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

arXiv: https://arxiv.org/abs/2604.00359 · HTML: https://mlsanity.com/paper/2604.00359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations](https://mlsanity.com/paper/2108.08882/) (2021)
- [Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation](https://mlsanity.com/paper/2405.12300/) (2024)
- [SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation](https://mlsanity.com/paper/2404.06638/) (2024)
- [Building Workflows for Interactive Human in the Loop Automated Experiment (hAE) in STEM-EELS](https://mlsanity.com/paper/2404.07381/) (2024)
- [Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments](https://mlsanity.com/paper/2409.12462/) (2024)
- [Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images](https://mlsanity.com/paper/2203.13875/) (2022)
- [Segment Anything Model for Grain Characterization in Hard Drive Design](https://mlsanity.com/paper/2408.12732/) (2024)
- [On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition](https://mlsanity.com/paper/2403.16230/) (2024)

## Abstract (arXiv)

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