For over 40 years lithographic silicon scaling has driven circuit integration and performance improvement in the semiconductor industry. As silicon scaling slows down, the industry is increasingly dependent on IC package technologies to contribute to further circuit integration and performance improvements. This is a paradigm shift and requires the IC package industry to reduce the size and increase the density of internal interconnects on a scale which has never been done before. Traditional package characterization and process optimization relies on destructive techniques such as physical cross-sections and delayering to extract data from internal package features. These destructive techniques are not practical with today's advanced packages. In this paper we will demonstrate how data acquired non-destructively with a 3D X-ray microscope can be enhanced and optimized using machine learning, and can then be used to measure, characterize and optimize the design and production of buried interconnects in advanced IC packages. Test vehicles replicating 2.5D and HBM construction were designed and fabricated, and digital data was extracted from these test vehicles using 3D X-ray and machine learning techniques. The extracted digital data was used to characterize and optimize the design and production of the interconnects and demonstrates a superior alternative to destructive physical analysis. We report an mAP of 0.96 for 3D object detection, a dice score of 0.92 for 3D segmentation, and an average of 2.1um error for 3D metrology on the test dataset. This paper is the first part of a multi-part report.
翻译:40多年来,半导体行业的石化硅缩放促使半导体行业的电路整合和性能改进。随着硅缩放速度放慢,该行业日益依赖IC软件包技术来推动电路整合和性能改进。这是一种范式转变,要求IC软件包行业在前所未有的规模上缩小内部互连的规模,提高内部互连的密度。传统的包件定性和流程优化依赖于破坏性技术,如物理交叉路段和延迟提取内部包件特性的数据。这些破坏性技术对当今的先进软件包并不实用。在本文件中,我们将展示如何利用机器学习来增强和优化以3DX光显微镜获得的无破坏性数据包件技术,然后利用IC软件包行业来测量、定性和优化内部互连连接的规模;设计并制造了复制2.5D和HBM建造的测试车辆,并首次利用3DX光和机器学习技术从这些测试车辆中提取了数字数据。提取的数字数据用于对三D光谱多光谱的多维数据进行定性和优化,用于为我们S-D平均目的的3D目的的三维度数据测试部分数据测试,并展示了我们SIS平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平平比。