Open AccessConvolutional neural network model based on terahertz imaging for integrated circuit defect detections
Author(s) -
Qi Mao,
Yanqiu Zhu,
Cixing Lv,
Yao Lu,
Xiaohui Yan,
Shihan Yan,
Jingbo Liu
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.384146
Subject(s) - terahertz radiation , convolutional neural network , computer science , integrated circuit , terahertz spectroscopy and technology , dropout (neural networks) , artificial intelligence , optics , materials science , optoelectronics , physics , machine learning
Detection of integrated circuit (IC) defects is vital in IC manufacturing. Traditional defect detection methods have relied on scanning electron microscopy and X-ray imaging techniques that are time consuming and destructive. Hence, in this paper we considered terahertz imaging as a label-free and nondestructive alternative. This study aimed to use a convolutional neural network model (CNN) to improve the performance of the terahertz imaging IC detection system. First, we constructed a terahertz imaging IC dataset and analyzed it. Subsequently, a new CNN structure was proposed based on the VGG16 network. Finally, it was optimized based on its structure and dropout rate. The method proposed above can improve IC defects detection accuracy of THz imaging. Most significantly, this work will promote the application of terahertz imaging in practice and provide a foundation to further research in relevant fields.