Abstract
A novel scalable model of substrate components for deep n-well (DNW) RF MOSFETs with different number of fingers is presented for the first time. The test structure developed in [1] is employed to directly access the characteristics of the substrate to extract the different substrate components. A methodology is developed to directly extract the parameters for the substrate network from the measured data. By using the measured two-port data of a set of nMOSFETs with different number of fingers, with the DNW in grounded and float configuration, respectively, the parameters of the scalable substrate model are obtained. The method and the substrate model are further verified and validated by matching the measured and simulated output admittances. Excellent agreement up to 40 GHz for configurations in common-source has been achieved.
Highlights
THE incorporation of a Deep N-Well (DNW) implantation into a standard CMOS technology has become a popular choice for reducing undesired interference in CMOS mixed-signal/RF SoC designs [2,3,4,5,6]
A novel scalable model of substrate components for deep n-well (DNW) RF MOSFETs with different number of fingers is presented for the first time
The results show that the substrate components within the p-well and the capacitances caused by the DNW are strongly dependent on Number of fingers (Nf), while the parasitic components in the original p-substrate have a slight dependence on Nf in multi-finger devices
Summary
THE incorporation of a Deep N-Well (DNW) implantation into a standard CMOS technology has become a popular choice for reducing undesired interference in CMOS mixed-signal/RF SoC designs [2,3,4,5,6]. Substrate network parameters are of the utmost importance in accurately modeling the output admittance of RF MOSFETs. For mixed-signal/RF SoC design, a scalable model of RF MOSFETs is useful. There are few detailed works on scalable models with substrate network components in DNW RF MOSFETs with different number of fingers. The capacitive coupling effect, which is physically in existence, is always neglected All of these make the previously reported substrate models less physically reasonable to use for accurately extracting the substrate network components of DNW RF MOSFETs. In this paper, a compact, physically based substrate network is proposed targeted at DNW RF MOSFET modeling. Excellent agreement between the simulated and measured output admittance for a set of devices with different number of fingers up to 40 GHz validated the accuracy of the methodology proposed for DNW RF-MOSFET modeling in this paper
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