AI-Driven Feature Recognition of SEM Profiles in Deep Reactive Ion Etching Based on Physics-Constrained Variational Autoencoder

Deep reactive ion etching (DRIE) is critical for fabricating high-aspect-ratio structures in microelectromechanical systems (MEMS), yet its complex, parameter-dependent process poses significant optimization challenges. Artificial intelligence (AI) offers an efficient optimization solution, but its implementation faces the technical challenge of acquiring large-scale data from scanning electron microscopy (SEM) images, the standard for evaluating DRIE etching outcomes. Traditional SEM analysis relies on labor-intensive manual methods, incurring 15-20% errors and hindering high-throughput manufacturing. Existing automated methods, such as CNNs and SVMs, falter with 70-80% accuracy in noisy SEM images, failing to capture the dynamic evolution of etched structures. To address these limitations, we propose a physics-constrained variational level set autoencoder (VLSet-AE) for automated SEM sectional-profile analysis. By integrating physical etching constraints and a three-dimensional framework (time, linewidth, etching depth), VLSet-AE achieves precise contour recognition and nine critical dimensions extraction—scallop depth (2.29%), scallop width (peak-to-peak: 2.05%, valley-to-valley: 6.28%), scallop radius (4.69%), profile angle (0.56%), trench depth (5.46%), bow width (4.35%), mid width (2.43%), and bottom width (4.78%)—with an average error of 3.65% an overall model accuracy of 94.3%, significantly outperforming manual annotation and state-of-the-art alternatives. Compared to seven current models (e.g., CNNs, LSTMs, ResNet), VLSet-AE achieves the shortest training time (20 s), fastest inference time (1.2 s), highest recognition accuracy (96%), and competitive memory usage (50 MB) and parameter count (4.0 million). By enabling efficient, large-scale data acquisition for AI-optimized DRIE processes, VLSet-AE empowers scalable, intelligent manufacturing, unlocking the potential for advanced microfabrication technologies. This approach provides a forward-looking framework for AI-driven MEMS process design and manufacturing, delivering innovative solutions for future AI-assisted microfabrication advancements.