Physics-Informed Machine Learning-Based Edge Detection for SEM Images

The Scanning Electron Microscope (SEM) images of Random Access Memory (RAM) chips contain valuable processrelated information, particularly at the edges, which can provide critical insights for hotspot detection and Line Edge Roughness (LER) measurement. However, significant noise and low grayscale variation in SEM images often lead to edge omissions and misdetections. In this paper, we introduce the concept of the SEM Interlayer Effect (SIE), based on empirical observations and theoretical analysis, to address these challenges. Leveraging insights from SIE, we propose a novel Physics-Informed Edge Detection (PIED) method, which enhances the underlying neural network architecture and incorporates a hierarchical weighted loss function. Based on the real-world SEM image dataset from RAM production, PIED achieves a superior Optimal Dataset Scale (ODS) F-measure compared to the Canny edge detector, improving from 0.9001 to 0.9701—a 7.8% increase. This demonstrates that even in the absence of ground truth, PIED significantly enhances edge detection performance, which is crucial for improving process control in semiconductor manufacturing.