Short critical area model and extraction algorithm based on defect characteristics in integrated circuits

Wang, Junping; Wu, Yao; Zhao, Teng-Wei

doi:10.1007/s10470-016-0841-y

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Specifically, defect detection employing a conventional deep neural network (DNN) can be applied for pattern recognition and classification. In the detection tasks, the defect points to be processed are much smaller, typically containing approximately one to seven pixels, but they are analyzed in an image consisting of 1024 × 1024 pixels or even more [3] . This poses a great challenge for automatic inspection.…”

Section: Introductionmentioning

confidence: 99%

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Zhou

Gao

Zhang

et al. 2022

Fundamental Research

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Section: Introductionmentioning

confidence: 99%

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Zhou

Gao

Zhang

et al. 2022

Fundamental Research

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“…Corrosion and fatigue damage occur in daily applications, thus increasing production costs and causing considerable wasted resources and economic opportunities [1]. Defect detection technology has achieved favorable results in areas, such as pipelines [2.3], electronic components [4]- [6], parts [7]- [9], fault diagnosis [10.11] and others [12.13]. However, research on the defect detection of tiny parts, particularly the literature on real-time defect detection of tiny parts in conveyor belts, has remained scarce.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

et al. 2019

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We adopted actual intelligent production requirements and proposed a tiny part defect detection method to obtain a stable and accurate real-time tiny part defect detection system and solve the problems of manually setting conveyor speed and industrial camera parameters in defect detection for factory products. First, we considered the important influences of the properties of tiny parts and the environmental parameters of a defect detection system on its stability. Second, we established a correlation model between the detection capability coefficient of the part system and the moving speed of the conveyor. Third, we proposed a defect detection algorithm for tiny parts that are based on a single short detector network (SSD) and deep learning. Finally, we combined an industrial real-time detection platform with the missed detection algorithm for mechanical parts based on intermediate variables to address the problem of missed detections. We used a 0.8 cm darning needle as the experimental object. The system defect detection accuracy was the highest when the speed of the conveyor belt was 7.67 m/min.

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Novel Model of OCA in IC and Its Implementation on Hadoop

Wei¹,

Wang²

2019

IJEEE

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Short critical area model and extraction algorithm based on defect characteristics in integrated circuits

Cited by 3 publications

References 29 publications

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Novel Model of OCA in IC and Its Implementation on Hadoop

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