Next generation electronic devices require the use of improved materials and very precise material processing techniques. To reduce feature sizes and improve energy efficiency, the devices employ extremely thin layers, high aspect ratios, atomically-sharp interfaces, or any combination thereof. Due to inherent difficulties in applying real-time in situ monitoring and control of film properties, factory operators typically rely on batch thin film deposition and etching cycles, followed by scanning electron microscopy (SEM) or x-ray photoelectron spectroscopy (XPS) characterization of the deposited thin films to determine the effect of controllable reactor parameters on the resulting product. This empirical approach reduces productivity and fails to provide complete data on the behavior and operation of chambered reactors common to thin-film processing. Multiscale computational fluid dynamics (CFD) modeling provides a means for addressing these concerns by reducing empiricism and allowing the development of complete data sets that can be used to optimize and control reactor operating conditions in real time.

The proposed research is exploratory in nature and focuses on developing a multiscale CFD modeling and control framework that can enable control of thin film manufacturing via plasma-enhanced atomic layer deposition (PE-ALD) to optimize in real time the microstructure of the deposited thin films. CFD models have been shown to capture the complex reaction and transport phenomena present within plasma charged reactors, while microscopic models, typically based on kinetic Monte Carlo (kMC) algorithms, have successfully reproduced the surface features of deposited films. A multiscale CFD model encompassing both domains would represent a significant step forward in understanding of thin-film processing via PE-ALD and could allow for improved real-time online monitoring and control of chambered reactor operations. However, such a model will be unsuitable for the development of real-time optimizers and model-based controllers because CFD simulations are generally computationally demanding and cannot be linked to online model predictive control schemes. Nonetheless, the proposed multiscale CFD model can be used as a risk-free and effective tool to investigate previously unexplored operating conditions of the PE-ALD reactor and create a database which can be utilized to derive a computationally efficient data-driven model for PE-ALD real-time control. The multiscale CFD model which will be developed will allow for the application of a novel, computationally efficient data-based Bayesian artificial neural network (ANN). Furthermore, data-driven models developed using the reactor model will form the basis for real-time process optimization and control. The data-based model will be used to develop real-time operational decision strategies for PE-ALD that reduce thin film layer deposition times, which constitutes a necessary step for adoption of this technology. The proposed methodology can form a basis for real-time optimization and control of next generation deposition systems and may be adapted to a wide range of industrial processes. Dissemination of research results will include web-based access to a database and results repository. In addition to training a PhD student, the PI plans to integrate research results into the curriculum through the inclusion of CFD modeling and its integration with control in the Advanced Process Control course that the PI offers to both graduate and undergraduate UCLA students, as well as through the integration of CFD concepts and tools into the undergraduate numerical methods, process design and process control core courses.

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.

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University of California Los Angeles
Los Angeles
United States
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