Hybrid ANN Approach for Enhanced Device Modeling Accuracy
应用文章
With the continuous evolution of semiconductor technology and the increasing complexity of device behaviors, traditional modeling approaches are facing challenges in accuracy, flexibility, and development time. Artificial Neural Networks (ANNs) have emerged as powerful data-driven tools for device modeling, especially in situations where a physical compact model is limited or time-constrained. However, using a full ANN approach also has its limitations. To overcome these challenges, Keysight introduces a hybrid ANN modeling approach that combines the strengths of both ANN and physics-based models.
This application note explains the hybrid ANN modeling method, highlights its advantages, and demonstrates how it addresses the limitations of standalone models through two practical examples: GaN HEMT S-parameter modeling and PowerMOS IV modeling.