**6. Conclusions**

The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review was made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis was made. All data mining applications were classified in function of the application area. Five distinct areas were identified: quality control, maintenance, production, decision support systems, and finally, categorized as a whole, measurement, metrology, and instrumentation. Results showed that quality was the most popular one, with 47 publications, making 34.3% of all publications. Maintenance was an area in which only a few studies were made, highlighting the gap and the opportunity for more studies to be made in this area.

The work performed in this study concerning data mining applications in semiconductor manufacturing can have theoretical implications. The characterization and categorization of several useful and successful cases can positively contribute to future research efforts of employing such a wide range of techniques with the purpose of increasing the application and diffusion of data mining applications in semiconductor manufacturing. Knowledge of different models and algorithms could have positive implications for the development of theory, for understanding all the possible applications in different areas of semiconductor production, but also for the development of practice, since many of these were implemented and validated on the shop floor. However, as the literature review has shown, many applications can still be made since several studies address only a specific step of semiconductor manufacturing and documentation of real-life application are scarce. Additionally, recent data mining techniques and models have a grea<sup>t</sup> opportunity to be used since only a few studies exist. Finally, since the semiconductor manufacturing process is always evolving, the need to adapt these techniques to the newer process is another challenge and opportunity to explore.

Overall, as seen from all the comprised studies from distinct steps of semiconductor production, the scope and functions of data mining techniques can be enhanced and disseminated throughout the entire semiconductor manufacturing process in order to provide, in real time, a proactive adjustment and advanced control decisions for the whole process and the smart facilities. Therefore, more research should be made to employ and facilitate smart production for Industry 4.0 in several industries for digital transformation and for upgrading existing manufacturing units. This will allow for an improving capability for optimizing interrelated decisions and improving decision flexibility.

**Author Contributions:** Conceptualization and methodology, R.G. and P.E.-C.; software R.G.; validation and investigation, R.G. and P.E.-C.; review and editing, E.M.G.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
