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

TL;DR

This paper introduces a flexible, AI-assisted web platform for analyzing STEM images to measure layer thickness and interface roughness in semiconductor multilayer structures, combining automation with human oversight.

Contribution

It presents a modular, adaptive workflow framework that integrates AI algorithms with human input, enabling scalable and standardized analysis in semiconductor metrology.

Findings

Automated layer thickness and roughness quantification with nanometer precision.

A web-based interface that processes TEM/EMD files directly.

The system balances automation and human correction for flexible analysis.

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