Insights into infrared crystal phase characteristics based on deep learning holography with attention residual network

Abstract

This paper introduces the infrared crystal phase, and provides unconventional mechanistic insights into the commonly thought “crystal phase”. The critical challenge with the obtained fidelity phase is the unwrapping process, which was addressed using self-attention mechanism deep learning infrared-band holography. This method strikes a balance between the theoretical rigor of physical models and the flexibility of data-driven approaches. Specifically, utilizing a short wavelength infrared digital holographic system and algorithm resulted in the acquisition of high-quality wrapped phases. Then, the network architecture was applied for phase unwrapping. Through demonstrative applications, static phase-type thickness variation was measured in samples. During moments of intense phase transitions, the microstructural evolution of Na₂CO₃ crystals was monitored, and the process of perovskite material film formation was observed. The results demonstrated that environmental detection noise and twin images were effectively suppressed, and phase values were also dramatically varied after stabilization of the traditional amplitude signal. These discoveries guide the characterization of novel materials and also provide insights into alterations of properties during crystal preparation and growth, which is crucial for the final outcome.