Kalman Filter-Based Estimation of Surface Conductivity and Surface Variations in Scanning Tunneling Microscopy

In this article, we present a novel method for decoupling surface electronic properties from topographic surface variations in scanning tunneling microscope (STM). In a conventional STM image, these surface properties are mixed, and separating them is nontrivial. We perform closed-loop system identification to obtain a dynamic model of the STM. We then use this model in conjunction with a Kalman filter (KF) and a disturbance observer to estimate surface conductivity,
$\sigma $
, and surface variations,
$h$
, from the measurements obtained during a scan. We experimentally verify this method by first performing hydrogen depassivation lithography on a
$\mathrm {Si(100)-2\times 1:H}$
passivated surface to demonstrate the effectiveness of our proposed method in accurately estimating surface conductivity and surface height variations.