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

Abstract

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. INDEX TERMS Drift-diffusion, line edge roughness, metal gate granularity, Monte Carlo, quantum corrections, nanowire FET.

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

“…The analysed device has a low off-current of 0.03 μA/μm, acceptable for applications in mobile low power devices with a long battery life, and an on-current of 1770 μA/μm, that has been achieved by increasing the maximum S/D doping from 5×1019 cm−3, used in the 22 nm gate length experimental device, to 1020 cm−3. This increase in the doping has allowed to raise the device on-current by 40% (as previously shown in [38]), at the cost of a slight deterioration in the sub-threshold slope (SS). The device SS is 71 mV/dec, not far from the ideal limit of 60 mV/dec.…”

“…The reason for this is twofold: i) in the sub-threshold, the electrostatics dominate and quantum-corrected DD simulations are able to provide accurate results and, ii) MC results can be extremely noisy at very low gate biases and lead to incorrect off-current or sub-threshold slope values. However, in the on-region regime, VENDES performs the variability studies via the SCH-MC simulations since the DD approach is unable to capture a non-equilibrium carrier transport even if it is properly calibrated, leading to large over- or under-estimation of the variability [38]. However, it is important to remark (as seen in Figure 3), that both SCH-DD and MC-DD simulations match perfectly at the threshold.…”

Steady-State Simulation of Semiconductor Devices Using Discontinuous Galerkin Methods

“…The carrier movements change n e (r) and n h (r), which in turn affect E(r) through the Poisson equation [see (1)]. Furthermore, R(n e , n h ) [in (6)] and µ e (E) and µ h (E) [in (7)] are nonlinear functions of n e (r) and n h (r), and E(r), respectively. This system can be solved using either a decoupled approach such as the Gummel method or a fully-coupled scheme such as the direct application of the Newton method [2], [22].…”

“…Unknowns ϕ t+1 (r) and E t+1 (r) are obtained by solving (14). Then, µ e (E t+1 ) and µ h (E t+1 ) are computed using E t+1 (r) in (7). Finally, n t+1 e (r) and n t+1 h (r) can be obtained by solving…”

Investigation of n-ZnO/p-Si and n-TiO2/p-Si Heterojunction Solar Cells: TCAD + DFT

“…The drift-diffusion model calculates the current formed due to the difference in the concentration of charge carriers and the electric field. It is presented in equation 3 [53] n n n p p p J nq J pq…”