Angel Yanguas-Gil, Argonne National Laboratory, Lemont, IL
Fast process optimization is critical to help reduce the cost of development and adoption of novel thin film-based technologies. Examples include energy technologies, where low-cost manufacturing is key to ensure commercial viability, and microelectronics, where longer processing times, with substrates sometimes spending weeks in a fab before reaching a specific step, higher complexity, and ever stringent requirements compound the cost of innovation at the leading technology nodes. Current approaches to transfer technologies from lab to manufacturing often require extensive tool time and characterization or, when assisted by simulations, accurate models carefully tuned to each specific process.
In this presentation I will explore how machine learning can be leveraged to help accelerate the optimization of atomic layer deposition. In particular, I will highlight two different approaches: the first one explores the use of surrogate models to connect experimental metrology data with optimal processing conditions. We have explored two different cases: optimizing a process within a reactor and optimizing process transfer to a different reactor. In both cases, we show that, for thermal ALD processes, the information contained in thickness profiles in undersaturated conditions is enough to help predict optimal dose times both within and across different reactors. We also extended this methodology to the case of plasma-assisted deposition processes. The second approach relies on the use of in-situ characterization techniques to design self-driving deposition tools that can automatically search and identify optimal process conditions. For this approach, we developed a two-step process where algorithms are tested first using simulations and digital twins of the reactors before being experimentally deployed. This methodology can lead to x100 faster process optimization compared to standard growth-vent-characterize optimization cycles.