## 7 Conclusions  
This work has presented a scoping review in relation to the detection and classification of defects in semiconductors from scanning electron microscope (SEM) images through the use of machine learning (ML) and deep learning (DL) approaches. Throughout the paper, we have addressed several issues.  
First, our search strategy was determined, obtaining nine final articles from a total of 224 found initially. Next, we focused on the fundamentals of SEM, explaining the objective of this microscopy and the main components of an SEM device. Later, we described the most typical ML methods, classifying them into supervised, unsupervised and semi-supervised methods. Then, we presented the different components of a CNN and, later, the most typical models as well as the two main approaches that can be followed for defect detection: one-stage and two-stage. Finally, the results obtained in the different articles were presented and discussed.  
The final conclusion should be that the main purpose of the scoping review has been fulfilled. To the best of our understanding, all the knowledge about the detection and classification of defects in semiconductors from SEM images using ML and DL has been gathered.  
From the authors' point of view, as only nine papers that address the challenge of detecting and classifying defects in semiconductor materials from SEM images have been found, there is a huge opportunity with a multitude of exploitable approaches to achieve even better results than those obtained so far in the literature reviewed. All the information that has been gathered will help in future works to directly rule out some methods that offer poor performance and to focus directly on the higher performance methods. Therefore, the main contribution of this work is to guide future authors towards the best performing methods to further improve the results in defect detection and classification and thus contribute to the development and prosperity of the industry.  
Conceptualisation, A.F.-C.; formal analysis, F.L.d.l.R. and R.S.-R. funding acquisition: A.F.-C. investigation, F.L.d.l.R, R.S.-R., J.L.G.-S. and R.M. methodology, F.L.d.l.R. supervision, R.M. and A.F.-C. writing--original draft, F.L.d.l.R. and R.S.-R. Writing--review and editing, R.M. and A.F.-C. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by iRel40, a European co-funded innovation project that has been granted by the ECSEL Joint Undertaking (JU) (grant number 876659). The funding of the project comes from the Horizon 2020 research programme and participating countries. National funding is provided by Germany, including the Free States of Saxony and Thuringia, Austria, Belgium, Finland, France, Italy, the Netherlands, Slovakia, Spain, Sweden, and Turkey. The publication is part of the project PCI2020-112001, funded by MCIN/AEI/10.13039/501100011033 and by the European Union \"NextGenerationEU\"/PRTR.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Conflicts of Interest: The authors declare no conflict of interest. The funding sources had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.