Affiliations: [a] Electrical and Electronics Engineering, Centre for IoT and AI (CITI), KPR Institute of Engineering and Technology, Coimbatore, India
| [b] Electronics and Communication Engineering, Kathir College of Engineering, Coimbatore, India
| [c] Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, India
| [d] Computer and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India
Correspondence:
[*]
Corresponding author. Palanichamy Naveen, Electrical and Electronics Engineering, Centre for IoT and AI (CITI), KPR Institute of Engineering and Technology, Coimbatore 641407, India. E-mail: naveenamp88@gmail.com..
Note: [1] Fault diagnosis in semiconductor manufacturing processes using a CNN-based generative adversarial network.
Abstract: In the current industry, quality inspection in semiconductor manufacturing is of immense significance. Significant achievements have been made in fault diagnosis in fabricated semiconductor wafer manufacturing due to the development of machine learning. Since real-time intermediate signals are non-linear and time-varying, the signals undergo various distortions due to changes in equipment, material, and process. This leads to a drastic change in information in intermediate signals. This paper presents a fault diagnosis model for semiconductor manufacturing processes using a generative adversarial network (GAN). The study aims to address the challenges associated with efficient and accurate fault identification in these complex processes. Our approach involves the extraction of relevant components, development of a paired generator model, and implementation of a deep convolutional neural network. Experimental evaluations were conducted using a comprehensive dataset and compared against six existing models. The results demonstrate the superiority of our proposed model, showcasing higher accuracy, specificity, and sensitivity across various shift tasks. This research contributes to the field by introducing a novel approach for fault diagnosis, paving the way for improved process control and product quality in semiconductor manufacturing. Future work will focus on further optimizing the model and extending its applicability to other manufacturing domains.
