A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

Schlosser, Tobias; Beuth, Frederik; Friedrich, Michael; Kowerko, Danny

doi:10.1109/etfa.2019.8869311

Cited by 18 publications

(9 citation statements)

References 14 publications

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“…II-B. The current work is deeply rooted in neuroscience and focuses on the concept of visual attention, while extending an earlier workin-progress publication of us using opposingly a specialized pipeline [7]. The current biologically-grounded contribution also evaluates, in comparison to this shorter work, the concept of visual attention with deep learning more broader and indepth.…”

Section: Wafer 2 Wafer 3 Wafermentioning

confidence: 97%

“…The field of automated visual inspection for wafer dicing uses classically image processing approaches commonly differentiated due to their functionality in projection-, filterbased, and hybrid approaches [3,7]. Projection-based approaches include for example principal component analysis, whereas filter-based approaches encompass spectral estimation methods, yet, they often need manual adaption.…”

Section: Introductionmentioning

confidence: 99%

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Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

Beuth

Schlosser

Friedrich

et al. 2020

IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society

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It is a long-term goal to transfer biological processing principles as well as the power of human recognition into machine vision and engineering systems. One of such principles is visual attention, a smart human concept which focuses processing on a part of a scene. In this contribution, we utilize attention to improve the automatic detection of defect patterns for wafers within the domain of semiconductor manufacturing. Previous works in the domain have often utilized classical machine learning approaches such as KNNs, SVMs, or MLPs, while a few have already used modern approaches like deep neural networks (DNNs). However, one problem in the domain is that the faults are often very small and have to be detected within a larger size of the chip or even the wafer. Therefore, small structures in the size of pixels have to be detected in a vast amount of image data. One interesting principle of the human brain for solving this problem is visual attention. Hence, we employ here a biologically plausible model of visual attention for automatic visual inspection. On this basis, we propose a hybrid system of visual attention and a deep neural network. As demonstrated, our system achieves among other decisive advantages an improvement in accuracy from 81 % to 92 %, and an increase in accuracy for detecting faults from 67 % to 88 %. Therefore, the error rates are reduced from 19 % to 8 %, and notably from 33 % to 12 % for detecting a fault in a chip. Hence, these results show that attention can greatly improve the performance of visual inspection systems. Furthermore, we conduct a broad evaluation, which identifies specific advantages of the biological attention model in this application, and benchmarks standard deep learning approaches as an alternative with and without attention. This work is an extended arXiv version of the original conference article published in "IECON 2020". It has been extended regarding visual attention, covering (i) the improvement and equations of visual attention model, (ii) a deeper evaluation of the model, (iii) a discussion about possibilities to combine the attention model with the DNN, and (iv) a detailed overview about the data.

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Section: Wafer 2 Wafer 3 Wafermentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

Beuth

Schlosser

Friedrich

et al. 2020

IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society

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“…Then these ROIs are directly fed into the classifiers [8,9]. These approaches in which both traditional and novel methods are combined are commonly referred to as hybrid approaches [10]. Although many ML and DL methods have been used [11], it is now, as a result of the enormous increase in graphics processing unit (GPU) computing power, that they can be applied efficiently, thus achieving high accuracy and high performance ultimately.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…In several works, the defect classification from wafer and mask data with deep learning methods has been demonstrated. [7][8][9][10][11][12][13] Such methods also have been used to perform pattern matching, contour extraction, and 3D profile reconstruction from SEM images. [14][15][16] A general property of SEM images, the noise, has been addressed in further works, with goal of reducing the noise to obtain higher accuracy in the image analysis.…”

Section: Introductionmentioning

confidence: 99%

Mask defect detection with hybrid deep learning network

Evanschitzky

Auth

Heil

et al. 2021

J. Micro/Nanopattern. Mats. Metro.

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Background: Deep learning is a very fast-growing field in the area of artificial intelligence with remarkable results in recent years. Many works in the lithography and photomask field have shown progress of technology in this application area and the potential to improve lithography by the automation of processes. Despite this progress, the use of machine learning techniques in the field of mask repair still seems to be at the beginning.Aim: We show that deep learning-based methods can successfully be applied to mask repair applications.Approach: The presented system is a hybrid and modular approach based on a combination of several deep learning networks and analytical methods, enabling the detection of mask pattern defects and the determination of the exact defect shapes from SEM images. In the current version, the system is trained for line/space patterns and contact patterns with typical defect types. The modularity allows for the extensibility to new use cases. The issue of an insufficient amount of training data is addressed using purely computer-generated simplified SEM data in combination with a specific network architecture. Results:The very good functionality and defect detection accuracy of the system are demonstrated with a set of real SEM images with line/space patterns and contact patterns with numerous defects. In particular, a 100% true defect detection rate could be obtained. Conclusions:The presented machine learning approach demonstrates the successful defect identification, location, and shape determination from real mask SEM images.

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A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

Cited by 18 publications

References 14 publications

Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

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

Mask defect detection with hybrid deep learning network

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