Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process

• Published in: Journal of intelligent manufacturing Vol. 31; no. 1; pp. 73 - 86
• Main Authors: Lee, Ki Bum, Kim, Chang Ouk
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2020; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Business and Management; Chemical-mechanical polishing; Control; Control equipment; Control systems; Deep learning; Drift; Electronics industry; Feature extraction; Machine learning; Machines; Manufacturing; Mathematical models; Mechatronics; Metrology; Neural networks; Organic chemistry; Performance prediction; Process control; Process controls; Processes; Production; Recurrent neural networks; Robotics; Semiconductors; System effectiveness; Time dependence; Virtual metrology; Chemical mechanical planarization; Recurrent neural network; Convolutional neural network; Advanced process control
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-018-1437-4

In semiconductor manufacturing, the chemical mechanical planarization (CMP) process produces higher thickness variability in the edge area of the wafer than that in the center area due to the characteristics of the polishing operation. To address this problem, advanced CMP equipment includes a function that controls the removal rate of each area. However, to take full advantage of this capability, effective advanced process control systems must be implemented with a virtual metrology (VM) model. However, the prediction performance of the VM model often deteriorates due to process drift. Here, we present a deep learning-based VM model that demonstrates high performance in the presence of nonlinear process drift. The proposed model combines a recurrent neural network and a convolutional neural network to extract time-dependent and time-independent features. A two-stage model training method is proposed that alternately updates the weights of each network to improve prediction performance. In the experiments using on-site CMP process data, the performance of the deep learning models exceeded that of standard machine learning models. The proposed model showed an 8.48% decrease in process variability relative to the best-performing machine learning model, which was elastic nets.