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Automatic assessment of nanostructure quality is essential for scale-up nanomanufacturing. In our previous work, we have developed a method to quantify nanostructure growth quality and detect structural defects through interaction analysis. However, because the method builds on complete feature measurement, its direct application to nanomanufacturing systems is severely constrained by nanostructure metrology. For current inspection techniques such as scanning electron microscope (SEM), the major difficulties of measuring nanostructures lie in two aspects: (i) taking and calibrating images for seamless coverage and (ii) extracting and matching feature information from the images. In this paper, we develop a tailored sampling strategy to relax the metrology constraint. It not only explores the growth region with greatly reduced metrology efforts but maintains desired sampling resolution. In addition, we customize Expectation-Maximization algorithm to optimize interaction estimation with corresponding “incomplete” measurement. Our developed approach enables nanostructure characterization within manufacturing relevant time spans and thus provides a supporting tool for nanomanufacturing. Note to Practitioners-Automatic assessment of nanostructure quality is essential for scale-up nanomanufacturing, but current characterization of nanostructures based on SEM or transmission electron microscopy (TEM) is labor and computation intensive. This paper develops methods to quantify nanostructure local variability and detect defects with minimum metrology efforts.