200mm晶圆厂良率缺陷检测的智能抽样方法

Ang Kian Huat, J. Yap, Ning Ning, Tan Siew Fen, Shakar Govindasamy Mani, Myla Terredano
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引用次数: 0

摘要

在半导体制造中,通过亮场和暗场扫描工具平台进行在线检测监控是检测异常事件的第二道防线,以防止缺陷逃逸到晶圆分拣或组装测试中。早期检测和良好的工艺工具覆盖率对晶圆质量至关重要。这些必须与竞争性的周期时间和成本相平衡。现在有许多更新的扫描工具、软件和制造系统可以优化这种采样监控。作为传统的200mm晶圆厂,系统升级可能不可行或可能昂贵。因此,必须开发一种创新的解决方案,以保持在半导体行业的相关性。本文介绍了一种在线检测抽样人工智能(AI)方法,以确保在不增加资本支出成本的情况下最大限度地提高工艺工具的扫描覆盖率——从相同的产能中获得更多的收益;以相同的扫描能力获得更多的覆盖范围,并改善防线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Sampling Methodology for Yield Defect Inspection in a 200mm Foundry Wafer Fab
In Semiconductor Manufacturing, inline Inspection monitoring by Brightfield and Darkfield scan tool platform is a 2nd line of defence to detect abnormal events to prevent defectivity escape to Wafer Sort or Assembly Test. Early detection and good process tool coverage are essential to the quality of wafers. These have to be balanced with competitive cycle time and cost. There are many newer scan tool, software and manufacturing systems available today that can optimize this sampling monitoring. As a legacy 200mm foundry wafer fab, system upgrade may not be feasible or can be expensive. As a result, an innovative solution had to be developed in order to stay relevant in the semiconductor industry. This paper introduces an inline inspection sampling Artificial Intelligent (AI) methodology to ensure maximum process tool scan coverage without additional CAPEX cost - getting more out from the same capacity; getting more coverage with same scan capacity and improve line of defence.
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