演示用于自主高分辨率扫描显微镜的AI驱动工作流程
Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy
摘要：
随着科学仪器的不断进步，扫描显微镜现在能够以亚原子水平的空间分辨率和亚皮秒时间分辨率对物理系统进行成像。相应地，它们正在产生越来越多的数据，存储和分析这些数据变得越来越困难。解决这一挑战的一种方法是通过自动驾驶实验技术，该技术可以主动分析收集的数据，并使用这些信息进行实时测量选择，这样收集的数据虽然稀疏，但可以代表样本，并提供足够的信息。在这里，我们报告了快速自主扫描工具包（FAST），它结合了经过训练的神经网络，路由优化技术和高效的硬件控制方法，以实现自动驾驶扫描显微镜实验。我们的方法的主要特点是：它不需要任何有关样品的先验信息，它有一个非常低的计算成本，它使用通用的硬件控制与最小的实验特定的包装。我们在数值实验和$WSe_2$薄膜的扫描暗场X射线显微镜实验中测试该工具包，其中我们的实验表明，<25%的样品的FAST扫描足以产生高保真图像和样品中的表面变形的定量分析。我们表明，FAST可以自主识别样品中感兴趣的所有特征，同时显着减少扫描时间，所采集的数据量和样品上的剂量。FAST工具包很容易应用于任何扫描显微镜模式，我们预计采用这种技术将使更广泛的多层次研究物理现象的演变与时间，温度或其他实验参数。
英文摘要：
With the continuing advances in scientific instrumentation, scanning microscopes are now able to image physical systems with up to sub-atomic-level spatial resolutions and sub-picosecond time resolutions. Commensurately, they are generating ever-increasing volumes of data, storing and analysis of which is becoming an increasingly difficult prospect. One approach to address this challenge is through self-driving experimentation techniques that can actively analyze the data being collected and use this information to make on-the-fly measurement choices, such that the data collected is sparse but representative of the sample and sufficiently informative. Here, we report the Fast Autonomous Scanning Toolkit (FAST) that combines a trained neural network, a route optimization technique, and efficient hardware control methods to enable a self-driving scanning microscopy experiment. The key features of our method are that: it does not require any prior information about the sample, it has a very low computational cost, and that it uses generic hardware controls with minimal experiment-specific wrapping. We test this toolkit in numerical experiments and a scanning dark-field x-ray microscopy experiment of a $WSe_2$ thin film, where our experiments show that a FAST scan of <25% of the sample is sufficient to produce both a high-fidelity image and a quantitative analysis of the surface distortions in the sample. We show that FAST can autonomously identify all features of interest in the sample while significantly reducing the scan time, the volume of data acquired, and dose on the sample. The FAST toolkit is easy to apply for any scanning microscopy modalities and we anticipate adoption of this technique will empower broader multi-level studies of the evolution of physical phenomena with respect to time, temperature, or other experimental parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
