Research on Reverse Decoupling and Optimization of White-Light Interference Signals Based on Deep Learning

In the intelligent operation and maintenance of core process equipment in semiconductor manufacturing, the thickness and morphological parameters of sub-micron multilayer transparent films on wafer surfaces are critical quality indicators that govern device performance and yield. White Light Interferometry (WLI) enables the high-precision measurement of thin-film thickness and topography parameters, and is widely deployed in high-precision manufacturing fields such as semiconductors. However, when measuring sub-micron multilayer transparent films, WLI faces challenges including low computational efficiency, severe parameter coupling, and non-unique solutions, making it difficult to meet the demands of high-throughput online inspection. To address these issues, this paper proposes a deep learning-based method for decoupling and optimizing WLI signals through inverse modeling. The approach establishes an innovative hybrid intelligent framework: first, a training dataset is generated based on interferometric system modeling and simulation; then, a classification model is employed to intelligently categorize the acquired interference signals, decomposing the complex multimodal inversion problem into several sub-problems with simpler patterns. Next, for each signal category, a specialized closed-loop deep network model is designed and trained. This network integrates an inverse prediction network with a forward reconstruction network in series. During training, both prediction error and reconstruction error are jointly used as the loss function, ensuring solution uniqueness and physical consistency, thereby enhancing inversion reliability and robustness. This research provides an effective solution for high-precision online optical measurement of complex thin-film structures, offering significant theoretical value and broad industrial application prospects.