KEYWORDS: Sum frequency generation, Interfaces, Deep learning, Thin films, Spectroscopy, Signal intensity, Signal attenuation, Polystyrene, Optical surfaces, Vibration
Extreme Ultraviolet Lithography (EUV) and other advanced manufacturing technologies have increased the demand for characterizing 3D integrated structures. Due to the inability to directly access and observe buried interfaces, existing metrology tools face significant challenges in this area. Sum Frequency Generation (SFG) spectroscopy, with its surface and interface selectivity, non-destructive nature, and high sensitivity, represents a feasible option for probing molecular interactions at these buried interfaces. However, the nonlinear characteristics of SFG spectra and the coupling between spectral components make manual spectral decomposition highly complex, requiring extensive experience in spectral analysis and is relatively time-consuming, while the stability can hardly be ensured. This severely limits the application of SFG in large-scale, high-throughput characterizations. To overcome this bottleneck, we have developed a toolkit for the decomposition process by integrating advanced deep learning techniques, specifically a Multi-Layer Perceptron (MLP) network with custom activation functions. This toolkit reduces the analysis time from several hours to just a few minutes, while maintaining high accuracy compared to manual operations.
Measurement is a critical aspect of advanced lithography, particularly for evaluating process capabilities in sub-10 nm technology nodes. Traditional manual measurement methods for Scanning Electron Microscopy (SEM) images lack robustness and are inadequate for large-scale analysis, especially for complex 2D features such as dislocations in hexagonal array patterns. To address these limitations, a contour-based algorithm has been developed for measuring Critical Dimension (CD), pitch, and dislocations in hexagonal arrays. By generating virtual layout based on measured CD and pitch, the algorithm enables detailed analysis of placement errors and provides insights into pattern regularity and symmetry. This approach has been verified on SEM images of hexagonal arrays with a pitch of approximately 30 nm, demonstrating its effectiveness in supporting advanced manufacturing process evaluation.
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