AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design

• URL: http://arxiv.org/abs/2604.00359v1
• Date: Wed, 01 Apr 2026 01:13:09 GMT
• Title: AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design
• Abstract summary: We present a tunable human-AI-assisted workflow framework for device characterization.<n>The system integrates gradient-based peak detection with interactive correction modules.<n>It processes TEM/EMD files directly, applies noise reduction and interface tracking algorithms, and outputs statistical roughness and thickness metrics with nanometer precision.
• Score: 0.6055934915901143
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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