Moreover, the robustness of the proposed method was demonstrated by autofocusing on unseen samples. The results of these dual networks showed successful autofocus performance on three trained samples. The ACNet can delicately control the focus of SEM online based on the AENet’s outputs for any lateral sample position and magnification. ![]() The AENet was designed to evaluate the quality of given images, with scores ranging from 0 to 9 regardless of the magnification. To broaden the usability of SEM, we propose an autofocus method for a SEM system based on a dual deep learning network, which consists of an autofocusing-evaluation network (AENet) and an autofocusing-control network (ACNet). Only trained operators can use SEM equipment properly, meaning that the use of SEM is restricted. ![]() ![]() However, conducting SEM is rather complex due to the nature of using an electron beam and the many parameters that must be adjusted to acquire high-quality images. Scanning electron microscopy (SEM) is a high-resolution imaging technique with subnanometer spatial resolution that is widely used in materials science, basic science, and nanofabrication.
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