Published: CABEQ 36 (1) (2022) 1-16
Paper type: Review
V. Arpitha and A. K. Pani
In modern industries, early fault detection is crucial for maintaining process safety and product quality. Process data contains information on the entire plant acting as a map for visualization of relationships between various plant units, making data-driven process monitoring a key technology for efficiency enhancement. This article focuses on review of process monitoring techniques reported for metal etching process, which is a batch operation carried out in semiconductor manufacturing industry. Various machine learning (and deep learning) techniques applied to date for fault detection and diagnosis of metal etching process are surveyed. Detailed survey of research work on different techniques and the reported results are presented in graphical (pie chart and bar chart) and tabular format. The review article further presents the pros and cons, gaps and future directions in the techniques applied in metal etching process.
This work is licensed under a Creative Commons Attribution 4.0 International License
metal etch process, semiconductor manufacturing, machine learning, process monitoring, fault detection