A Deep Transfer Learning Model for Packaged Integrated Circuit Failure Detection by Terahertz Imaging

IEEE Access

Yao Lu¹  Jingbo Liu¹  Qi Mao² 

[1] School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, China;School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China;

关键词: THz-TDS imaging;    image enhancement;    failure detection;    convolutional neural networks;    transfer learning;   

DOI  :  10.1109/ACCESS.2021.3118687

来源: DOAJ

【 摘 要 】

Terahertz time-domain spectroscopy imaging system (THz-TDS) is becoming a promising tool for packaged integrated circuit (IC) failure detection due to its nonmetal penetrability and low radiation. However, two major obstacles are hindering the industrial application of the THz-TDS based IC detection method: 1) the low resolution of THz images may affect the detection accuracy; 2) the failure detection tasks are always carried out manually, which is inefficient and inaccurate. Thus, in this paper, we firstly enhanced the quality of IC THz images with a deconvolution algorithm and a mathematically simulated point spread function (PSF), and then we proposed a deep convolutional neural network (CNN) based failure detection framework to achieve end-to-end IC inspection automatically. Besides, we introduced transfer learning to overcome the limitation of the IC dataset size. The result demonstrated that our proposed method achieved excellent performance concerning both accuracy and efficiency.

【 授权许可】

Unknown