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Artificial intelligence deep learning for 3D IC reliability prediction
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AI
DOI：10.1038/s41598-022-08179-z.png)
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摘要
En 中文
Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore's law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human's judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the Good or Failure condition for a reflowed microbump, such as area loss percentage at the middle cross-section, are also revealed.
Keyword:
3-DIMENSIONAL INTEGRATION
ELECTROMIGRATION
MICROBUMPS
VOIDS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.3W
被引数:
83.5W
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引用论文
分享收藏New challenges of miniaturization of electronic devices: Electromigration and thermomigration in lead-free solder joints电子设备小型化的新挑战: 无铅焊点中的电迁移和热迁移.png)
MATERIALS & DESIGNOAAI
MATERIALS & DESIGNIF7.9
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