Integration of deep learning for nonlinear spectral decomposition of in situ interfaces analysis

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.