3D Monte Carlo simulation of the programming dynamics and their statistical variability in nanoscale charge-trap memories

Impact of random dopant fluctuations on trap-assisted tunnelling in nanoscale MOSFETs

“…2 [6,7]. Moreover TAT is a key parameter for High-K dielectric devices featuring a thick oxide with a relatively high density of traps [11] and for non-volatile memory devices, where it compromises charge retention [12].…”

“…4a). The calculation of these time constants is based on a local model, as in [12], with the additional correction for inelastic tunnelling and multi-phonon activation energy E a [13]. The relevant equation for capture time is the following equation:…”

FDSOI molecular flash cell with reduced variability for low power flash applications

“…The molecular charge storage is very localised, thus minimising cross-cell capacitive coupling (arising from charge redistribution on the sides of a poly-Si FG and being one of the most critical issues with flash memories). Although this benefit is present in floating gates realised by chargetrapping dielectric or by a metallic nano-cluster array, both technologies exhibit very large variability [4]. Charge-trap memories suffer variation in trap-density and trap-energy and the size and density of nano-clusters is difficult to control (this precludes their ultimate miniaturisation).…”

Comparison Between Bulk and FDSOI POM Flash Cell: A Multiscale Simulation Study

“…Nanoscrystals and charge-trapping memories have been proposed aiming to improve the flash cell performance [5], [6]. However, the random number and position of the traps create a significant additional variability in the threshold voltage of the programmed flash cells [7], [8]. One possible option for an improvement is to replace the nanocrystals/random defects with molecules [9], [10].…”