Optimizing YOLOv7 for semiconductor defect detection

Enrique Dehaerne; Bappaditya Dey; Sandip Halder; Stefan De Gendt

doi:10.1117/12.2657564

27 April 2023 Optimizing YOLOv7 for semiconductor defect detection

Enrique Dehaerne, Bappaditya Dey, Sandip Halder, Stefan De Gendt

Proceedings Volume 12496, Metrology, Inspection, and Process Control XXXVII; 124962D (2023) https://doi.org/10.1117/12.2657564
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Conference Poster

Abstract

The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. YOLOv7 is a state-of-the-art object detector based on the YOLO family of models which have become popular for industrial applications. One such possible application domain can be semiconductor defect inspection. The performance of any machine learning model depends on its hyperparameters. Furthermore, combining predictions of one or more models in different ways can also affect performance. In this research, we experiment with YOLOv7, a recently proposed, state-of-the-art object detector, by training and evaluating models with different hyperparameters to investigate which ones improve performance in terms of detection precision for semiconductor line space pattern defects. The base YOLOv7 model with default hyperparameters and Non Maximum Suppression (NMS) prediction combining outperforms all RetinaNet models from previous work in terms of mean Average Precision (mAP).We find that vertically flipping images randomly during training yields a 3% improvement in the mean AP of all defect classes. Other hyperparameter values improved AP only for certain classes compared to the default model. Combining models that achieve the best AP for different defect classes was found to be an effective ensembling strategy. Combining predictions from ensembles using Weighted Box Fusion (WBF) prediction gave the best performance. The best ensemble with WBF improved on the mAP of the default model by 10%.

Citation Download Citation

Enrique Dehaerne, Bappaditya Dey, Sandip Halder, and Stefan De Gendt "Optimizing YOLOv7 for semiconductor defect detection", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 124962D (27 April 2023); https://doi.org/10.1117/12.2657564

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available