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Impact of Cross-Sectional Shape on 10-nm Gate Length InGaAs FinFET Performance and Variability / Natalia Seoane; Guillermo Indalecio; Daniel Nagy; Karol Kalna; Antonio J. Garcia-Loureiro
IEEE Transactions on Electron Devices, Volume: 65, Issue: 2, Pages: 456 - 462
Swansea University Author: Kalna, Karol
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Three cross sections (rectangular, bullet shaped, and triangular), resulting from the fabrication process, of nanoscale In0.53Ga0.47As-on-insulator FinFETs with a gate length of 10.4 nm are modeled using in-house 3-D finite-element density-gradient quantum-corrected drift–diffusion and Monte Carlo s...
|Published in:||IEEE Transactions on Electron Devices|
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Three cross sections (rectangular, bullet shaped, and triangular), resulting from the fabrication process, of nanoscale In0.53Ga0.47As-on-insulator FinFETs with a gate length of 10.4 nm are modeled using in-house 3-D finite-element density-gradient quantum-corrected drift–diffusion and Monte Carlo simulations. We investigate the impact of the shape on I – V characteristics and on the variability induced by metal grain granularity (MGG), line-edge roughness (LER), and random dopants (RDs) and compared with their combined effect. The more triangular the cross section, the lower the OFF-current, the drain-induced-barrier-lowering, and the subthreshold slope. The ION/IOFF ratio is three times higher for the triangular-shaped FinFET than for the rectangular-shape one. Independent of the cross section, the MGG variations are the preeminent fluctuations affecting the FinFETs, with four to two times larger σVT than that from the LER and the RDs, respectively. However, the variability induced threshold voltage ( VT ) shift is minimal for the MGG (around 2 mV), but VT shift increases 4-fold and 15-fold for the LER and the RDs, respectively. The cross-sectional shape has a very small influence in VT and OFF-current of the MGG, LER, and RD variabilities, both separated and in combination, with standard deviation differences of only 4% among the different device shapes. Finally, the statistical sum of the three sources of variability can predict simulated combined variability with only a minor overestimation.
Density gradient (DG) quantum corrections, drift–diffusion (DD), FinFET, line-edge roughness (LER), metal grain granularity (MGG), random dopants (RDs)
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