AutoRASOR：自主快速扫描电子显微镜操作员
AutoRASOR: Autonomous Rapid Scanning Electron Microscope Operator
- University of Toronto（多伦多大学）
- Hitachi High-Tech Canada, Inc.（日立高新加拿大公司）
- CanmetMATERIALS, Natural Resources Canada（加拿大自然资源部CanmetMATERIALS研究中心）
- Data Science Institute, University of Toronto（多伦多大学数据科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
AutoRASOR提出一种任务无关的自主扫描电镜流程，利用视觉基础模型和主动学习策略，无需预训练即可高效捕获未知样品的多尺度形态，生成信息丰富的多尺度数据集。
AI中文摘要：
扫描电子显微镜（SEM）是表征材料微观结构的基础技术，而微观结构决定了材料的许多基本物理和化学性质。随着自驱动实验室（SDLs）的兴起，样品现在以大批量方式合成，这要求同样高通量的自主表征。现有的自动化电子显微镜流程是任务特定的：它们检测预定义的特征、优化已知属性，或需要样品的先验知识，从而将每个流程限制在其所构建的材料系统上。我们引入了AutoRASOR，一个任务无关的自主SEM流程，它无需领域特定的预训练、微调或人工提示，即可捕获未知样品的多尺度形态。AutoRASOR使用视觉基础模型（VFM）DINOv3实时嵌入显微图像，并通过两种互补策略选择感兴趣区域（ROIs）：潜在最远点采样（LFPS）和基于形态模糊性的主动学习，该模糊性是指在给定较低放大倍率外观下未解析的细尺度特征的条件方差。基于模糊性的主动学习比随机ROI选择持续捕获更多样化和稀有的形态，而LFPS在有限的采集预算内可靠地恢复样品的形态分布，这在真实SEM显微图像和合成相场图像上均得到了测试。通过将自主表征从预定义的、任务特定的特征目标转向形态调查，AutoRASOR为多样化的下游任务生成信息丰富的多尺度数据集。无需领域特定的预训练或先验材料假设，该框架可以开箱即用地部署，以表征SDLs产生的新材料。
英文摘要：
Scanning Electron Microscopy (SEM) is a foundational technique for characterizing material microstructure, which dictates many fundamental physical and chemical properties. With the rise of Self-Driving Labs (SDLs), samples are now synthesized in large batches, demanding equally high-throughput, autonomous characterization. Existing automated electron microscopy pipelines are task-specific: they detect pre-defined features, optimize known properties, or require prior knowledge of the sample, restricting each pipeline to the material system it was built for. We introduce AutoRASOR, a task-agnostic autonomous SEM pipeline that captures the multi-scale morphology of an unknown sample without domain-specific pre-training, fine-tuning, or human prompting. AutoRASOR embeds micrographs in real time with a vision foundation model (VFM), DINOv3, and selects regions of interest (ROIs) through two complementary policies: Latent Farthest Point Sampling (LFPS) and active learning on morphological ambiguity, which is the conditional variance of unresolved fine-scale features given lower magnification appearance. Active learning on ambiguity consistently captures more diverse and rare morphologies than random ROI selection, while LFPS reliably recovers the specimen's morphological distribution within a limited capture budget, tested both on real SEM micrographs and synthetic phase-field images. By shifting autonomous characterization from pre-defined, task-specific targeting of features to morphological survey, AutoRASOR generates information-rich multi-scale datasets for diverse downstream tasks. Without the need for domain-specific pre-training or prior material assumptions, this framework can be deployed out-of-the-box to characterize novel materials produced by SDLs.
