Scaling and Statistics of Bottom-Up Synthesized Armchair Graphene Nanoribbon Transistors

TL;DR

This paper develops a Monte Carlo model to connect the morphology of bottom-up synthesized armchair graphene nanoribbons with their transistor performance, revealing scaling trends and guiding future device improvements.

Contribution

It introduces a new nanofabrication process for sub-7 nm GNR transistors and systematically analyzes how morphology affects device performance using modeling and experimental data.

Findings

GNR channel length achieved down to 7 nm.

Morphology significantly influences transistor performance.

Modeling reveals key scaling trends and challenges.

Abstract

Bottom-up assembled nanomaterials and nanostructures allow for the studies of rich and unprecedented quantum-related and mesoscopic transport phenomena. However, it can be difficult to quantify the correlations between the geometrical or structural parameters obtained from advanced microscopy and measured electrical characteristics when they are made into macroscopic devices. Here, we propose a strategy to connect the nanomaterial morphologies and the device performance through a Monte Carlo device model and apply it to understand the scaling trends of bottom-up synthesized armchair graphene nanoribbon (GNR) transistors. A new nanofabrication process is developed for GNR transistors with channel length down to 7 nm. The impacts of the GNR spatial distributions and the device geometries on the device performance are investigated systematically through comparison of experimental data with the model. Through this study, challenges and opportunities of transistor technologies based on bottom-up synthesized GNRs are pinpointed, paving the way to the further improvement of the GNR device performance for future transistor technology nodes.