Small-signal modeling for p-n junctions in the breakdown region at different temperatures using artificial neural networks

Abstract

In this paper, an artificial neural network (ANN) approach is applied to efficiently and accurately determine small-signal parameters of a breakdown model for silicon p-n junctions operating in the impact ionization region at different temperatures for the first time. A feed-forward back propagation neural network program with a Levenberg Marquardt optimization algorithm is employed to implement this ANN-based parameter determination approach. Measured S-parameters at different temperatures which are regarded as a training data-set are fitted by this program to determine small-signal parameters of the breakdown equivalent circuit model at different temperatures. Multiplication factors of p-n junctions at different temperatures are also extracted. This simple and accurate breakdown characterization method based on the ANNs can be applicable to automatic parameter determination for devices operating in the breakdown region.

Keywords:

• Artificial neural network
• small-signal
• temperature
• S-parameters
• breakdown
• p-n junction modeling

Acknowledgments

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 high-frequency measurement support, and the Wireless Communication Antenna Research Center, Kaohsiung, Taiwan for the support.