Abstract: In this paper, a genetic-neural-network (GNN) based large-signal model for GaN HEMTs is presented along with its parameters extraction procedure. The model is easy to construct and implement in CAD software and requires only DC and S-parameter measurements. An improved decomposition technique is used to model self-heating effect. Two GNN models are constructed to simulate isothermal drain current and power dissipation, respectively. The two model are then composed to simulate the drain current. The modeling procedure was applied to a packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation with measured data. A very good agreement between the simulation and measurement is obtained.
Abstract: In this paper, we have proposed a novel FinFET with
extended body under the poly gate, which is called EB-FinFET, and
its characteristic is demonstrated by using three-dimensional (3-D)
numerical simulation. We have analyzed and compared it with
conventional FinFET. The extended body height dependence on the
drain induced barrier lowering (DIBL) and subthreshold swing (S.S)
have been also investigated. According to the 3-D numerical
simulation, the proposed structure has a firm structure, an acceptable
short channel effect (SCE), a reduced series resistance, an increased
on state drain current (I
on) and a large normalized I
DS. Furthermore,
the structure can also improve corner effect and reduce self-heating
effect due to the extended body. Our results show that the EBFinFET
is excellent for nanoscale device.