基于机器视觉的集成电路声扫图像缺陷检测软件设计  

Software design of acoustic scanning image defect detection based on machine vision

作　　者：赵玥 肖梦燕 邱宝军[1] 罗军[1] 王小强[1] 罗道军[1] ZHAO Yue;XIAO Meng-yan;QIU Bao-jun;LUO Jun;WANG Xiao-qiang;LUO Dao-jun(China Electronic Product Reliability and Environmental Testing Research Institute,Guangzhou 510610,China)

机构地区：[1]工业和信息化部电子第五研究所,广东广州510610

出　　处：《计算机工程与科学》2023年第10期1806-1813,共8页Computer Engineering & Science

基　　金：广东省基础与应用基础研究基金(2021A1515110939)。

摘　　要：集成电路是电子产品的重要组成部分,其质量控制和故障分析是电子产品能否长期运行的前提。声学扫描显微镜SAM作为一种无损缺陷检测手段,在集成电路成像检测、内部缺陷识别方面获得了广泛应用。针对声扫图像缺陷检测的智能化需求,以及对检测的实时性和正确性要求,研发了基于机器视觉的集成电路声扫图像缺陷检测软件,提供了图像处理和图像检测一体化功能。该软件的算法框架结合了深度学习技术、OpenCV传统图像处理技术和JavaScript界面设计技术,可以管理各种类型的集成电路数据,并能对声扫图像进行分析处理与缺陷判定。Integrated circuits are an important part of electronic products,and their quality control and fault analysis are prerequisites for the long-term operation of electronic products.Scanning Acoustic Microscope(SAM),as a non-destructive defect detection method,has been widely used in imaging detection and internal defect identification of integrated circuits.In response to the intelligent demand for acoustic scanning image defect detection and the requirements for real-time and accurate detection,this paper develops a software for integrated circuit acoustic scanning image defect detection based on machine vision,providing integrated functions for image processing and image detection.The algorithm framework of this software combines deep learning technology,traditional image processing technology using OpenCV,and JavaScript interface design technology,allowing it to manage various types of integrated circuit data and analyze,process,and determine defects in scanning acoustic images.

关 键 词：声扫图像 缺陷检测 目标检测网络 JAVASCRIPT 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]