AI辅助人工交互的网页平台，实现硬盘多层膜结构的纳米级厚度与界面粗糙度自动分析。
AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design
- 结合梯度峰检测与交互修正模块，实现人机协同的自适应分析流程。
- 可对多层薄膜进行纳米精度的厚度和界面粗糙度统计，处理速度快且结果稳定。
- 适合半导体制造中的材料表征团队，支持快速部署与标准化分析。
透射电子显微镜（STEM）已成为半导体材料计量的核心工具，能够对决定器件性能的复杂多层结构进行纳米尺度分析。构建高效的计量工作流需在自动化与灵活性之间取得平衡：僵化的流程对样本差异敏感，而纯人工方法则耗时且主观。本文提出一种可调的人机协同工作流框架，支持模块化、自适应的STEM图像分析，用于器件表征。以多层薄膜为例，展示了自动化的层厚与界面粗糙度量化流程。系统融合基于梯度的峰检测与交互式修正模块，在设计阶段引入人工干预，同时确保跨样本的全自动执行。该平台为Web端应用，可直接处理TEM/EMD文件，执行降噪与界面追踪算法，输出具有纳米级精度的粗糙度与厚度统计指标。该架构为可复用、可扩展的计量流程提供了通用范式，实现了人类洞察与机器精度的融合，适用于半导体制造中的规模化、标准化分析。代码已开源：https://github.com/utkarshp1161/thickness-mapping-webapp
原文摘要 · Abstract (English)
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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