An Open-Source Hardware and Software Toolkit to Enable Agentic RHEED-Guided Thin-Film Synthesis

Abstract

Reflection high-energy electron diffraction (RHEED) provides rich information about evolving surfaces during thin-film growth, but non-automated, operator-dependent alignment and fragmented analysis workflows limit its potential in fully autonomous synthesis. Here, we present an open-source hardware and software toolkit that makes RHEED control and quantitative analysis accessible to operators and artificial intelligence (AI) agents. Demonstrated on a pulsed laser deposition system, the toolkit provides programmable electron-optics control, automated beam alignment and rocking-curve acquisition, and a training-free method for crystallographic azimuthal alignment. The auto RHEED application extracts structural and growth-related observables through shared graphical and programmatic interfaces, including a Model Context Protocol (MCP) server. An agent-driven demonstration shows how natural-language requests can guide the selection, configuration, and execution of quantitative analyses. An extensible adapter interface streamlines the incorporation of community-developed methods for AI analysis and RHEED simulation as they emerge, supported by Markdown implementation guides designed for AI coding agents. These capabilities support complementary descriptions of surface evolution through physical measurements and learned image representations while providing a practical route for incorporating new computational methods. Together, these tools reduce barriers to automated and agentic RHEED measurements and establish a foundation for future agentic control of thin-film synthesis guided by the evolving surface.