Abstract
Transmission electron microscopes (TEMs) 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. In particular, an important feature in need of automation is specimen drift compensation, which is a common cause of image blurring in long-exposure TEM images, especially at high magnifications. In this paper, a systematic online approach to specimen drift compensation, called adaptive minimum variance control, is discussed in detail. The method makes use of an identified drift model, continuously updated from online drift measurements, to predict and ameliorate future drift values, significantly reducing their variance. The method's performance, measured in terms of drift variance reduction, is illustrated using both experimental and simulated data, and it is then compared with the performance of two pragmatic model-free methods: last data point prediction and linear extrapolation prediction.
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