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.
Highlights
Nowadays, the semiconductor industry is developing rapidly, and more precise products are being designed and produced as a result of the great advances in technology
To evaluate the performance of the proposed prediction model, we compare the performance of the proposed deep learning-based convolutional neural network (DCNN) model with that of three state-of-the-art machine learning (ML) algorithms, logistic regression (LR), random forest (RF), gradient boosted decision trees (GBDT), and several well-known deep learning (DL)-based algorithms named VGGNet, ResNet, and EfficientNet [19,20,21,22,23,24]
This paper presents a DL-based convolutional neural network (DCNN) prediction model for the recognition of wafer map failure types based on the real-time wafer map image dataset
Summary
The semiconductor industry is developing rapidly, and more precise products are being designed and produced as a result of the great advances in technology. The simple recognition of the wafer map failure pattern types can be conducted by experienced semiconductor engineers for the detection of the actual causes of semiconductor failure This entire process is inefficient, expensive, and time-consuming. Deep learning (DL)-based approaches such as convolutional neural networks (CNN) have recently been used for image processing tasks in many domains [12,13,14,15,16]. This paper proposes a DL-based multi-class classification model using the deep convolutional neural network (DCNN) for the automatic recognition of wafer map failure pattern types during semiconductor manufacturing processes on the basis of real wafer map image data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have