Sergei V. Kalinin

Dynamic Modes in Kelvin Probe Force Microscopy: Band Excitation and G-Mode

Since its invention in 1991, Kelvin Probe Force Microscopy (KPFM) has developed into the primary tool used to characterize electrical phenomena on the nanometer scale, with multiple applications for transport, ferroelectric, biological, organic and inorganic photovoltaics, amongst a myriad of other materials. At the same time, this multitude of applications is underpinned by a relatively simple detection scheme utilizing the classical lock-in signal detection combined with tip bias feedback. It has been widely recognized that this detection scheme has several limitations, including influences of the experimental parameters (e.g. driving amplitude, feedback gains, phase offset) as well as loss of information on other material properties (e.g. capacitance and its bias dependence and time-dependent responses). In this chapter, we review the operational principles of KPFM, briefly overview the existing excitation schemes beyond the classical lock-in—feedback principle, and discuss at length the implementations and applications of KPFM based on band excitation and the full information capture embodied in general mode (G-Mode). The future potential pathways for development of detection in KPFM are discussed.

Rapid Measurement of I-V Curves via Complete Information Acquisition

Current-voltage (I-V) measurements are perhaps the oldest spectroscopic technique in scanning probe microscopy. The measurement of I-V (as well as derivatives dI/dV, etc.) has been instrumental in revealing surface states, electronic structures, quasiparticle interference patterns, and electronic modulations on surfaces of materials with nanoscale (and even atomic) resolution for decades. The key advantage is the ability to measure the properties as a function of spatial position, which is typically undertaken by dividing the area of interest into a spectroscopic array (grid) of points, and then positioning the SPM tip at each of these grid sites for the experiment. The main drawback to most spectroscopic techniques, however, remains the time taken for acquisition. In fact, the most basic of all measurements – the acquisition of a current-voltage (I-V) curve in SPM remains extremely slow, typically taking several seconds per measurement. Here, we show a new method based on complete information acquisition to increase this rate by 100-1000x.

Phase determination from atomically resolved images: physics-constrained deep data analysis through an unmixing approach

It is now commonplace to obtain atomically-resolved, real-space images on materials through both scanning tunneling microscopy as well as scanning transmission electron microscopy, and such data is invaluable to determining a host of interactions of order parameters with defects, observe surface reconstructions and nanoscale phase separations, determine the atomic structure of interfaces such as grain boundaries, dislocation cores and domain walls, etc. However, analysis techniques that seek to automate the phase determination and identification remain absent: in fact, it is already becoming impossible to manually identify and analyze all collected images from a single sitting on a microscope, which can yield several ~Gb worth of image data. As such, the need for such techniques is paramount, and here we present our approach towards tackling this problem

Big, deep, and smart data from atomically resolved images: exploring the origins of materials functionality

1. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge TN 37831 USA 2. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831 USA 3. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA 4. Joint Institute for Computational Sciences, ORNL-University of Tennessee, ORNL, Oak Ridge, TN 37831 USA