摘要
半导体芯片作为集成电路的重要组成部分,对其质量要求越来越高,因芯片在小型化、高密度的制造过程中产生缺陷,进而影响了芯片的性能和寿命。因此,缺陷的检测与识别对芯片可靠性的提升十分重要。综述了近10年来国内外基于机器视觉的芯片缺陷检测方法的研究进展。首先介绍了芯片的制造流程以及当前主流的芯片封装技术。然后概述了用于芯片缺陷成像的主流无损检测技术,主要包括光学成像、声学成像、红外热成像、电磁成像与X射线成像等技术。接着分别重点阐述了基于传统技术和基于深度学习的芯片表面的缺陷检测方法。随后按照缺陷部位比较分析了芯片封装体的缺陷检测方法。最后总结芯片缺陷检测当前存在的问题,对未来的研究方向进行了展望。
As a critical element of integrated circuits,semiconductor chips now demand increasingly higher quality standards.During the miniaturization and high-density manufacturing processes,chips are prone to defects that can impact their performance and longevity.Therefore,detecting and identifying these defects is crucial for enhancing chip reliability.This paper reviews the advancements in chip defect detection methods using machine vision over the past decade,both domestically and internationally.Initially,it introduces the chip manufacturing process and the prevailing chip packaging technologies.It then outlines the mainstream non-destructive testing technologies for chip defect imaging,which include optical imaging,acoustic imaging,infrared thermal imaging,electromagnetic imaging,and X-ray imaging.The paper further explains the methods for detecting surface defects in chips using both traditional technologies and deep learning.Additionally,it compares and analyzes defect detection methods for chip packages based on defect locations.Finally,the paper summarizes the current challenges in chip defect detection and explores potential future research directions.
作者
胡志强
吴一全
Hu Zhiqiang;Wu Yiquan(College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第7期1-26,共26页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61573183)项目资助。
