## 1 Introduction
Small perturbations of the translation, rotation, scale, and even noise significantly affect the calculation of the similarity scores. Moreover, traditional methods sometimes lead to poor results owing to the increased complexity of microelectronic structures. For this reason, the problem of identifying abnormal dies is no longer a binary thresholding problem. Accordingly, it is regarded as a classification task in the present work. In recent years, deep-learning techniques have been extensively adopted in image classification applications. Deep architectures such as convolutional neural networks (CNNs) have verified their superiority over other existing methods. These deep architectures are currently the most popular approach for classification tasks. CNN-based models can be trained through end-to-end learning without specifying task-related feature extractors. The VGG-16 and VGG-19 models proposed in [12] are extremely popular and significantly improve AlexNet [13] by enlarging the filters and adding more convolution layers. However, deeper neural networks often become more difficult to train. [PERSON] et al. [14] presented a residual learning framework to simplify the training of a deep network. Their proposed residual networks (ResNets) are easy to optimize and can obtain a high level of accuracy from a remarkably increased depth of a network. The series of Inception networks presented in [15; 16; 17] is a significant milestone in the development of CNN-based classifiers. Unlike the majority of previous networks that stack more layers for better performance, Inception networks use certain tricks to improve the speed and accuracy, such as the operation of multi-sized filters at the same level, employing an Inception module with reduced dimensions, factorization of a \(5\times 5\) filter into two \(3\times 3\) filters to decrease the time consumed, regularization through label smoothing to prevent overfitting, and utilization of a hybrid Inception module inspired by ResNets. Thus far, the use of ResNets and Inception models has been a dominant trend when facing image-classification problems. In [18], the concern regarding increased computation efficiency was addressed, and a class of efficient models called MobileNets was presented. The goal of this study is to inspect all die patterns on a wafer and then identify defective dies or anomalies. The main contributions and innovations of our study are briefly described below. * We propose an automatic procedure for extracting a standard template which is then utilized for detecting the die patterns from the original SAT image of a wafer. * From the detected die patterns and their spatial properties, we present a simple method to predict the locations of pattern candidates that possibly contain certain predefined patterns. * We design and implement a deep CNN-based classifier to identify all detected patterns and predicted pattern candidates. This classifier can categorize them into the background, alignment mark, normal, and abnormal classes. * Finally, the proposed method uses the obtained patterns with the spatial properties and classification results to produce a wafer map. This map provides important information to engineers in their analysis regarding the root cause of die-scale failures [19]. The remainder of this paper is organized as follows. Section 2 introduces the main algorithm of the proposed method. The implementation details and experimental results are described in Section 3. Finally, some concluding remarks are presented in Section 4.