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Here, a prior\u2010guided SEM analysis framework converts routine micrographs into class\u2010resolved masks, quantitative morphology descriptors, and a compact SEM\u2010Derived Morphology Quality Index (MQI). YOLO\u2010derived grain\u2010center priors guide U\u2010MambaBot segmentation, while Class\u2010Probability TV Regularization improves local coherence. Six descriptors are extracted from the segmented  matrix and \u2010like regions, from which four are selected for MQI construction. Across nested, publication\u2010grouped, and within\u2010publication validation schemes, MQI retains a significant monotonic association with power conversion efficiency (Spearman  and 0.42). In annealing\u2010time and additive\u2010concentration studies, MQI reproduces film\u2010 and device\u2010level performance trends and identifies the same optimal processing conditions. Without refitting, it further tracks reported morphology\u2013performance trends across 12 independent studies and 37 processing conditions, yielding a pooled within\u2010study Spearman  (publication\u2010level bootstrap 95% CI 0.79\u20131.00). This framework enables quantitative, morphology\u2010aware SEM screening and process\u2010condition ranking for perovskite films.<\/jats:p>","DOI":"10.1002\/adma.74893","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:28:41Z","timestamp":1790152121000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prior\u2010Guided SEM Image Segmentation Enables Quantitative Morphology Assessment and Process Optimization in Perovskite Solar Cells"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8209-0838","authenticated-orcid":false,"given":"Yixi","family":"Wang","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Photovoltaic Science and Technology Fudan University  Shanghai China"},{"name":"College of Biomedical Engineering Fudan University  Shanghai 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