Deep Learning-based Object Detection in Electron Microscopy for Semiconductor Analysis: Advances, Challenges, and Perspectives

초록

With the continuous miniaturization of semiconductor devices, nanoscale defect detection has become a key fundamental and technological requirement. Electron microscopes, with their high resolution, play a crucial role in structural and electrical analysis. In recent years, deep learning has significantly improved the accuracy and automation level of semiconductor defect detection and characterization. This paper reviews the application progress of deep learning in electron microscope imaging for semiconductor analysis and research, focusing on the characteristics and performance of CNN, Transformer, and their hybrid models. Despite significant achievements, data scarcity, insufficient generalization ability, and computational costs remain major challenges. Proposing solutions to these challenges is expected to further promote the development of intelligent detection and facilitate high-precision and high-yield semiconductor manufacturing. © The Institution of Engineering & Technology 2025.

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