Data-driven Prognostic Approaches for Semiconductor Manufacturing Process: A Review of Recent Works and Future Perspectives

The manufacturing process of semiconductor devices is one of the most complex processes in manufacturing industry. The devices fabrication is performed through hundreds of sequential process steps with different recipes. The level of complexity is also increasing due to the high demands in terms of feature size and number of devices. Maintaining high yield and good quality production are the main objectives of these industries. These objectives can be achieved by adopting efficient maintenance strategies. In this context, a suitable prognostic model is required in order to schedule the maintenance actions. Among the different prognostic approaches, data-driven ones received a lot of attention since they do not require any specific knowledge for modeling these complex processes. Although the advances in data-driven prognostic works, there is a real lack of survey papers that overview and discuss the existing approaches for this industry over the past 10 years. Therefore, this paper presents a systematic overview of data-driven prognostic for semiconductor manufacturing. It investigates the different used methods, the challenges of their application and the unexplored research areas.