Adversarial Defect Detection in Semiconductor Manufacturing Process

Kim, Jae-Hoon; Nam, Yunhyoung; Kang, Mincheol; Kım, Kihyun; Hong, Ji-Suk; Lee, Sooryong; Kim, Do-Nyun

doi:10.1109/tsm.2021.3089869

Cited by 25 publications

(7 citation statements)

References 21 publications

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“…For example, works such as [3,4,5,6] similarly use YOLO but the focus is on finding certain patterns in SEM images without comparing with intended design, which means that if a pattern such as contact is missing, it would not get detected as defect as there is no reference for comparison. Combining SEM image and layout image into a single input as 2 channels has been done in [7] but the work uses Generative Adversarial Network (GAN) model for defect detection and is constrained to only two defect types.…”

Section: Related Workmentioning

confidence: 99%

Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Choi,

Xie,

Greeneltch

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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Performing accurate and timely Scanning Electron Microscope (SEM) image analysis to identify wafer defects is crucial as it directly impacts manufacturing yield. In this paper, a machine learning (ML) based approach for analyzing SEM images (from wafer inspection machines) to locate and classify wafer defects is proposed. A state-of-the-art one-stage objection detection model called YOLOv8 (You Only Look Once version 8) is used as it offers a good balance between accuracy and inference speed. Experimental results confirm that an ensemble model composed of multiple YOLOv8 models can predict 6 types of defects with a mean Average Precision (mAP) of 0.789 (at IoU=0.5) for unseen test data consisting of real-world SEM images from 5 wafer fabs that have varying image qualities.

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Section: Related Workmentioning

confidence: 99%

Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Choi,

Xie,

Greeneltch

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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show abstract

“…To allow for flexible bounding box sizes and achieve higher accuracy on complex datasets, more complex CNN-based object detection frameworks have been investigated in recent works. 11,12 These complex models all use a feature extractor backbone. 13 This is a pretrained CNN that extracts relevant high-, mid-, and low-level features from the image.…”

Section: Centernet Object Detection Frameworkmentioning

confidence: 99%

SEMI-CenterNet: a machine learning facilitated approach for semiconductor defect inspection

De Ridder,

Dey,

Dehaerne

et al. 2023

38th European Mask and Lithography Conference (EMLC 2023)

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“…Ref. 8 uses a conditional Generative Adversarial Networks (GANs) with convolutional layers to detect defects. The generator outputs the bounding boxes and severity of the defects ("hard" or "soft") given pairs of images and layouts.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based defect classification and detection in SEM images: a mask R-CNN approach

Dey

Dehaerne

Halder

et al. 2022

Metrology, Inspection, and Process Control XXXVI

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In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.

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Adversarial Defect Detection in Semiconductor Manufacturing Process

Cited by 25 publications

References 21 publications

Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

SEMI-CenterNet: a machine learning facilitated approach for semiconductor defect inspection

Deep learning based defect classification and detection in SEM images: a mask R-CNN approach

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