A Comparative Study of Machine Learning Algorithms for Measuring Thin-Film Thickness Using Terahertz Time-Domain Waves Simulated by the Finite Difference Time Domain Method

Terahertz (THz) waves offer unique advantages, including non-contact operation, high penetration capability, and high resolution, making them particularly well-suited for the non-destructive thickness measurement of film-structured materials. In reflective terahertz time-domain spectroscopy (THz-TDS), thickness measurement approaches are generally classified into three categories: optimization-based methods that rely on theoretical models, time-of-flight (ToF), and machine learning. Model-based optimization techniques require precise knowledge of the optical parameters and structural configuration of each layer; however, they often suffer from slow convergence and are prone to becoming trapped in local optima. In contrast, ToF-based methods determine thickness by calculating the time delay between echo pulses reflected from different interfaces, yet their applicability is limited when the film thickness is extremely small. Machine learning, especially deep learning, enables the establishment of a direct, data-driven mapping between THz waveforms (or their extracted features) and the target thickness. Such approaches offer rapid inference, strong robustness to noise, and good adaptability to thin or structurally complex films, although their accuracy remains dependent on the quality of training data and the generalization capability of the model. In this study, high-fidelity THz waveform data generated via finite-difference time-domain (FDTD) simulations are utilized to conduct a comparative investigation into the film thickness prediction performance of several representative machine learning algorithms, including Back Propagation (BP) neural networks, Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), K-Nearest Neighbors (KNN), and Partial Least Squares (PLS) regression. The results indicate that, in terms of prediction error, the overall ranking of algorithmic performance from best to worst is: PLS > RF > SVM > BP > ELM > KNN. These findings provide valuable guidance for the future application of machine learning-assisted THz-TDS in precise film thickness measurement.