Automated Die Inking through On-line Machine Learning

Abstract

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. Recently, machine learning has been used to automate and speed-up the inking process. In this work, we employ on-line machine learning to address the practicability limitations of the current state-of the-art automated inking approach. Effectiveness is demonstrated on an industrial dataset of manually inked wafers.