Channeled spectroscopic ellipsometry enabled by physics-informed tandem untrained neural networks

Ellipsometry is a powerful metrology technique for characterizing the optical properties of various materials. Channeled spectroscopic ellipsometry (CSE) has shown great promise among the different types of ellipsometry due to its simple setup and rapid performance. Furthermore, CSE modulates the polarization parameters of thin films into a spectrum, thus transforming the measurement process into a demodulation problem. However, conventional CSE faces challenges in measurement accuracy and computational efficiency, with strict hardware and calibration requirements. Inspired by physics-informed machine learning, we propose CSE enabled by the physics-informed tandem untrained neural networks (PITUNN), which does not require training, exhibits high computational efficiency and partially alleviates the strict requirements for hardware and calibration accuracy. We also demonstrate the effectiveness of CSE enabled by the PITUNN and its ability to handle system errors and random noise through simulations and experiments on thin films of different thickness and materials.