发表机构
University of Tennessee; Pennsylvania State University（田纳西大学; 宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出多任务扫描探针显微镜，基于多任务高斯过程的闭环工作流程，实现测量位置与模态的自主选择，在AlScN晶圆上验证了其可扩展主动学习应用的价值。
AI 中文摘要
扫描探针显微镜可实现对材料的结构、电学、机电、磁学及力学性质的纳米级表征。其在晶圆级表征和组合材料探索中的应用日益广泛，因此需要在大空间域内高效分配测量任务。当可用测量模态的采集时间和针尖与样品的损伤潜力存在差异时，这一点尤为重要，因为这会导致对空间网格进行详尽的多模态映射不切实际。在此，我们展示了多任务扫描探针显微镜，这是一种实时闭环工作流程，其中多任务高斯过程可学习空间及跨模态关系，并自主选择下一个测量位置和下一个实验方案。该方法在自动化大样品原子力显微镜上实现，并在成分梯度AlScN晶圆上使用轻敲模式和双交流共振跟踪（DART）测量进行验证。配对的初始测量建立了任务间的关系，之后非重合测量用于更新响应图谱。所得工作流程将扫描探针显微镜中的主动学习从空间采样扩展到测量模态的自主分配，为将快速、弱扰动成像与较慢的接触式、电学、机电、磁学或光谱测量相结合提供了基础。
英文摘要
Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-scale characterization and combinatorial materials exploration creates a need to distribute measurements efficiently across large spatial domains. This is particularly important when available modalities differ in acquisition time and potential for tip and sample damage, making exhaustive multimodal mapping over spatial grids impractical. Here, we demonstrate multitask scanning probe microscopy, a live, closed-loop workflow in which a multitask Gaussian process learns spatial and cross-modal relationships and autonomously selects both the next measurement location and the next experimental protocol. The approach is implemented on an automated large-sample atomic force microscope and demonstrated on a composition-spread AlScN wafer using tapping-mode and Dual AC Resonance Tracking (DART) measurements. Paired initial measurements establish the relation between the tasks, after which noncoincident measurements are used to update both response landscapes. The resulting workflow extends active learning in scanning probe microscopy from spatial sampling to autonomous allocation of measurement modalities and provides a basis for combining rapid, weakly perturbative imaging with slower contact, electrical, electromechanical, magnetic, or spectroscopic measurements.
