Integrated μ-Raman and Acoustic Emission for Fatigue Damage Characterization and Dynamic Performance Prediction of Flexible Electronic Laminates

In flexible electronic devices, the heterogeneous laminated structure (HLS) composed of an ultrathin silicon (Si) film and a polyimide (PI) substrate is prone to microdefect-dominated fatigue failure under cyclic loading. Traditional statistical-lifetime-based assessment methods fail to support refined reliability evaluation accounting for individual variability. To address this, this study integrates μ-Raman spectroscopy (μRS) and acoustic emission (AE) techniques, in combination with particle filtering (PF), to propose a “real-time monitoring−dynamic prediction” framework that enables unified analysis of HLS damage monitoring, state diagnosis, and performance prediction. By fusing multisource information from μRS and AE, synergistic characterization of performance degradation and damage evolution based on internal mechanical measurements is achieved, revealing the spatial nonuniformity and temporal intermittency of fatigue microdamage in the film layer. Furthermore, highly sensitive AE features are selected based on the damage-quantifying b -value to construct a real-time state diagnosis model, and PF is used to dynamically predict HLS performance degradation over 10 5 fatigue cycles. This study is expected to provide a reference for performance prediction and reliability assessment of flexible electronic devices.