Intelligent condition-based maintenance of reactive ion etching process

The semiconductor industry is facing increasing competition; motivating the need for condition-based maintenance in conjunction with process control. Condition-based maintenance can reduce the downtime of expensive equipment, production costs, and improve yield. Moreover, it can potentially reduce operation cost of semiconductor fabs by lowering the number of expensive spare parts that need to be in stock. This paper proposes an intelligent condition-based maintenance approach wherein the operating parameters for the process are selected while being constrained both by the process and maintenance requirements. Reactive ion etcher is selected as the target equipment: it is widely used and is a critical equipment in semiconductor industry. Based on real-time process and equipment condition data, artificial neural networks are used for assessing the current condition of the equipment and predicting the remaining time before the etcher needs to be shut down for maintenance. The proposed data driven approach is specially suited for the semiconductor industry, which relies heavily on statistical techniques for process control and optimization.