An improved large-signal model by artificial neural network for the MOSFETs operating in the breakdown region

Abstract

An improved large-signal breakdown model establishment applying an artificial neural network (ANN) approach is presented for metal-oxide-semiconductor field-effect transistors (MOSFETs) operating in the breakdown regime for the first time. A neural network program with the feed-forward back propagation algorithm and Levenberg Marquardt optimization is utilized to obtain the MOSFET large-signal model for breakdown operation. When compared with the conventional model without the breakdown effects considered, more accurate large-signal characteristics in the breakdown regime can be obtained by incorporating the avalanche network using the ANN approach. The breakdown network is demonstrated to be significant for large-signal characterization at high bias according to the analysis of minimum acceptable error. Besides, the divergence problem due to the neglected avalanche network can be avoided during ANN training in the avalanche regime. The accuracy of the presented model can keep about 2% error. The presented ANN approach can be applied to device large-signal modeling in the impact ionization regime.

Keywords:

• Artificial neural network
• large-signal
• MOSFET modeling
• breakdown
• power amplifier

Acknowledgements

The authors would like to thank National Chip Implementation Center (CIC), Hsinchu, Taiwan for chip fabrication, the National Nano Device Laboratories (NDL), Hsinchu, Taiwan for the measurement support, and the Wireless Communication Antenna Research Center, Kaohsiung, Taiwan for the support.