Automated Die Inking

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.