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This paper presents a novel Neurospace Mapping (Neuro-SM) method for packaged transistor modeling. A new structure consisting of the input package module, the nonlinear module, the output package module, and the S-Matrix calculation module is proposed for the first time. The proposed method can develop the model only using the terminal signals, instead of the internal and physical structure information of the transistors. An advanced training method utilizing the different parameters to adjust the different characteristics of the packaged transistors is developed to make the proposed model match the device data efficiently and accurately. Measured data of radio frequency (RF) power laterally diffused metal-oxide semiconductor (LDMOS) transistor are used to verify the capability of the proposed Neuro-SM method. The results demonstrate that the novel Neuro-SM model is more accurate and efficient than existing device models.

Neurospace Mapping Modeling for Packaged Transistors