Ensuring high reliability in modern integrated circuits (ICs) requires the employment of several die screening methodologies. One such technique, commonly referred to as die inking, aims to discard devices that are likely to fail, based on their proximity to known failed devices on the wafer. Die inking is traditionally performed manually by visually inspecting each manufactured wafer and thus it is very time-consuming. Towards reducing this cost, we introduce a novel machine learning-based methodology to learn and automatically generate the inking pat-terns from the failure maps, thus eliminating the need for human intervention. Effectiveness is demonstrated on an industrial set of manually inked wafers.

中文翻译：

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 自动模具着墨


确保现代集成电路 (IC) 的高可靠性需要采用多种芯片筛选方法。其中一种技术通常称为芯片上墨，旨在根据可能发生故障的器件与晶圆上已知故障器件的接近程度来丢弃这些器件。芯片上墨传统上是通过目视检查每个制造的晶圆来手动执行的，因此非常耗时。为了降低成本，我们引入了一种新颖的基于机器学习的方法来从故障图中学习并自动生成墨迹模式，从而消除了人工干预的需要。其有效性在一组工业手动上墨晶圆上得到了证明。