Fundamental Understanding of Residual Stress in Binary and Ternary Nitride Thin Films: Measurements and Modeling

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NON-TECHNICAL DESCRIPTION: Nitride thin films are widely used in applications such as protective tool coatings and microelectronic interconnects. However, the deposited films often have large amounts of residual stress that can reduce their performance or lead to failure. A more fundamental understanding of the connection between the film deposition conditions and the resulting stress is needed in order to control and predict the stress. This understanding is achieved by performing comprehensive measurements and using them to develop a model that can predict the stress under different conditions. This provides a science-based method to choose the processing parameters instead of trial-and-error. Sharing this model with others impacts the broad community of thin film users by providing a tool to analyze and optimize their growth processes. The students who are trained on this project develop a deep understanding of the kinetic processes controlling thin film growth that are valuable in industries such as automotive components and semiconductor processing. TECHNICAL DETAILS: The research on stress in nitride thin films integrates measurements with the development of a quantitative model. The measurements are performed in real-time during the deposition of the films using an optical technique. The different processing parameters (growth rate, pressure, nitrogen flow rate) are systematically varied so that a comprehensive set of data is generated. The measurements are done on both binary nitrides (nitride with a single transition metal species) and ternary nitrides (nitride with two transition metal species) with different compositions. The data is used to develop a set of rate equations based on the underlying kinetic mechanisms that can calculate the stress for different processing conditions. Applying the model equations to the experiments allows a set of kinetic parameters to be extracted for each material. This is important for interpreting the data and understanding the processes controlling the stress. It also transforms the method of choosing processing conditions by predicting parameters that can produce the desired stress. A user-friendly web-based version of the model can be used by others to analyze their own results and select the best processing conditions for their needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.