A Lightweight, Low-Label Framework Towards Autonomous Morphology-Resolved SEM–EDS Analysis in Material Microscopy
Abstract
Abstract High-throughput materials characterization is essential for self-driving laboratories, yet it is often the slowest and least automated step, largely because extracting meaningful structure–property relationships from complex, multimodal data still requires substantial expert interpretation. This is particularly true for nanomaterials, whose properties are highly sensitive to structure, environment, and measurement conditions. Moreover, extracting statistically meaningful particle-level compositional information, such as chemical heterogeneity and impurity distributions, from paired Scanning Electron Microscopy (SEM)–Energy-Dispersive X-ray Spectroscopy (EDS), remains largely manual and expertise-intensive. Here, we establish a lightweight, data-efficient deep learning framework that integrates directly into SEM–EDS workflows to enable rapid, automated, morphology-resolved particle identification and compositional quantification. To overcome the reliance of existing deep learning methods on large annotated datasets and time-intensive manual labeling, our framework operates with minimal human annotation. Starting from only five fully human-labeled SEM images containing 577 annotated objects, we use a canvas-based strategy to generate a synthetic training dataset comprising approximately 6,000 labeled objects. Training the lightweight model with this augmented dataset yields a mean intersection over union (mIoU) of 0.74, representing a 19.8% relative improvement over…
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