Thin film thickness analysis based on a deep learning algorithm using data augmentation

In semiconductor manufacturing process, thin film thickness must be precisely controlled. Because it requires fast and precise thickness measurement, studies have been conducted to analyze the thicknesses of thin films by applying deep learning algorithms to spectral reflectometry. The reflectance spectrum of a thin film sample, which varies according to the film thickness, can be calculated with well-known theoretical equation. A theoretical dataset being used to train a deep learning algorithm for thin film thickness analysis is generated by the theoretical equation. For the practical use of the trained deep learning algorithm, performance evaluation using actual measured data is essential, but it is not easy because the exact thickness of the film sample is not known. Recently, a study that proposed an uncertainty evaluation of thin film thickness measurement using a deep learning model by utilizing the certified reference materials (CRMs) was published. In this study, the measurement uncertainty of a deep learning algorithm for thin film thickness measurement using data augmentation was evaluated. Referring to previous studies, a multilayer perceptron algorithm was designed and trained by theoretical reflectance spectra of silicon dioxide thin film on silicon substrate with thin film thickness varying from 1 nm to 110 nm in visible band. Considering the intensity fluctuation of the light source used in the reflectometry, a noise with a normal distribution of 1% standard deviation was applied to the training dataset. Then, the reflectance spectrum of the silicon dioxide thin film CRMs measured in the wavelength range of 355 nm to 657 nm was analyzed with the trained model. Based on the thickness analysis results, a measurement uncertainty evaluation was performed by considering several uncertainty factors of the offset of the analysis result from the certified value, the uncertainty of the CRMs itself, and the measurement repeatability.