35‐3: A Novel Approach for Connector Modeling and Simulation Using Machine Learning

Connectors are key components in signal transmission for highspeed display systems, and their performance is crucial to signal integrity and display quality. However, traditional methods based on 3D FEM(Finite Element Method) simulations and physical testing have limitations in accuracy and efficiency, making it hard to meet the design needs of modern high‐speed connectors. To address this, this paper proposes a machine learning model that combines XGBoost regression with Bayesian optimization to predict key performance metrics of connectors, such as insertion loss, return loss, TDR impedance, and eye diagram characteristics. Experimental results show that the method can make accurate predictions in complex conditions and supports a connector transmission bandwidth of 25Gbps, providing an effective solution for high‐speed signal channel design.