PDF(Pubmed)
Abstract：
Microscopic defects in flip chips, originating from manufacturing, significantly affect performance and longevity. Post-fabrication sampling methods ensure product functionality but lack in-line defect monitoring to enhance chip yield and lifespan in real-time. This study introduces a photoacoustic remote sensing (PARS) system for in-line imaging and defect recognition during flip-chip fabrication. We first propose a real-time PARS imaging method based on continuous acquisition combined with parallel processing image reconstruction to achieve real-time imaging during the scanning of flip-chip samples, reducing reconstruction time from an average of approximately 1134 ms to 38 ms. Subsequently, we propose improved YOLOv7 with space-to-depth block (IYOLOv7-SPD), an enhanced deep learning defect recognition method, for accurate in-line recognition and localization of microscopic defects during the PARS real-time imaging process. The experimental results validate the viability of the proposed system for enhancing the lifespan and yield of flip-chip products in chip manufacturing facilities.
摘要：
倒装芯片中的微观缺陷，源于制造业，显着影响性能和寿命。制造后采样方法可确保产品功能，但缺乏实时提高芯片产量和使用寿命的在线缺陷监测。这项研究介绍了一种光声遥感（PARS）系统，用于倒装芯片制造过程中的在线成像和缺陷识别。我们首先提出了一种基于连续采集与并行处理图像重建相结合的实时PARS成像方法，以实现倒装芯片样品扫描过程中的实时成像，将重建时间从平均约1134ms减少到38ms。随后，我们提出了改进的YOLOv7与空间深度块（IYOLOv7-SPD），一种增强的深度学习缺陷识别方法，在PARS实时成像过程中对微观缺陷进行准确的在线识别和定位。实验结果验证了所提出的系统在芯片制造设施中提高倒装芯片产品的寿命和产量的可行性。
