Machine learning-based virtual metrology on film thickness in amorphous carbon layer deposition process. (August 2021)

• Record Type: Journal Article
• Authors: Choi, Jeong Eun; Hong, Sang Jeen
• Is Part Of: Measurement. Volume 16(2021)
• Publication Date: 2021-08
• Subjects: Virtual metrology -- Amorphous carbon layer -- Machine learning -- Optical emission spectroscopy -- Neural network; Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2021.100046
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: A stringent manufacturing process control is important to achieve reliable process control in nanoscale semiconductor manufacturing processes. Based on the advanced process control mechanisms in advanced semiconductor manufacturing, virtual metrology (VM) can be used to reduce the time and cost incurred in conventional metrology. This paper proposes a VM model based on the neural network (NN) architecture for predicting film thickness in amorphous carbon layer deposition processes, which is used for etch hard mask in high-aspect-ratio etch processes. A series of deposition experiments was conducted using the Box–Behnken design. The equipment status data and in situ optical emission spectroscopy (OES) sensor data were collected. Conventional recipe-based process modeling with the equipment status data showed a reasonable correlation with the deposited film thickness, and the augmentation of in situ plasma chemistry information through OES improved the machine learning accuracy with artificial neural networks. Consequently, our model's prediction accuracy was approximately 99.5% for R 2 when both equipment status data and OES sensor data were used as training data, as compared to when only equipment status data were used to train the model. Therefore, we recommend that in situ OES sensor data, as well as equipment status data, should be used in training NN-based VM models for high prediction accuracy.