Maximizing the Productivity of Photolithography Equipment by Machine Learning Based on Time Analytics

Abstract

Maximizing productivity is one of the most critical factors for competitiveness in the manufacturing industry. Needless to say, the semiconductor industry, in which the automation rate is relatively high and the manufacturing process continues 24 h a day, requires high productivity to be maintained. This paper is about a model that analyzes the cause of an increase in time needed for the whole photolithography process and automatically classifies it in real-time by machine learning. The time analytics model based on a k-means algorithm divides the processing time into four hundred detailed time steps and classifies causes through normalizing and clustering processes. Further, true/false measures of performance were employed based on the confusion matrix. To increase the accuracy of the model, the classified cause becomes a source for creating a new algorithm that can detect problems quickly and accurately. A small number of wafers that the system has failed to classify has accumulated in the database to increase the frequency of occurrence. As a result of evaluating the time analytics model in the photolithography extreme ultraviolet (EUV) equipment, the model has classified 98.6% of the wafers that exceed the limitation. Continuous updates of new phenomena that will be generated from advanced technologies will be more important than the current classification ability. We are accumulating unclassified data for a sustainable system and will continue to classify by synthesizing new phenomena. Data classified in real-time with high accuracy become a steppingstone for maintaining high productivity. Production equipment and processes are developed to enhance individual characteristics. Nevertheless, a data mining method that divides the process time can also be widely used in manufacturing processes of other fields.