Variability Predictions for the Next Technology Generations of n-type SixGe1−x Nanowire MOSFETs

Abstract

Using a state-of-the-art quantum transport simulator based on the effective mass approximation, we have thoroughly studied the impact of variability on SixGe1−x channel gate-all-around nanowire metal-oxide-semiconductor field-effect transistors (NWFETs) associated with random discrete dopants, line edge roughness, and metal gate granularity. Performance predictions of NWFETs with different cross-sectional shapes such as square, circle, and ellipse are also investigated. For each NWFETs, the effective masses have carefully been extracted from sp3d5s∗ tight-binding band structures. In total, we have generated 7200 transistor samples and performed approximately 10,000 quantum transport simulations. Our statistical analysis reveals that metal gate granularity is dominant among the variability sources considered in this work. Assuming the parameters of the variability sources are the same, we have found that there is no significant difference of variability between SiGe and Si channel NWFETs.

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

“…Its modular structure is illustrated in Figure 2, where the five main components of NESS are presented: the structure generator (SG), the effective mass extractor, the material database, the solvers, and the output generator allowing to store the simulation results (i.e., current, electrostatic potential, charge concentration). Firstly, the SG [23,24] is a flexible module capable of generating and configuring various types of devices (such as NWTs, multi-gate 3D devices architectures, or bulk CMOS transistors) and the corresponding simulation domains. It accepts a text file as input, and the generated device structure can be saved in a binary or ASCII format (vtk files) for easy visualization with freeware software, such as ParaView.…”

“…It can calculate the correct electron confinement and transport effective masses from atomistic simulations (such as density functional theory (DFT)) or semi-empirical models (such as tight-binding (TB)) of the electronic band structure of NWTs with the technologically relevant cross-sectional area, shape, and transport orientations. The capabilities of this module have been already demonstrated in accurately computing the effective masses of Si [25] and Si x Ge 1−x [23] NWTs considering different dimensions and shapes.…”

“…Alloy disorder scattering is not implemented in the current version of NESS and it is not considered in this work. To adopt more realistic conduction bands in this nanoscaled structures, the effective masses have been extracted for each shape from atomistic simulations making use of the effective mass extractor of NESS [23].…”

Enhanced Capabilities of the Nano-Electronic Simulation Software (NESS)

“…1 shows the five main modules of our simulation environment NESS: structure generator, effective mass extractor, material database, solvers, and outputs. First, the structure generator allows the creation and configuration of the 3D device structures [6], [7], including the following main variability sources: random discrete dopant (RDD), line edge roughness (LER), and metal gate granularity (MGG). Second, the effective mass extractor [8] can calculate the correct electron effective masses, in both confinement and transport directions, from the first principle simulations of the electronic bandstructure of nanowire transistors (NWTs) with technologically relevant cross-sectional area, shape, and transport orientations.…”

Editorial for the Special Issue on Miniaturized Transistors

“…The authors demonstrate the achievements and feasibility of a full simulation of the impact of relevant systematic and stochastic variations on advanced devices and circuits. In [13], Lee et al used TCAD in order to study the impact of variability on the next generation SixGe1-x channel gate-all-around (GAA) nanowire metal MOSFETs by looking at the effects of random discrete dopants, line edge roughness, and metal gate granularity. After generating 7200 transistor samples and performing 10,000 quantum transport simulations, a statistical analysis is performed, revealing metal gate granularity as the dominant variability source which should be considered.…”