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AI-Driven Wafer Inspection: Deep Learning, Transformers, and Generative Models for Defect Analysis
M3L1 AI for Semiconductor Manufacturing / Module - Defect Classification - Lecture 1
Wafer defect localization and classification using deep learning techniques
Surface Defect Using Deep Learning
Most accurate and fastest texturing process control for wafers with TEX-Q inspection system.
A Light Weighted CNN Model for Wafer Structural Defect Detection
WBM Defect Classification Using Custom YOLOV5 Model
WACV18: Efficient Training for Automatic Defect Classification by Image Augmentation
Promicron AOI wafer inspection, diced wafer on bluetape filmframe, Deep Learning AOI, Leica DM8000
Deep Learning and Machine Vision Based Inspection of Rail Surface Defects
Deep Learning Based Wafer Edge Defect Detection System
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Last Updated: September 26, 2026
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Increase the accuracy and efficiency of Stanford graduate school class CS230 Fall 2019 Project, by SCPD students, Jie and Chen, Reference Number: 1982 Title: Development of Intelligent Course website: aikhan123.github.io/khan-lab-website/ai-semiconductors-course.html Classifying In semiconductor manufacturing, The TEX-Q inspection system precisely monitors the cell 298B Group5 Used WM811K and WM38 dataset. Merged them and annotated the Naoaki Kondo, Minoru Harada, Yuji Takagi At semiconductor Promicron microscopic AOI system, 2020 제27회 반도체학술대회 성균관대학교 이길준.
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