Ultra‑low‑energy control of graphene stacking could enable slidetronic memory and logic

Graphitic polytypes are unique stacking arrangements of graphene layers, and they strongly affect the material’s behavior: electrical conductivity, response to magnetic fields, intrinsic polarization, and even the emergence of unconventional superconductivity can all change when the stacking is reconfigured. Until now, controlled switching between these stacking states required micrometre‑scale domains and relatively large, micronewton‑scale forces, which made practical devices unrealistic. In the new work, the team overcomes this limitation by designing tiny graphene “islands” whose stacking can be switched cleanly and reversibly at the 30‑nanometre scale, with energy costs that are orders of magnitude lower than in conventional memory technologies.

Researchers from Tel Aviv University, SlideTro and National Institute for Materials Science recently demonstrated ultra‑low‑energy, fully reversible control over the internal stacking order of graphene, pointing to a new class of slidetronic, multi‑ferroic devices. Their approach enables deterministic switching between different graphitic polytypes – such as transforming Bernal tetralayers into rhombohedral crystals – in nanoscale islands only a few tens of nanometers across, using lateral shear forces below 1 nanonewton and less than 1 femtojoule of energy per switching event.Graphitic polytypes are unique stacking arrangements of graphene layers, and they strongly affect the material’s behavior: electrical conductivity, response to magnetic fields, intrinsic polarization, and even the emergence of unconventional superconductivity can all change when the stacking is reconfigured. Until now, controlled switching between these stacking states required micrometre‑scale domains and relatively large, micronewton‑scale forces, which made practical devices unrealistic. In the new work, the team overcomes this limitation by designing tiny graphene “islands” whose stacking can be switched cleanly and reversibly at the 30‑nanometre scale, with energy costs that are orders of magnitude lower than in conventional memory technologies.

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