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 W Elam

Keywords: self-driving labs, atomic layer deposition, accelerated materials design, in-situ characterization, microelectronics, thin films

TL;DR: experimental setup and digital twin for a self-driving ALD tool, including an example of AI-driven process optimization providing a x100 acceleration in process development

Abstract: 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.

Paper Track: Behind the Scenes

Submission Category: AI-Guided Design, Automated Material Characterization