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

arXiv: https://arxiv.org/abs/1904.07686 · HTML: https://mlsanity.com/paper/1904.07686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions](https://mlsanity.com/paper/2305.09777/) (2023)
- [Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM](https://mlsanity.com/paper/2603.23576/) (2026)

## Abstract (arXiv)

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.