Predictive data analytics and machine learning enabling metrology and process control for advanced node IC fabrication

• Main Authors: Rana, Narender, Yunlin Zhang, Wall, Donald, Dirahoui, Bachir
• Format: Conference Proceeding
• Language: English
• Subjects: Capacitance; Critical Dimension Atomic Force Microscopy (CD-AFM); Critical Dimension Scanning Electron Microscopy (CD-SEM); Data models; Deep Trench Capacitance and Metal Line Resistance; Electrical CD (ECD); Hybrid Metrology (HM); Machine Learning (ML); Metrology; Model Based Infrared Reflectometry (MBIR); Neural Network (NN); Neural networks; Optical Critical Dimension Metrology (OCD); Partial Least Square regression (PLS); PM: Predictive Metrology (PM); Predictive models; Principal Components Analysis (PCA); Resistance; Semiconductor device modeling
• ISSN: 1078-8743; 2376-6697
• DOI: 10.1109/ASMC.2015.7164502

Processor technology is going through multiple changes in terms of patterning techniques (multipatterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tighter controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Predictive metrology and analytics offer Multivariate data with non-linear trends and complex correlations generally cannot be described well by mathematical models but can be relatively easily learned by computing machines and used to predict or extrapolate. In this paper we present the application of machine learning and analytics to accurately predict the electrical performance of deep trenches and metal lines. Machine learning models can be used in process control where, for example, the electrical test results are predicted early in the processing flow invoking appropriate actionable decisions. It is demonstrated that metal line resistance can be modeled directly by the raw reflectance spectra obtained using scatterometry tool. This obviates the need to make complex geometrical models to measure the CDs and then establishing the correlation of CDs to resistance. It is shown that dimensional parameters such as height and CD can be derived from the predicted electrical measurements. Such information can be used in feedforward or feedback flow to optimize, control or monitor processes in fab. Results show improved correlation of neural network model predicted deep trench capacitance to the measured capacitance compared to the capacitance predicted by multivariate linear regression model that is currently in use. This paper presents the concept of predictive metrology with the use of machine learning and predictive analytics for CD and electrical test predictions. Predictive metrology can be used in conjunction with hybrid metrology to enable APC and novel metrology pathways in gap areas in the advanced semiconductor research, development and manufacturing.