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Can graphene serve as a single material for both memory and logic?

By Brian Buntz | June 25, 2024

graphene molecular grid, graphene atomic structure concept, hexag

Conceptual graphene rendering [Adobe Stock]

A new study published in Nature explores the electrochemical potential of graphene, a single-atom-thick sheet of carbon to function as both memory and logic components. By sandwiching graphene between electrolytes and applying specific voltages, researchers, including Marcelo Lozada-Hidalgo and J. Tong of the University of Manchester, created independent pathways for both protons and electrons to flow through the material.

The researchers report that this development allows them to control the electrical conductivity of graphene, switching it between conducting and insulating states, similar to computer memory. The scientists also report that they could precisely control the flow of protons through the material, enabling logic operations much like a computer processor.

Independent control of protons and electrons

This dual functionality arises from the capacity of protons to both permeate graphene and bind to its electrons. By precisely tuning the voltages applied to the device, researchers can independently control the flow of protons and electrons. Noted in Nature paper, “double-gated graphene devices enable precise and robust control of proton transport and hydrogenation.”

As other researchers have already demonstrated, the electric field (E) and charge carrier density (n) in graphene can be independently controlled by manipulating the top (Vt) and bottom (Vb) gate voltages. Specifically, they found that the electric field is proportional to the difference between the gate voltages:

E ∝ Vt – Vb

n ∝ Vt + Vb

The electric field is thus proportional to the difference between the gate voltages, while the charge carrier density is proportional to their sum.

The electric field strength is denoted as:

E ≈ 1.13 × 109 m-1 (Vt – Vb)

The potential of dual memory and logic functionality

This independent control allows the researchers to manipulate proton transport (governed primarily by E) and hydrogenation (governed primarily by n) separately, enabling the dual memory and logic functionality of the graphene device. By applying appropriate voltage combinations, they could switch the graphene between conducting and insulating states for memory operations, while simultaneously controlling proton flow for logic operations.

The potential applications of this technology could be significant as “the combination of functions in the same physical area of the device would eliminate the need for peripheral circuits between the logic and memory components in prospective arrays of these devices, making them more energy efficient and compact,” the authors write.

This finding could support the development of more powerful and energy-efficient computing devices. The Nature article highlights that “the combination of functions in the same physical area of the device would eliminate the need for peripheral circuits between the logic and memory components,” potentially leading to devices that are both smaller and consume less power.

The authors go on to suggest a broader impact, noting that “double-gated graphene devices enable precise and robust control of proton transport and hydrogenation.”

Implications for quantum phenomena and future applications

Graphene has long served a playground for exploring exotic quantum phenomena, A review of recent arXiv papers found research on niche topics such as twisted bilayer graphene, valleytronics, and topological states. While not explicitly stated, the recent Nature paper’s focus on the ability to precisely control both electron and proton flow within graphene, particularly in terms of charge carrier density and electric field manipulation, could point to new possibilities for manipulating quantum phenomena — such as quantum transport, valleytronics, and topological states — and potentially harnessing them for technological applications.

The network graph below shows the interconnections and relationships between core concepts in recent graphene research on the pre-print site ArXiv.

Graphene network image

Co-occurrence network of top 30 keywords in recent graphene research. Based on an analysis of roughly 200 papers from arXiv. Node size indicates keyword frequency, while edge thickness represents co-occurrence strength between keywords.

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