SPARSE -- Efficient High-Resolution SEM Imaging of Rare Microstructural Features Across Large Areas by Selective Rescanning

Abstract

Characterisation of rare microstructural features in scanning electron microscopy (SEM) requires imaging large areas at high resolution. This leads to prohibitively long acquisition times. We present an open-source Python framework that addresses this bottleneck through a two-stage approach: a fast scan identifies regions of interest, which are then selectively rescanned with imaging parameters suitable for quantitative analysis. The framework defines a generic microscope interface and a modular detection interface, allowing adaptation to different microscope platforms and detection methods. Scanning, detection, and rescanning are parallelized using separate processes, ensuring that computation time does not extend acquisition time. The two processes communicate exclusively through queues, avoiding shared mutable state and eliminating the need for explicit synchronization. We validate the framework on damage detection in dual-phase DP800 steel using a Tescan Clara SEM. For a representative configuration a detection rate of 99 % is achieved at approximately 58 % of the conventional acquisition time. At 95 % detection rate, acquisition time drops to 19 %. These time savings estimates represent lower bounds based on the ratio of scanned pixels. The complete implementation will be made available upon publication and upon request during peer-review.