SEM imaging and Automated Defect Analysis at advanced technology nodes

Jain, Abhinav; Sheridan, John G.; Xing, Rita; Levitov, Felix; Yasharzade, Shay; Nguyen, Hoang Vu

doi:10.23919/mipro.2017.7966585

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…With the shrinkage of the products of the semiconductor manufacturing industry, the killer defects that completely ruin the product are also becoming smaller [18]. This fact has encouraged industries to implement high-resolution microscopy techniques, such as SEM explained above, and to develop ML methods that detect and classify these tiny killer defects with great precision.…”

Section: Machine Learningmentioning

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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Section: Machine Learningmentioning

confidence: 99%