A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images  
[PERSON]  
1 Instituto de Investigacion en Informatica de Alloacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain; [PERSON]F.L.d.I.R.); [EMAIL_ADDRESS] (R.S.-R.); [EMAIL_ADDRESS] (J.L.G.-S.); [PERSON]ADDRESS] (R.M.)  
2 Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
of those repetitions could jeopardize the whole manufacturing process [3]. As a result, today it is common for an expert to identify defects by visual judgement using a scanning electron microscope (SEM) after each of these steps along the manufacturing chain. Manual surface inspection methods conducted by quality inspectors suffer from the disadvantages of low efficiency, high labour intensity, low accuracy and low real-time efficiency [4]. These weaknesses can be addressed by using advanced computer vision techniques.  
With the advent of innovative machine learning (ML) and deep learning (DL) methods [5], which typically outperform traditional computer vision algorithms when fed with high-volume datasets [6], a revolution is taking place in inspection systems. Novel and traditional methods constitute the state-of-the-art of the inspection systems [7]. In many occasions, traditional methods are used to preprocess the images in order to improve them, extracting regions of interest (ROI) with the purposes of not wasting computational time focusing on redundant data and increasing the detectability of the defects. Then these ROIs are directly fed into the classifiers [8; 9]. These approaches in which both traditional and novel methods are combined are commonly referred to as hybrid approaches [10]. Although many ML and DL methods have been used [11], it is now, as a result of the enormous increase in graphics processing unit (GPU) computing power, that they can be applied efficiently, thus achieving high accuracy and high performance ultimately.  
This scoping review is intended to collect all ML and DL methods used up to now for the detection and classification of defects in semiconductor wafers from SEM images. The main challenge faced by this review is to shed light on these novel methods that will for sure replace or at least complement and assist human operators in the task of defect detection and classification in the semiconductor industry, moving from a present situation where this task is mostly performed manually to a new reality where detection and classification will be performed automatically. Therefore, this review will allow future authors to have prior knowledge on the best performing models. This will contribute to the growing trend of applying these techniques to the semiconductor manufacturing industry in defect detection and classification, improving the results obtained and increasing both revenue and efficiency in the industry.  
The paper is structured as follows. First, the methodology for searching the associated literature will be presented in Section 2. Then, we will focus on the fundamentals of the SEM in Section 3. Next, different ML methods will be analysed in Section 4. After that, DL methods and, fundamentally, the most typical CNN models and the general components of these networks will be introduced in Section 5. Later, the results and discussion will be conducted in Section 6. Finally, we will end this review with the conclusions in Section 7.  
Figure 1: Semiconductor wafer manufacturing steps.