# Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning

## TL;DR

This paper demonstrates that machine learning models can effectively predict plasma etching equipment failures, outperforming human benchmarks, and offering a promising approach to maintenance in semiconductor manufacturing.

## Contribution

It introduces three machine learning tasks for predicting equipment failure, showcasing their effectiveness over traditional methods.

## Key findings

- ML models outperform human benchmarks in failure prediction
- ML approaches reduce maintenance costs and downtime
- Three distinct ML tasks improve failure forecasting accuracy

## Abstract

Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an alternative for predicting upcoming machine failures based on relevant data points. In this paper, we describe three different machine learning tasks that can be used for that purpose: (i) predicting Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF intervals of an equipment. Our results show that trained machine learning models can outperform benchmarks resembling human judgments in all three tasks. This suggests that machine learning offers a viable alternative to currently deployed plasma etching equipment maintenance strategies and decision making processes.