An Overview of Deeply Optimized Convolutional Neural Networks and Research in Surface Defect Classification of Workpieces

Abstract

Currently, the industrial development is becoming increasingly rapid. Technicalization, informatization and industrialization gives the fundamental impetus for industrial development and progress. Nevertheless, there are numerous problems that hindering industrial progress and human security in the industrial field. The surface defects of the workpieces are one of the primary problems. Moreover, defects of multi-type, mixed and unapparent characteristics presented by workpieces make the detection and classification of workpiece more difficult. Deep convolutional neural networks (DCNN) show strong ability of feature extraction and mines deeper essential features of data because of its features of unique receptive field structure and weights of shared. It can represent original data information well and obtain results more accurately than the traditional methods. But there also remains a problem that conventional DCNN has a huge number of parameters and computation, which brings great pressure to the equipment in terms of computing power, memory, speed and so on. Based on this situation, the optimization methods of CNNs model in the aspects of data, structure, algorithm are summarized. Related lightweight structures and networks are also summarized in this paper. The purpose of these work is to reduce the number of parameters and computation and improve the training performance. At the same time, the research on defect classification of workpieces based on traditional machine learning and deep learning model is reviewed, and the research on defect classification of workpieces based on deeply optimized CNNs is referred and prospected.

Wafer Defect Identification with Optimal Hyper-Parameter Tuning of Support Vector Machine using the Deep Feature of ResNet 101

“…Furthermore, evaluating a set of hyperparameter configurations is time-consuming and costly. Therefore, it is crucial to carefully and efficiently optimize hyperparameters to achieve the best possible training outcomes [13].…”

“…The intuition behind this approach is that a sufficiently large set of random samples is likely to include the global optimum or a close approximation with high probability. Moreover, random search is typically faster than grid search [13].…”

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

“…At present, although deep learning techniques have been successfully applied to many industrial detection tasks and can achieve the most advanced performance, the defect detection task is still challenging due to the need for a large number of training samples and highquality samples. The latest review of defect detection [13], [16], [89] points out that the three key issues of the current deep learning defect detection task can be summarized as follows:…”

“…Compared with many current literatures devoted to summarizing defect detection technology [13], [14], [15], [16], this paper makes the following contributions:…”

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model

“…erefore, wafer maps defect analysis gives significant information that may be used to uncover abnormal processes that occur during semiconductor manufacture and to take steps to rectify these issues. A precise classification of wafer map structures plays a significant role in the detection of wafer defects, which would ultimately improve the production and quality of semiconductors by leading to a more efficient wafer fabrication process [3].…”