A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing

Abstract

Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as reducing the product cost. Therefore, this paper proposes an accurate model for the automatic recognition of wafer map failure types using a deep learning-based convolutional neural network (DCNN). For this experiment, we use WM811K, which is an open-source real-time wafer map dataset containing wafer map images of nine failure classes. Our research contents can be briefly summarized as follows. First, we use random sampling to extract 500 images from each class of the original image dataset. Then we propose a deep convolutional neural network model to generate a multi-class classification model. Lastly, we evaluate the performance of the proposed prediction model and compare it with three other popular machine learning-based models—logistic regression, random forest, and gradient boosted decision trees—and several well-known deep learning models—VGGNet, ResNet, and EfficientNet. Consequently, the comprehensive analysis showed that the performance of the proposed DCNN model outperformed those of other popular machine learning and deep learning-based prediction models.

Efficient Convolutional Neural Networks for Semiconductor Wafer Bin Map Classification

“…Chen et al performed wafer map classification on the WM-811K dataset by combining a dual-channel CNN and an ECOC-SVM classifier in [ 10 ]. Zheng et al performed wafer map classification by comparing ML-based and DL-based models, demonstrating that DL-based models achieve better performance [ 11 ]. In [ 12 ], high accuracy was achieved by image augmentation with G2LGAN followed by wafer map classification with MobileNetV2 classifier.…”

Research on the Strawberry Recognition Algorithm Based on Deep Learning

“…Image recognition is an important CV (computer vision) technology. In the past decade, DL has become the most important AI (artificial intelligence) technology in the field of image recognition, and its development and application have achieved tremendous achievements [4][5][6][7][8]; it is widely used in the field of transportation, such as license plate recognition [9] (pp. [1][2][3][4][5][6][7][8][9][10], unmanned obstacle avoidance [10], etc.…”

“…In the past decade, DL has become the most important AI (artificial intelligence) technology in the field of image recognition, and its development and application have achieved tremendous achievements [4][5][6][7][8]; it is widely used in the field of transportation, such as license plate recognition [9] (pp. [1][2][3][4][5][6][7][8][9][10], unmanned obstacle avoidance [10], etc. In terms of the development of DL in the field of agriculture, DJI and other brands of UAVs (Unmanned Aerial Vehicles) spray pesticides and fertilizer [11].…”

“…Xiao et al [13] (pp. [1][2][3][4][5][6][7][8][9][10][11][12] used a Swin transformer model to identify the maturity of apples and pears. This method has achieved good classification results, but the data set of this experiment is not extensive enough to show the real growth environment of fruits.…”

Inpainted Image Reconstruction Using an Extended Hopfield Neural Network Based Machine Learning System

“…Thus, deep learning approach can be seen as a special topic in optimization theory. Standard types of deep learning neural networks include the multilayer perceptrons (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN) [ 15 , 16 , 17 , 18 ]. However, optimal networks topology and implementation technology have not yet been selected (the generalizability of networks is not well understood, and there is a lack of explanation for the relationship between the network topology and performance [ 19 ].…”