Two turn-key surface potential-based compact models are developed to simulate multigate transistors for integrated circuit (IC) designs. The BSIM-CMG (common-multigate) model is developed to simulate double-, triple-, and all-around-gate FinFETs and it is selected as the world's first industry-standard compact model for the FinFET. The BSIM-IMG (independent-multigate) model is developed for independent double-gate, ultrathin body (UTB) transistors, capturing the dynamic threshold voltage adjustment with back gate bias. Starting from long-channel devices, the basic models are first obtained using a Poisson-carrier transport approach. The basic models agree with the results of numerical two-dimensional device simulators. The real-device effects then augment the basic models. All the important real-device effects, such as shortchannel effects (SCEs), quantum mechanical confinement effects, mobility degradation, and parasitics are included in the models. BSIM-CMG and BSIM-IMG have been validated with hardware silicon-based data from multiple technologies. The developed models also meet the stringent quality assurance tests expected of production level models.INDEX TERMS Double-gate FET, FinFET, integrated circuit modeling, MOSFET compact model, RF FinFET, short-channel effects, SPICE, triple-gate FET, UTB-SOI, UTBB-SOI.
BSIM6 is the latest industry-standard bulk MOS-FET model from the BSIM group developed specially for accurate analog and RF circuit designs. The popular real-device effects have been brought from BSIM4. The model shows excellent source-drain symmetry during both dc and small signal analysis, thus giving excellent results during analog and RF circuit simulations, e.g., harmonic balance simulation. The model is fully scalable with geometry, biases, and temperature. The model has a physical charge-based capacitance model including polydepletion and quantum-mechanical effect thereby giving accurate results in small signal and transient simulations. The BSIM6 model has been extensively validated with industry data from 40-nm technology node.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.