Impact of Cross-Sectional Shape on 10-nm Gate Length InGaAs FinFET Performance and Variability

• Published in: IEEE transactions on electron devices 2018-02, Vol.65 (2), p.456-462
• Main Authors: Seoane, Natalia, Indalecio, Guillermo, Nagy, Daniel, Kalna, Karol, Garcia-Loureiro, Antonio J.
• Format: Article
• Language: English
• Subjects: Density gradient (DG) quantum corrections; drift–diffusion (DD); FinFET; FinFETs; line-edge roughness (LER); Logic gates; metal grain granularity (MGG); Metals; Performance evaluation; random dopants (RDs); Shape; Solid modeling; Threshold voltage
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2017.2785325

Three cross sections (rectangular, bullet shaped, and triangular), resulting from the fabrication process, of nanoscale In 0.53 Ga 0.47 As-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 {I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}} 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 \sigma {V}_{T} than that from the LER and the RDs, respectively. However, the variability induced threshold voltage ( {V}_{T} ) shift is minimal for the MGG (around 2 mV), but {V}_{T} shift increases 4-fold and 15-fold for the LER and the RDs, respectively. The cross-sectional shape has a very small influence in {V}_{T} 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.