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

Yao Lu,Qi Mao,Jingbo Liu

doi:10.1109/access.2021.3118687

Abstract

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.

Highlights

Detection of hidden failures in integrated circuit (IC) packages is vital as there is an increasing demand for quality and reliability in ICs
2) We proposed a convolutional neural networks (CNNs) based automated IC failure detection method and introduced transfer learning (TL) to overcome the limitation of the IC dataset size, yield a better inspection accuracy
We aim to present a practical solution for terahertz-based IC failure detection, we firstly enhanced the quality of THz images to improve detection accuracy, and we proposed a CNN based failure detection framework to achieve end-to-end IC inspection

Summary

Introduction

Detection of hidden failures in integrated circuit (IC) packages is vital as there is an increasing demand for quality and reliability in ICs. One of the most common types is electrical overstress (EOS), which induces thermal shock, and generates hidden internal defects, such as cracks, delamination, and voids of packaging materials [1, 2]. A variety of failure analysis systems have been investigated to detect hidden defects in IC packages. Conventional failure detection methods include X-ray [3] and scanning acoustic microscopy (SAM) [4]. These approaches are practical but still have shortcomings [5]. X-ray cannot detect defects caused by thermal fatigue or EOS, as its energy does not change when passing through air in the defect locations [1, 2]. SAM, on the other hand, requires immersion of samples in a liquid environment, which may lead to the oxidation of these electronic devices [6]

Objectives

Methods

Conclusion