大视场多尺度非接触光声智能缺陷检测算法  

Wide Field⁃of⁃View Multiscale Noncontact Photoacoustic Intelligent Defect⁃Detection Algorithm

作　　者：陈冀景 皮一涵 庞逸轩 张浩 丁凯旋 龙莹 李娇[1,2] 田震[1,2] Chen Jijing;Pi Yihan;Pang Yixuan;Zhang Hao;Ding Kaixuan;Long Ying;Li Jiao;Tian Zhen(School of Precision Instrument and Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China;Georgia Tech Shenzhen Institute,Tianjin University,Shenzhen 518067,Guangdong,China)

机构地区：[1]天津大学精密仪器与光电子工程学院,天津300072 [2]天津大学佐治亚理工深圳学院,广东深圳518067

出　　处：《中国激光》2024年第21期264-276,共13页Chinese Journal of Lasers

基　　金：国家自然科学基金(82171989,62235013);天津市杰出青年学者基金(20JCJQJC00190);深圳市自然科学基金重点项目(JCYJ20200109150212515);深圳市国际科技自主合作基金(GJHZ20210705142401004);2022年深圳市高校稳定支持计划(20220618114319001)。

摘　　要：为对倒装芯片在制备过程中产生的不同尺寸的缺陷进行大视场精准检测,本文提出了基于非相干式非接触光声显微镜(NINC-PAM)的大视场多尺度智能缺陷检测算法。搭建了NINC-PAM系统并开发了大视场光机联合扫描技术。基于此,提出了针对倒装芯片制备缺陷的Chip-YOLO多尺度缺陷检测算法。该算法优化了原始的YOLOv8,先后引入小目标检测(SOD)层、大型分离卷积注意力(LSKA)模块以及重参数化广义特征金字塔网络(RepGFPN)。横向对比以及消融实验结果展示了Chip-YOLO对不同尺寸缺陷高达60.1%的平均检测精度。不仅如此,所提算法还实现了在23 s内对倒装芯片样品中超过1 mm×1 mm的大视场多尺度剥离缺陷的检测。性能统计结果展示了Chip-YOLO相比于其他一、二阶段算法更精准、更快速的缺陷检测性能,证明了该算法有望为在线检测倒装芯片缺陷提供技术方案。Objective During the fabrication of flip chips,challenges to production yield and longevity arise owing to preparation defects,including delamination,missing solder bumps,and cracks.These defects typically manifest at dimensions ranging from the submillimeter to micron scale and are characterized by pronounced randomness and broad distribution.Consequently,comprehensive defect detection across these multiscale dimensions facilitates the early identification and removal of flawed chips,thereby enhancing both the production yield and long-term operational stability.Whereas existing nondestructive testing methodologies such as ultrasonics,laser ultrasonics,X-ray computed tomography,pulsed phase thermography,and conventional photoacoustics partially satisfy the requirements of flip-chip detection,they exhibit certain challenges.These challenges include sample contamination,detection-speed limitations imposed by the average sampling procedures,potential risks associated with ionizing radiation,and susceptibility to environmental effects.Hence,this study introduces an intelligent defect-detection methodology based on noninterferometric noncontact photoacoustic microscopy(NINC-PAM).This approach is designed to achieve accurate and extensive detections of preparation defects across varying dimensions within flip chips.By offering a feasible technical solution for inline nondestructive defect detection during the fabrication process of flip chips,this methodology is promising for substantially improving both the production yield and operational lifespan of flip chips.Methods First,we established an NINC-PAM system based on elasto-optical theory and autonomously developed an optical‒mechanical joint scanning imaging mode for the wide-field-of-view(FOV)imaging of flip-chip samples.Second,by leveraging the NINC-PAM system,we introduced a multiscale defect-detection algorithm named Chip-YOLO to identify preparation defects of varying sizes within flip chips.This algorithm enhances the original YOLOv8 architecture by sequent

关 键 词：光声显微镜 缺陷检测算法 倒装芯片 大视场 多尺度 

分 类 号：TH742[机械工程—光学工程]