2014 Annual IEEE India Conference (INDICON) 2014
DOI: 10.1109/indicon.2014.7030498
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BSIM-IMG with improved surface potential calculation recipe

Abstract: In this paper, we have reported the improved surface potential calculation in the BSIM-IMG model for FDSOI MOSFETs. Model validation is done with the experimental data provided by Low-power Electronics Association and Project (LEAP). The model shows accurate behavior for C-V and I-V characteristics while keeping smooth behavior for their higher order derivatives. Model has smooth transition from weak inver sion to strong inversion and satisfies DC and AC symmetry tests.

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Cited by 6 publications
(2 citation statements)
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“…The VT SC in FD‐SOI MOSFETs increases more with the decreasing gate length and can fall abruptly from a high applied bias or a large output swing because of the very high current gain from the built‐in parasitic bipolar and the impact‐ionization current. Other recent models featured only the long‐channel threshold‐voltage in devices with no HALO and no BTS …”
Section: Introductionmentioning
confidence: 99%
“…The VT SC in FD‐SOI MOSFETs increases more with the decreasing gate length and can fall abruptly from a high applied bias or a large output swing because of the very high current gain from the built‐in parasitic bipolar and the impact‐ionization current. Other recent models featured only the long‐channel threshold‐voltage in devices with no HALO and no BTS …”
Section: Introductionmentioning
confidence: 99%
“…The FDSOI transistor's characteristic has frequency dependence via several inherent phenomena like the self heating effect (SHE) [18], substrate effect [13], and gate resistance effect [19]. The dc compact model is not sufficient to predict correct device behavior of measured data over a wide frequency range [20]- [24]. To capture the high-frequency behavior of the FDSOI transistor, we have enhanced the BSIM-IMG model in this work for RF applications by incorporating parasitic components like gate resistance, substrate resistance, and thermal resistance networks.…”
Section: Introductionmentioning
confidence: 99%