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
@article{arxiv.2604.00359,
title = {AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design},
author = {Utkarsh Pratiush and Huaixun Huyan and Maryam Zahiri Azar and Esmeralda Yitamben and Allen Bourez and Sergei V Kalinin and Vasfi Burak Ozdol},
journal= {arXiv preprint arXiv:2604.00359},
year = {2026}
}