Drift-Diffusion Versus Monte Carlo Simulated ON-Current Variability in Nanowire FETs

Variability of semiconductor devices is seriously limiting their performance at nanoscale. The impact of variability can be accurately and effectively predicted by computer-aided simulations in order to aid future device designs. Quantum corrected (QC) drift-diffusion (DD) simulations are usually employed to estimate the variability of state-of-the-art non-planar devices but require meticulous calibration. More accurate simulation methods, such as QC Monte Carlo (MC), are considered time consuming and elaborate. Therefore, we predict TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability on a 10-nm gate length gate-all-around Si nanowire FET and perform a rigorous comparison of the QC DD and MC results. In case of the MGG, we have found that the QC DD predicted variability can have a difference of up to 20% in comparison with the QC MC predicted one. In case of the LER, we demonstrate that the QC DD can overestimate the QC MC simulation produced variability by a significant error of up to 56%. This error between the simulation methods will vary with the root mean square (RMS) height and maximum source/drain n -type doping. Our results indicate that the aforementioned QC DD simulation technique yields inaccurate results for the ON-current variability

Benchmarking of FinFET, Nanosheet, and Nanowire FET Architectures for Future Technology Nodes

Nanosheet (NS) and nanowire (NW) FET architectures scaled to a gate length (L G ) of 16 nm and below are benchmarked against equivalent FinFETs. The device performance is predicted using a 3D finite element drift-diffusion/Monte Carlo simulation toolbox with integrated 2D Schrödinger equation based quantum corrections. The NS FET is a viable replacement for the FinFET in high performance (HP) applications when scaled down to L G of 16 nm offering a larger on-current (I ON ) and slightly better sub-threshold characteristics. Below L G of 16 nm, the NW FET becomes the most promising architecture offering an almost ideal sub-threshold swing, the smallest off-current (I OFF ), and the largest I ON /I OFF ratio out of the three architectures. However, the NW FET suffers from early ION saturation with the increasing gate bias that can be tackled by minimizing interface roughness and/or by optimisation of a doping profile in the device bodyThis work was supported in part by the Spanish Government under Project TIN2013-41129-P and Project TIN2016-76373-P, in part by the Xunta de Galicia and FEDER Funds under Grant GRC 2014/008, and in part by the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019) under Grant ED431G/08. The work of Guillermo Indalecio was supported by the Programa de Axudas á Etapa Posdoutoral da Xunta de Galicia under Grant 2017/077. The work of Natalia Seoane was supported by the RyC Programme of the Spanish Ministerio de Ciencia, Innovación y Universidades under Grant RYC-2017-23312S

Benchmarking of FinFET, Nanosheet, and Nanowire FET Architectures for Future Technology Nodes

Nanosheet (NS) and nanowire (NW) FET architectures scaled to a gate length ( LG ) of 16 nm and below are benchmarked against equivalent FinFETs. The device performance is predicted using a 3D finite element drift-diffusion/Monte Carlo simulation toolbox with integrated 2D Schrödinger equation based quantum corrections. The NS FET is a viable replacement for the FinFET in high performance (HP) applications when scaled down to LG of 16 nm offering a larger on-current ( ION ) and slightly better sub-threshold characteristics. Below LG of 16 nm, the NW FET becomes the most promising architecture offering an almost ideal sub-threshold swing, the smallest off-current ( IOFF ), and the largest ION/IOFF ratio out of the three architectures. However, the NW FET suffers from early ION saturation with the increasing gate bias that can be tackled by minimizing interface roughness and/or by optimisation of a doping profile in the device body

FinFET Versus Gate-All-Around Nanowire FET: Performance, Scaling, and Variability

Performance, scalability and resilience to variability of Si SOI FinFETs and gate-all-around (GAA) nanowires (NWs) are studied using in-house-built 3D simulation tools. Two experimentally based devices, a 25 nm gate length FinFET and a 22 nm GAA NW are modelled and then scaled down to 10.7 and 10 nm gate lengths, respectively. A TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability affecting OFF and ON characteristics are investigated and compared. In the OFF-region, the FinFETs have over an order of magnitude larger OFF-current that those of the equivalent GAA NWs. In the ON-region, the 25/10.7 nm gate length FinFETs deliver 20/58% larger ON-current than the 22/10 nm gate length GAA NWs. The FinFETs are more resilient to the MGG and LER variability in the sub-threshold compared to the GAA NWs. However, the MGG ON-current variability is larger for the 10.7 nm FinFET than that for the 10 nm GAA NW. The LER ON-current variability depends largely on the RMS height; whereas a 0.6 nm RMS height yields a similar variability for both FinFETs and GAA NWs. Finally, the industry preferred 110 channel orientation is more resilient to the MGG and LER variability in both architectures

Multilevel 3-D Device Simulation Approach Applied to Deeply Scaled Nanowire Field Effect Transistors

Three silicon nanowire (SiNW) field effect transistors (FETs) with 15 -, 12.5 -and 10.6 -nm gate lengths are simulated using hierarchical multilevel quantum and semiclassical models verified against experimental ID – VG characteristics. The tight-binding (TB) formalism is employed to obtain the band structure in k -space of ellipsoidal NWs to extract electron effective masses. The masses are transferred into quantum-corrected 3-D finite element (FE) drift-diffusion (DD) and ensemble Monte Carlo (MC) simulations, which accurately capture the quantum-mechanical confinement of the ellipsoidal NW cross sections. We demonstrate that the accurate parameterization of the bandstructure and the quantum-mechanical confinement has a profound impact on the computed ID – VG characteristics of nanoscaled devices. Finally, we devise a step-by-step technology computer-aided design (TCAD) methodology of simple parameterization for efficient DD device simulations

Simulations of Statistical Variability in n-Type FinFET, Nanowire, and Nanosheet FETs

Four sources of variability, metal grain granularity (MGG), line-edge roughness (LER), gate-edge roughness (GER), and random discrete dopants (RDD), affecting the performance of state-of-the-art FinFET, nanosheet (NS), and nanowire (NW) FETs, are analysed via our in-house 3D finite-element drift-diffusion/Monte Carlo simulator that includes 2D Schrödinger equation quantum corrections. The MGG and LER are the sources of variability that influence device performance of the three multi-gate architectures the most. The FinFET and the NS FET are similarly affected by the MGG variations with threshold voltage and on-current standard deviations significantly lower (at least 20 %) than those of the NW FET. The LER variability has a negligible influence in the NS FET performance with σVT values around 12 and 42 times lower than those of the FinFET and the NW FET. The three architectures are equally affected by the RDD (σVT= 8 mV) and minimally influenced by the GER (σVT ≈ 4 mV). The variability of NS FETs makes them strong candidates to replace FinFETs

Simulation and Modeling of Novel Electronic Device Architectures with NESS (Nano-Electronic Simulation Software): A Modular Nano TCAD Simulation Framework

The modeling of nano-electronic devices is a cost-effective approach for optimizing the semiconductor device performance and for guiding the fabrication technology. In this paper, we present the capabilities of the new flexible multi-scale nano TCAD simulation software called NanoElectronic Simulation Software (NESS). NESS is designed to study the charge transport in contemporary and novel ultra-scaled semiconductor devices. In order to simulate the charge transport in such ultra-scaled devices with complex architectures and design, we have developed numerous simulation modules based on various simulation approaches. Currently, NESS contains a driftdiffusion, Kubo–Greenwood, and non-equilibrium Green’s function (NEGF) modules. All modules are numerical solvers which are implemented in the C++ programming language, and all of them are linked and solved self-consistently with the Poisson equation. Here, we have deployed some of those modules to showcase the capabilities of NESS to simulate advanced nano-scale semiconductor devices. The devices simulated in this paper are chosen to represent the current state-of-the-art and future technologies where quantum mechanical effects play an important role. Our examples include ultra-scaled nanowire transistors, tunnel transistors, resonant tunneling diodes, and negative capacitance transistors. Our results show that NESS is a robust, fast, and reliable simulation platform which can accurately predict and describe the underlying physics in novel ultra-scaled electronic devices.European Union Horizon 2020 - 688101 SUPERAID7EPSRC UKRI Innovation Fellowship - EP/S001131/1 (QSEE), No. EP/P009972/1 (QUANTDEVMOD)H2020-FETOPEN-2019 s- No.862539-Electromed-FET OPEN.No. EP/S000259/1(Variability PDK for design based research on FPGA/neuro computing

NEGF simulations of a junctionless Si gate-all-around nanowire transistor with discrete dopants

We have carried out 3D Non-Equilibrium Green Function simulations of ajunctionlessgate-all-around n-type silicon nanowiretransistor of 4.2 × 4.2 nm2 cross-section. We model the dopants in a fully atomistic way. The dopant distributions are randomly generated following an average doping concentration of 1020 cm−3. Elastic and inelastic phonon scattering is considered in our simulation. Considering the dopants in adiscrete way is the first step in the simulation of random dopant variability in junctionlesstransistors in a fully quantum mechanical way. Our results show that, for devices with an “unlucky” dopants configuration, where there is a starvation of donors under the gate, the threshold voltage can increase by a few hundred mV relative to devices with a more homogeneous distribution of dopants. For the first time we have used a quantum transport model with dissipation to evaluate the change in threshold voltage and subthreshold slope due to the discrete random donors in the channel of ajunctionlessnanowire nMOS transistor. These calculations require a robust convergence scheme between the quantum transport equation and the Poisson equation in order to achieve convergence in the dopant-induced resonance regime

A Multi-Method Simulation Toolbox to Study Performance and Variability of Nanowire FETs

An in-house-built three-dimensional multi-method semi-classical/classical toolbox has been developed to characterise the performance, scalability, and variability of state-of-the-art semiconductor devices. To demonstrate capabilities of the toolbox, a 10 nm gate length Si gate-all-around field-effect transistor is selected as a benchmark device. The device exhibits an off-current ( IOFF ) of 0.03 μ A/ μ m, and an on-current ( ION ) of 1770 μ A/ μ m, with the ION/IOFF ratio 6.63×104 , a value 27% larger than that of a 10.7 nm gate length Si FinFET. The device SS is 71 mV/dec, no far from the ideal limit of 60 mV/dec. The threshold voltage standard deviation due to statistical combination of four sources of variability (line- and gate-edge roughness, metal grain granularity, and random dopants) is 55.5 mV, a value noticeably larger than that of the equivalent FinFET (30 mV). Finally, using a fluctuation sensitivity map, we establish which regions of the device are the most sensitive to the line-edge roughness and the metal grain granularity variability effects. The on-current of the device is strongly affected by any line-edge roughness taking place near the source-gate junction or by metal grains localised between the middle of the gate and the proximity of the gate-source junction