Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects

Batool, Uzma; Shapiai, Mohd Ibrahim; Fauzi, Hilman; Fong, Jia Xian

doi:10.1109/cspa48992.2020.9068669

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…This approach balances a given dataset by adding (oversampling) or removing (undersampling) data samples from the dataset. For the imbalanced data associated with wafer maps, Batool et al 5 and Piao et al 6 used a random undersampling method by using the WM-811K wafer dataset 7 . Kim et al 8 utilized oversampling instead of undersampling to generate higher detection accuracies.…”

Section: Related Workmentioning

confidence: 99%

“…The total loss function of CycleGAN can be expressed as a summation of the adversarial losses and the cycle consistency loss as noted below ℒ(𝐺 𝑋→𝑌 , 𝐺 𝑌→𝑋 , 𝐷 𝑋 , 𝐷 𝑌 ) = ℒ 𝐴 𝐷 𝑋 + ℒ 𝐴 𝐷 𝑌 + 𝜆ℒ 𝐶 (5) where 𝜆 controls the relative importance of the losses. Generated images are made to look realistic by the adversarial losses, and the cycle consistency loss reflects the variation between the original image and the generated or transformed image.…”

Section: Full Objective Functionmentioning

confidence: 99%

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Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Alam

Kehtarnavaz

2023

Real-Time Image Processing and Deep Learning 2023

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Defective dies on a silicon wafer form a pattern that is called a wafer map. In order to adequately train a deep learning-based automated optical inspection system to detect such defective patterns, a large number of defective patterns or wafer maps are needed. In practice, on an actual production line, defective patterns occur infrequently and thus are difficult and time consuming to collect. A computationally efficient defective pattern generation solution is developed in this paper by using the deep learning network of CycleGAN which is a variant of the generative adversarial network. The public domain WM-811K wafer dataset was used to generate or synthesize defective patterns or wafer maps. The two metrics of Fréchet inception distance and kernel inception distance were utilized to evaluate the resemblance of the generated defective images to the real defective images. The results obtained indicate that the developed defective pattern generation method produces realistic wafer maps at a computationally efficient rate of 3 synthesized images per second.

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Section: Related Workmentioning

confidence: 99%

Section: Full Objective Functionmentioning

confidence: 99%

Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Alam

Kehtarnavaz

2023

Real-Time Image Processing and Deep Learning 2023

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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 survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

Huang,

Meng,

Huang

2023

Cluster Comput

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Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects

Cited by 13 publications

References 23 publications

Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Mask defect detection with hybrid deep learning network

A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

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