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As the scaling of conventional planar CMOS is reaching its limits, multiple-gate CMOS structures will likely take up the baton. To facilitate circuit simulation in such advanced technologies, we have developed BSIM-MG: a versatile compact model for multi-gate MOSFETs. In this chapter separate formulations for common multi-gate and independent multi-gate MOSFETs are presented. The core I-V and C-V...
Dielectric Isolated (DI) FinFETs exhibit superior electrostatic control compared to bulk FinFET without needing heavy sub-fin punchthrough stop doping, which increases device variability. Bottom oxidation through STI (BOTS) [1] and silicon-on-nothing (SON) are viable techniques to fabricate DI FinFETs on inexpensive bulk substates, as alternative to SOI substrate. In this paper we analyze DI FinFETs...
Two FinFET fabrication processes are compared with simulation: the conventional fin-first process and the novel fin-last process. With the fin-last process, more longitudinal strain can be incorporated into the channel from source and drain SiGe stressor than fin-first. pFET mobility advantage is 15% at fully-strained condition and with silicon recess. Maintaining vertical junction uniformity is the...
□ BSIM-IMG is a Turnkey, Production Ready model □ Will be submitted to the CMC for standardization □ Physical, Scalable Core Model for FDSOI devices □ Plethora of Real Device Effects modeled □ Advanced Device Effects — Quantum, Back-gate bias, Self-heating □ Validated on Hardware Data from two FDSOI/ UTBSOI technologies □ Available in major EDA tools.
This paper presents an exploratory application of BSIM-IMG (May/2011-release) to ET/UTBB SOI MOSFET modeling and circuit simulations. Compliance with fundamental compact model requirements and physical scalability with respect to technology parameters in BSIM-IMG are analyzed. BSIM-IMG model parameters are extracted on a 20nm technology. Simulation results are presented both for conventional benchmark...
We present a methodology to generate performance-aware corner models--PAM. Accuracy is improved by emphasizing electrical variation data and reconciling the process and electrical variation data. PAM supports corner (plusmnsigma and plusmn2sigma) simulation and MC simulation. Furthermore, PAM supports application-specific corner cards, for example, for gain sensitive applications.
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