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Workshop: AI for Accelerated Materials Design (AI4Mat)
Experimental platform and digital twin for AI-driven materials optimization and discovery for microelectronics using atomic layer deposition
Angel Yanguas-Gil · Steve Letourneau · Noah Paulson · Jeffrey Elam
Keywords: [ microelectronics ] [ self-driving labs ] [ atomic layer deposition ] [ thin films ] [ accelerated materials design ] [ in-situ characterization ]
Atomic layer deposition (ALD) is a thin film growth technique that is key for both microelectronics and energy applications. Its step-by-step nature and its integration into fully automated clusters with wafer handling systems make is an ideal tool for AI-driven optimization and discovery. In this work we describe an experimental setup and digital twin of an ALD reactor coupled with in-situ characterization techniques that we have developed as a platform for the development and validation of novel algorithms for self-driving labs. Preliminary results show that it is possible to achieve a 100-fold reduction in the time required to optimize new processes. Finally we share some of the lessons learned during the design and validation of our self-driven thin film growth tool.