Towards an adaptive minimum variance control scheme for specimen drift compensation in transmission electron microscopes

Transmission electron microscopes are the tools of choice in materials science, semiconductor, and biological research and it is expected that they will be increasingly used to autonomously perform high-volume, repetitive, nano-measurements in the near future. Thus, there is a clear need to develop automation strategies for these microscopes. This paper introduces an adaptive minimum variance control scheme to compensate specimen drift, a common cause of image blurring in long-exposure images. The controller, which is new in the electron microscope literature, makes use of ARMASA, a statistical toolbox designed to identify linear models from finite-length data sets, to generate ARMA models of the drift process `on-the-fly´. These models are then used as part of a controller designed to reduce the drift variance. The benefits of the proposed scheme, which can be quite substantial, are illustrated through a set of simulations that use a model of the drift present in a sequence of experimental images.