🤖 AI Summary
This study addresses the limitations of rigid automated pipelines and inefficient, subjective manual approaches in analyzing semiconductor multilayer thin films from scanning transmission electron microscopy (STEM) images. To overcome these challenges, the authors propose a tunable human–AI collaborative metrology workflow that integrates human prior knowledge with algorithmic automation. The modular framework incorporates gradient peak detection, noise suppression, interface tracking, and interactive correction algorithms, enabling human intervention during design while supporting fully automatic execution during runtime. Operating directly on TEM/EMD file formats, the system delivers nanometer-precision measurements of layer thickness and interface roughness with statistical rigor. By balancing flexibility with automation, the approach enhances both analytical efficiency and consistency. The implementation is open-source, and the architecture is designed for reusability and extensibility.
📝 Abstract
Scanning transmission electron microscopy (STEM) has become a cornerstone instrument for semiconductor materials metrology, enabling nanoscale analysis of complex multilayer structures that define device performance. Developing effective metrology workflows for such systems requires balancing automation with flexibility; rigid pipelines are brittle to sample variability, while purely manual approaches are slow and subjective. Here, we present a tunable human-AI-assisted workflow framework that enables modular and adaptive analysis of STEM images for device characterization. As an illustrative example, we demonstrate a workflow for automated layer thickness and interface roughness quantification in multilayer thin films. The system integrates gradient-based peak detection with interactive correction modules, allowing human input at the design stage while maintaining fully automated execution across samples. Implemented as a web-based interface, it processes TEM/EMD files directly, applies noise reduction and interface tracking algorithms, and outputs statistical roughness and thickness metrics with nanometer precision. This architecture exemplifies a general approach toward adaptive, reusable metrology workflows - bridging human insight and machine precision for scalable, standardized analysis in semiconductor manufacturing. The code is made available at https://github.com/utkarshp1161/thickness-mapping-webapp