AI 中文总结
探讨电子材料表征的两种传统方式，介绍“自动驾驶”表征工具进展。基于AEcroscopyWave平台，通过创建控制硬件的API并整合AI方法，弥合行业规模自动化与高度定制系统差距，展示异构仪器供智能体使用的好处。
AI 中文摘要
传统上，电子材料表征分为两种不同方式：行业规模的自动化系统用于检测材料缺陷和确保质量（如半导体行业），以及高度定制、由操作员驱动且需人类专家的系统。前者通量高但灵活性有限，后者带宽受限但有研究级发现能力。“自动驾驶”表征工具的进展有望弥合二者差距，通过创建控制硬件的应用程序接口及整合AI方法实现自主化。本文讨论了AEcroscopyWave这一为智能AI时代定制的表征平台的最新进展，它能统一控制扫描探针显微镜及可编程外围仪器，通过测试案例展示了让异构科学仪器可供智能体访问、组合和使用的好处。
英文摘要
The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ensure quality (such as in the semiconductor industry), and highly customized, operator-driven systems requiring human experts. The former offers high throughput but limited flexibility, whereas the latter is heavily bandwidth-limited but provides research-grade discovery capabilities. Recent advances in "self-driving" characterization tools offer the potential to bridge the two stratified regimes, by the creation of application program interfaces (APIs) that can control hardware, and the integration of AI methods to incorporate autonomy into the process. Here, we discuss our latest developments in AEcroscopyWave, a custom-built characterization platform for the agentic-AI era, that provides unified control of scanning probe microscopes with programmable peripheral instrumentation, highlighting the design choices that are necessary for maximizing the capability of the system and the ease of use for both human and AI agents. The benefits of making heterogeneous scientific instruments accessible, composable and usable by agents is demonstrated by test cases.
Comments17 pages, 5 figures
