Investigation and Comparison of Work Function Variation for FinFET and UTB SOI Devices Using a Voronoi Approach

Chou, Shao-Heng; Fan, Ming-Long; Su, Pin

doi:10.1109/ted.2013.2248087

Cited by 49 publications

(20 citation statements)

References 22 publications

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“…To assess WFV, we use the Vonoroi grain pattern [15] for TiN gate material, which has two different grain orientations <200> and <111> with the probability of 60% and 40%, respectively, as shown in Figure 5a by the yellow and orange regions, and the relevant parameters are shown in Table 2. To assess fin LER, the rough line edge patterns are generated by Fourier synthesis approach [16] with correlation length (Λ) = 20 nm and root-mean-square amplitude (Δ) = 1.5 nm as shown in Figure 5b.…”

Section: Simulation Methodologymentioning

confidence: 99%

Impacts of Work Function Variation and Line-Edge Roughness on TFET and FinFET Devices and 32-Bit CLA Circuits

Chen

Fan

et al. 2015

JLPEA

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In this paper, we analyze the variability of III-V homojunction tunnel FET (TFET) and FinFET devices and 32-bit carry-lookahead adder (CLA) circuit operating in near-threshold region. The impacts of the most severe intrinsic device variations including work function variation (WFV) and fin line-edge roughness (fin LER) on TFET and FinFET device Ion, Ioff, Cg, 32-bit CLA delay and power-delay product (PDP) are investigated and compared using 3D atomistic TCAD mixed-mode Monte-Carlo simulations and HSPICE simulations with look-up table based Verilog-A models calibrated with TCAD simulation results. The results indicate that WFV and fin LER have different impacts on device Ion and Ioff. Besides, at low operating voltage (<0.3 V), the CLA circuit delay and power-delay product (PDP) of TFET are significantly better than FinFET due to its better Ion and Cg,ave and their smaller variability. However, the leakage power of TFET CLA is larger than FinFET CLA due to the worse Ioff variability of TFET devices. OPEN ACCESS J. Low Power Electron. Appl. 2015, 5 102

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Section: Simulation Methodologymentioning

confidence: 99%

Impacts of Work Function Variation and Line-Edge Roughness on TFET and FinFET Devices and 32-Bit CLA Circuits

Chen

Fan

et al. 2015

JLPEA

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“…to divide the area into randomly shaped grains. The Voronoi algorithm produces realistic grain shapes [12] and is explained in [8].…”

Section: The Model Utilizes the Voronoi Algorithm To Create Grain-boumentioning

confidence: 99%

Analytical modeling of metal gate granularity based threshold voltage variability in NWFET

Vardhan

Mittal

Ganguly

et al. 2018

Solid-State Electronics

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Estimation of threshold voltage V T variability for NWFETs has been computationally expensive due to lack of analytical models. Variability estimation of NWFET is essential to design the next generation logic circuits. Compared to any other process induced variabilities, Metal Gate Granularity (MGG) is of paramount importance due to its large impact on V T variability. Here, an analytical model is proposed to estimate V T variability caused by MGG. We extend our earlier FinFET based MGG model to a cylindrical NWFET by satisfying three additional requirements. First, the gate dielectric layer is replaced by Silicon of electro-statically equivalent thickness using long cylinder approximation; Second, metal grains in NWFETs satisfy periodic boundary condition in azimuthal direction; Third, electrostatics is analytically solved in cylindrical polar coordinates with gate boundary condition defined by MGG. We show that quantum effects only shift the mean of the V T distribution without significant impact on the variability estimated by our electrostatics-based model.The V T distribution estimated by our model matches TCAD simulations. The model quantitatively captures grain size dependence with σ(V T ) with excellent accuracy (6%error) compared to stochastic 3D TCAD simulations, which is a significant improvement over the state-of-the-art model with fails to produce even a qualitative agreement. The proposed model is 63× faster compared to commercial TCAD simulations.

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“…A Poisson Voronoi Diagram (PVD) [11] reproduces this behaviour being able to generate realistic grains that account for the shape of domains growing from these randomly placed nucleation points. The PVD approach has been previously used [12], [13], [14] to simulate the impact of the metal grain WF variability in nanoscale FinFETs. The physical meaning of the PVD makes it a suitable tool to model the grains of the metal contact.…”

Section: Poisson Voronoi Diagramsmentioning

confidence: 99%

Study of Metal-Gate Work-Function Variation Using Voronoi Cells: Comparison of Rayleigh and Gamma Distributions

Indalecio

García‐Loureiro

Seoane

et al. 2016

IEEE Trans. Electron Devices

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We have demonstrated, via validation to experimental data for TiN and Ru, that the grains which appear in the metal gate stacks of nanoscale CMOS devices can be characterized via a two-parameter Gamma distribution (p-values 0.17 and 0.42 for TiN and Ru). Conversely, a previously presented fit which used Rayleigh distribution does not reproduce the experimental data (p-values 3 × 10 −14 and 0.0029 for TiN and Ru). Poisson Voronoi Diagrams (PVDs) are shown as a suitable algorithm to generate grains with Gamma distribution, via fitting of the distribution of 10000 grains. We have also compared the PVD variability against the Rayleigh model. for both TiN and TaN metal gates, and concluded that Rayleigh approach overestimates the device variability (by 11.9% for the TiN and by 7.14% for the TaN).

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Investigation and Comparison of Work Function Variation for FinFET and UTB SOI Devices Using a Voronoi Approach

Cited by 49 publications

References 22 publications

Impacts of Work Function Variation and Line-Edge Roughness on TFET and FinFET Devices and 32-Bit CLA Circuits

Impacts of Work Function Variation and Line-Edge Roughness on TFET and FinFET Devices and 32-Bit CLA Circuits

Analytical modeling of metal gate granularity based threshold voltage variability in NWFET

Study of Metal-Gate Work-Function Variation Using Voronoi Cells: Comparison of Rayleigh and Gamma Distributions

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