Enhanced Material Fatigue Prediction in Thin-Film Metallic Substrates via Multi-Modal Data Integration and Recurrent Neural Network Analysis

**Abstract:** This paper introduces a novel methodology for predicting fatigue life in thin-film metallic substrates used in microelectromechanical systems (MEMS) and flexible electronics. Current fatigue prediction models often struggle with the complexity of thin-film structures and the interplay of multiple failure mechanisms. We propose a system that integrates various data modalities – microscopy imaging (SEM, AFM), electrochemical data (EIS), and mechanical testing results – into a recurrent neural network (RNN) architecture augmented with a multi-layered evaluation pipeline. This allows for a more robust and accurate prediction of fatigue life compared to traditional methods, facilitating the design of more durable and reliable devices. This system's immediate commercializability stems from its reliance on established data acquisition techniques and the refinement of existing RNN architectures for significantly enhanced performance.