Benchmarking Measurement-Based Large-Signal FET Models for GaN HEMT Devices

This paper compares the accuracy and attributes of measurement-based large-signal FET models in the context of GaN HEMT modeling. We compare three FET models that have been implemented within PathWave Advanced Design System. In particular, the benefits and drawbacks of using neural networks to model the I-V and Q-V relations in a general way are analyzed. This is done by characterizing a 150 nm gate length 8x50 GaN-on-SiC HEMT and extracting the respective FET models based on DC-IV, small-signal, and large-signal data in the device's operating range. The three models are validated and benchmarked at different operating conditions and higher frequencies than their extraction frequency to show how neural network technology can serve as a powerful tool for the accurate modeling of thermal and trapping effects of GaN HEMTs.