

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang

Ren

Wan

et al. 2022

Sensors

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“…For example, Horng et al [ 12 ] proposed a two-stage approach to ordinal optimization theory, in which sufficient values are obtained in a reasonable computation time, reducing overkill, and retesting problems. Cheng et al [ 11 ] utilized machine learning algorithms to detect defect-inducing features automatically, thus utilizing re-testing of chips to improve yield. In addition, Selg et al [ 13 ] proposed a method for applying machine learning to predict test–retest effectively.…”

Section: New Scheme Of Mrsmentioning

confidence: 99%

Multiple Retest Systems for Screening High-Quality Chips

Yeh

Chen

2023

J Electron Test

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“…Another, related research area opposing to the inspection of visual material is concerned with the analysis of wafer maps which are based on a contact needle inspection. Wafer maps are typically generated by placing an uncut wafer into a wafer prober device (Cheng et al 2021). This test device utilizes contact needles in order to establish a connection to individual circuits.…”

Section: Introductionmentioning

confidence: 99%

“…After the wafer is cut, the defective circuits are sorted out, the results are color-coded as wafer maps, and their patterns are analyzed to optimize the manufacturing process. However, because of its intrusive nature, the electrical test bears the risk of potentially damaging the wafer (Cheng et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks

et al. 2022

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In the semiconductor industry, automated visual inspection aims to improve the detection and recognition of manufacturing defects by leveraging the power of artificial intelligence and computer vision systems, enabling manufacturers to profit from an increased yield and reduced manufacturing costs. Previous domain-specific contributions often utilized classical computer vision approaches, whereas more novel systems deploy deep learning based ones. However, a persistent problem in the domain stems from the recognition of very small defect patterns which are often in the size of only a few $$\mu $$ μ m and pixels within vast amounts of high-resolution imagery. While these defect patterns occur on the significantly larger wafer surface, classical machine and deep learning solutions have problems in dealing with the complexity of this challenge. This contribution introduces a novel hybrid multistage system of stacked deep neural networks (SH-DNN) which allows the localization of the finest structures within pixel size via a classical computer vision pipeline, while the classification process is realized by deep neural networks. The proposed system draws the focus over the level of detail from its structures to more task-relevant areas of interest. As the created test environment shows, our SH-DNN-based multistage system surpasses current approaches of learning-based automated visual inspection. The system reaches a performance (F1-score) of up to 99.5%, corresponding to a relative improvement of the system’s fault detection capabilities by 8.6-fold. Moreover, by specifically selecting models for the given manufacturing chain, runtime constraints are satisfied while improving the detection capabilities of currently deployed approaches.

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Machine Learning-Based Detection Method for Wafer Test Induced Defects

Cited by 33 publications

References 14 publications

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Multiple Retest Systems for Screening High-Quality Chips

Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks

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