AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design
This work addresses the need for scalable and standardized metrology workflows in semiconductor manufacturing by bridging human insight with machine precision, though it is incremental as it builds on existing methods with modular integration.
The authors tackled the challenge of automating nanoscale analysis in semiconductor materials by developing a tunable human-AI-assisted workflow framework for STEM image analysis, resulting in a web platform that outputs statistical roughness and thickness metrics with nanometer precision.
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