Using Unsupervised Machine Learning for Plasma Etching Endpoint Detection

Abstract

Much has been discussed around the advent of Industry 4.0 tools to improve yield across front-end and back-end semiconductor manufacturers. One of these tools is the etch endpoint detection (EPD) systems. It is essential to optimize the etch process by precisely landing on the underlying layers, because over-etching can cause underlying layer damage. In this work, we explore unsupervised machine learning for automatically identifying the endpoint during plasma etching of low open-area wafers using optical emission spectroscopy.