Oppliger Jens, Zengin Berk, Liu Danyang, Hauser Kevin, Witteveen Catherine, von Rohr Fabian, Natterer Fabian Donat
Department of Physics, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland.
Department of Physics, Harvard University, 17 Oxford Street Cambridge, MA 02138, United States of America.
MethodsX. 2022 Jul 13;9:101784. doi: 10.1016/j.mex.2022.101784. eCollection 2022.
Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes. • Accumulative sampling for unknown degree of sparsity • Controllably interrupt and resume QPI measurements • Scattering wave conserving background subtractions.
准粒子干涉成像（QPI）通过局域态密度（LDOS）图的傅里叶变换，为量子材料的能带结构提供了深入见解。传统上，使用扫描隧道显微镜获取这些图像很繁琐，因为需要进行大量测量，可能需要几天才能完成。最近关于QPI成像的稀疏采样演示表明，通过仅对总LDOS的一个小的随机子集进行采样，可以从根本上减少有效测量时间。然而，为了忠实地恢复QPI图像所需的子采样量仍然是一个反复出现的问题。在这里，我们引入了一种自适应稀疏采样（ASS）方法，即我们逐渐积累稀疏采样的LDOS测量值，直到通过压缩感知恢复达到所需的质量水平。LDOS的迭代测量随机子集可以与用于图像配准和漂移校正的常规形貌图像交错。这些参考形貌还允许恢复中断的测量，以进一步提高QPI质量。我们的ASS方法是对准粒子干涉成像的一种便捷扩展，应该会消除在实施稀疏采样映射方案时的进一步犹豫。• 对未知稀疏度进行累积采样 • 可控地中断和恢复QPI测量 • 散射波守恒背景减法
