Defect detection on IC wafers based on neural network

Abedini, A.; Ehsanian, Mehdi

doi:10.1109/icm.2017.8268815

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Generally, it has to analyze shape features at the first place in the detection process. The shape defects can be classified into regular shape defects and irregular shape defects, in which irregular shape defects are the complicated problem since they are not the regular pattern [14]. As for special products, whose surface is partially reflective or components are small, it may cause much overhead and reduce the reliability of inspection in traditional methods.…”

Section: Related Work a Manufacture Inspectionmentioning

confidence: 99%

“…In [14], a modern method using pulse coupled neural networks [17] is proposed for specific defects of integrated circuits (IC) without the reference image. Due to the characters of IC, the method presents a six-step process for identifying the position of IC defects, which can be illustrated briefly as an estimation of image noise parameter, bilateral filter, edge detection, Hough transform, classification and defect detection.…”

Section: Related Work a Manufacture Inspectionmentioning

confidence: 99%

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Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

Ota

Dong

2018

IEEE Trans. Ind. Inf.

395

146

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With the rapid development of Internet of Things (IoT) devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in the paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real-time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network (CNN) model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

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Section: Related Work a Manufacture Inspectionmentioning

confidence: 99%

Section: Related Work a Manufacture Inspectionmentioning

confidence: 99%

Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

Ota

Dong

2018

IEEE Trans. Ind. Inf.

395

146

View full text Add to dashboard Cite

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“…By definition, small objects refer to the objects smaller than 32 × 32 pixels or objects which cover less than only 10% of the image. [50], industrial product quality assessment [51], face recognition in surveillance cameras [52], sign detection in autonomous driving [53], ship detection in remotely sensed images [9] and others.…”

Section: Introductionmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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“…In general, the detection is based on reference image or no referential approaches. Morphological processing [23,24] and neural network [25] are without references, while methods [26,27] need the standard image. Finally, image registration based on the SURF algorithm is another way to align the detected image and the standard image.…”

Section: Introductionmentioning

confidence: 99%

Image Wafer Inspection based on Template Matching

Barone¹

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper presents a template matching technique for detecting defects in VLSI wafer images. This method is based on traditional techniques of image analysis and image registration, but it combines the prior art of image wafer inspection in a new way, using prior knowledge like the design layout of VLSI wafer manufacturing process. This technique requires a golden template of the patterned wafer image under inspection which is obtained from the wafer image itself mixed to the layout design schemes. First a mapping between physical space and pixel space is needed. Then a template matching is applied for a more accurate alignment between wafer device and template. Finally, a segmented comparison is used for finding out possible defects. Results of the proposed method are presented in terms of visual quality of defect detection, any misalignment at topology level and number of correctly detected defective devices.

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Defect detection on IC wafers based on neural network

Cited by 6 publications

References 6 publications

Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Image Wafer Inspection based on Template Matching

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