Convolutional neural network model based on terahertz imaging for integrated circuit defect detections
Qi Mao, Yunlong Zhu, Cixing Lv, Yao Lu, Xiaohui Yan, Shihan Yan, Jingbo Liu
Abstract
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.