Automatic beam optimization method for scanning electron microscopy based on electron beam Kernel estimation

Scanning Electron Microscopy (SEM) leverages electron wavelengths for nanoscale imaging, necessitating precise parameter adjustments like focus, stigmator, and aperture alignment. However, traditional methods depend on skilled personnel and are time-consuming. Existing auto-focus and auto-stigmation techniques face challenges due to interdependent nature of these parameters and sample diversity. We propose a beam kernel estimation method to independently optimize SEM parameters, regardless of sample variations. Our approach untangles parameter influences, enabling concurrent optimization of focus, stigmator x, y, and aperture-align x, y. It achieves robust performance, with average errors of 1.00 μm for focus, 0.30% for stigmators, and 0.79% for aperture alignment, surpassing sharpness-based approach with its average errors of 6.42 μm for focus and 2.32% for stigmators and lacking in aperture-align capabilities. Our approach addresses SEM parameter interplay via blind deconvolution, facilitating rapid and automated optimization, thereby enhancing precision, efficiency, and applicability across scientific and industrial domains. Yunje Cho and colleagues improve the resolution of scanning electron microscopes via high-precision auto-focus and auto-stigmation. Their method operates without pre-existing knowledge about the sample.