Making Graphene Nano Inductor Using Table Top Laser Engraver
Benjamin Barnesa, Ibrahim Elkholy, Nathan Bane, Justin Derickson and, Kausik S Das

TL;DR
This paper presents a novel, scalable laser engraving method to create graphene-based nano-inductors on PVA/graphene oxide films, addressing miniaturization challenges in nano-electronic device fabrication.
Contribution
The authors introduce a simple laser lithography technique to fabricate nano-scale inductors using graphene composites, overcoming current manufacturing limitations.
Findings
Laser engraving induces high curvature twisted screw dislocation pathways.
Conductive pathways linked by pi-pi interactions enable inductive effects.
The method offers a scalable approach for nano-inductor fabrication.
Abstract
There is great interest in so-called nano-electronic devices due to the furious rate of device miniaturization. Fabrication of micro and nano scale resistors and capacitors have already been achieved steadily, but so far, there has been little development in the way of nano-scale coil inductors. This is because of the physical limitations in miniaturization of the design of a solenoid with wires coiled around a metallic core. So, while transistors get steadily smaller, basic inductors in electronics remained relatively bulky. Few methods exist for creating conductive polymer coils and graphene-based kinetic nano-inductors, but their large-scale fabrication process is complex and mostly beyond the current commercial technology available. So, a simpler, scalable, and robust fabrication technique is needed to overcome this bottleneck. In this work we demonstrate a new technique consisting…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsElectrohydrodynamics and Fluid Dynamics · Graphene research and applications · Neuroscience and Neural Engineering
