One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning probe tip states on both metal and non-metal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively.
翻译:扫描探测器显微镜所面临的最大障碍之一是不断需要纠正现场扫描探测器的缺陷,目前这是一个人工的耗时过程,从自动化中受益匪浅。这里我们引入了一个进化神经网络协议,能够自动识别金属表面和非金属表面的各种可取和不可取的扫描探测器尖点状态。通过将最佳性能模型结合为多数投票组合,我们发现H:Si(100)的理想状态可以被区分为平均精确度0.89,平均接收器-操作器-特性曲线区域0.95。 更一般地说,高和低质量提示可以被区分为平均精确度0.96,接近完美区域下游0.98。经过微小的修改,我们还成功地自动识别了奥(111)和库(111)表面的不可取和非地表特定状态。在这些情况下,我们发现平均精确度分别为0.95和0.75,下游区域0.94和下游区域0.94。