Artificial Intelligence Techniques Based Generic, Scalable and Robust Large Signal Modeling of GaN High Electron Mobility Transistors for 6G Applications

Faculty Development Competitive Research Grant Program (AI and Data Science) 2024-2026

The principal objective of this research project is to develop scalable, generalized and CAD-compatible ML-based LSMs for GaN HEMTs in order to augment and utilize these models for designing advanced RFPAs for 6G applications. This project envisages to create a niche technology at Nazarbayev University which can place the university is a lead role in this domain in Central Asia and Kazakhstan. To support this initiative, three distinct aspects will be investigated and innovative solutions will be sought during this project. In brief, following are the key objectives of this research project:
a. Development of accurate, reliable, scalable, and efficient Small-Signal Models (SSMs) for GaN HEMTs using ML techniques
b. Development of ML-based accurate and reliable noise models and thus Low-Noise Amplifiers (LNAs) using SSMs as foundation
c. Development of platform independent, scalable, generalized and CAD-compatible LSMs for GaN HEMTs using ML techniques
d. ML-enabled waveform engineering based non-linear models for GaN HEMTs
e. Development of ML-based behavioral models for GaN devices using X-parameters measurements
f. Comprehensive and comparative analysis of various AI/ML techniques for both EC and behavioral modeling of GaN HEMTs