Automated Wafer Defect Classification using a Convolutional Neural Network Augmented with Distributed Computing

Lei, Hairong; Teh, Cho; Li, Hetong; Lee, Po-Hsuan; Fang, Wei

doi:10.1109/asmc49169.2020.9185253

Cited by 4 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following article [27], the authors implemented a CNN designed by themselves to perform the task of defect detection and classification. This CNN was composed of three convolutional layers, one max pooling layer and two FC layers.…”

Section: Article By Article Discussionmentioning

confidence: 99%

“…There are several DT algorithms such as regression tree, tree medium, and some ensemble methods such as random forest and bagged tree. As for SVMs, we have found one paper that uses a DT algorithm, concretely a random forest algorithm, to compare its accuracy with the principal method employed [27].…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

“…The configuration of these models will be briefly discussed in the results and discussion section. Moreover, several articles implementing CNN models designed by the authors have been selected in the scoping review [23,27,64].…”

Section: Other Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

et al. 2021

View full text Add to dashboard Cite

Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications. One prominent application of these technologies is defect detection and classification in the manufacturing industry in order to minimise costs and ensure customer satisfaction. Specifically, this scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL techniques and configurations have been used for defect detection and classification. Inspection operations have traditionally been carried out by specialised personnel in charge of visually judging the images obtained with a scanning electron microscope (SEM). This scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL methods have been used to detect and classify defects in SEM images. We also include the performance results of the different techniques and configurations described in the articles found. A thorough comparison of these results will help us to find the best solutions for future research related to the subject.

show abstract

Section: Article By Article Discussionmentioning

confidence: 99%

Section: Decision Trees (Dt)mentioning

confidence: 99%

See 1 more Smart Citation

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Automatic defect inspection that is accurate and reliable is key to reduce engineering time and manufacturing cost. Recently, Convolutional Neural Network (CNN)-based approaches have shown promising results in defect localization and classification [1][2][3][4] . Most of these previous works either only classify an image as containing a defect of a certain type or predict the location of a defect in the form of a minimal bounding-box, which we term as defect detection, which generally ignores the precise geometry of the defect pattern itself.…”

Section: Introductionmentioning

confidence: 99%

SEMI-PointRend: improved semiconductor wafer defect classification and segmentation as rendering

Hwang

Dey

Dehaerne³

et al. 2023

Metrology, Inspection, and Process Control XXXVII

View full text Add to dashboard Cite

In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by image rendering in computer graphics, a new image segmentation method that can generate high-resolution segmentation masks. It can also be flexibly integrated into common instance segmentation meta-architecture such as Mask-RCNN and semantic meta-architecture such as FCN. We implemented a model, termed as SEMI-PointRend, to generate precise segmentation masks by applying the PointRend neural network module. In this paper, we focus on comparing the defect segmentation predictions of SEMI-PointRend and Mask-RCNN for various defect types (line-collapse, single bridge, thin bridge, multi bridge non-horizontal). We show that SEMI-PointRend outperforms Mask R-CNN by up to 18.8% in terms of segmentation mean average precision.

show abstract

“…In the other approach, the machines are trained with normal data in advance, and then, they find the parts that do not match the learned normal data from the data to inspect. 7 In applying the former approach to detecting IC pattern anomaly/defects from SEM inspected images, 8 it is difficult to obtain sufficient numbers of training data, because anomaly data required for training are extremely rare in general. Additionally, tiny defects/anomalies need to be filtered from the normal pattern background, which has extremely wide varieties.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in random circuit patterns using autoencoder

Fukuda

Kondo

2021

J. Micro/Nanopattern. Mats. Metro.

View full text Add to dashboard Cite

Automated Wafer Defect Classification using a Convolutional Neural Network Augmented with Distributed Computing

Cited by 4 publications

References 4 publications

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

SEMI-PointRend: improved semiconductor wafer defect classification and segmentation as rendering

Anomaly detection in random circuit patterns using autoencoder

Contact Info

Product

Resources

About