| AVS 64th International Symposium & Exhibition | |
| Scanning Probe Microscopy Focus Topic | Monday Sessions |
| Session SP+2D+AS+NS+SS-MoA |
| Session: | Probing Electronic and Transport Properties |
| Presenter: | Rama Vasudevan, Oak Ridge National Laboratory |
| Authors: | S. Somnath, Oak Ridge National Laboratory K. Law, Oak Ridge National Laboratory R. Archibald, Oak Ridge National Laboratory S.V. Kalinin, Oak Ridge National Laboratory S. Jesse, Oak Ridge National Laboratory R. Vasudevan, Oak Ridge National Laboratory |
| Correspondent: | Click to Email |
Current-voltage (IV) curve acquisition is the oldest and most common spectroscopic method implemented on virtually every scanning probe microscope (SPM) available. Though in use for three decades, the basic measurement has not altered substantially in this time-frame, with the current being detected during DC pulses applied to the SPM tip. Such measurements include both a delay time after each DC voltage change (to reduce parasitic capacitance influence), as well as a an integration time, to reduce noise, limiting typical measurements to a few Hz at most. Here, we introduce a new method for IV curve acquisition, based on an AC-excitation of the SPM tip, in combination with full information acquisition from the current amplifier and Bayesian inference. IV curves are acquired on a model ferroelectric system, at rates ~500x faster than the current state of the art, with higher spatial and spectral resolution. The obtained results offer a complementary channel of information to supplement existing piezoresponse force microscopy studies, allowing to probe disorder at the nanoscale. Bayesian inference further allows quantification of the capacitance contribution, which can be utilized to estimate the dielectric constant of the ferroelectric, with results agreeing with reported values. These studies highlight the utility of both complete information acquisition, and Bayesian inference, in dramatically increasing the acquisition rates of data from SPM.
This research was sponsored by the Division of Materials Sciences and Engineering, BES, DOE (RKV, SVK, SS). This research was conducted and partially supported (SJ) at the Center for Nanophase Materials Sciences, which is a US DOE Office of Science User Facility. Bayesian inference portion was sponsored by the Applied Mathematics Division of ASCR, DOE; in particular under the ACUMEN project (KJHL, RA).