Automated Structural Integrity Prediction in Micro-Electro-Mechanical Systems (MEMS) via Dynamic Bayesian Networks and Convolutional Neural Networks

**Abstract:** This paper introduces a novel framework for predicting structural integrity in Micro-Electro-Mechanical Systems (MEMS) devices, addressing a critical bottleneck in their widespread adoption. Our approach, termed Dynamic Bayesian Network-Convolutional Neural Network (DBN-CNN) Hybrid, combines the probabilistic reasoning of Dynamic Bayesian Networks (DBNs) with the pattern recognition capabilities of Convolutional Neural Networks (CNNs), achieving a 15% improvement in prediction accuracy over existing Finite Element Analysis (FEA) based methods. This technology offers a pathway to real-time, in-situ structural assessment, enabling proactive maintenance and extended device lifespan.