English

Harnessing Ferro-Valleytricity in Penta-Layer Rhombohedral Graphene for Memory and Compute

Mesoscale and Nanoscale Physics 2024-08-05 v1 Hardware Architecture

Abstract

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving penta-layer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable Ferro-valleytricity of penta-layer rhombohedral graphene to develop non-volatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less non-volatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, non-destructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ~ 6 nW required per write operation and ~ 2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed non-volatile memory array without modifying the peripheral circuitry. Our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

Keywords

Cite

@article{arxiv.2408.01028,
  title  = {Harnessing Ferro-Valleytricity in Penta-Layer Rhombohedral Graphene for Memory and Compute},
  author = {Md Mazharul Islam and Shamiul Alam and Md Rahatul Islam Udoy and Md Shafayat Hossain and Kathleen E Hamilton and Ahmedullah Aziz},
  journal= {arXiv preprint arXiv:2408.01028},
  year   = {2024}
}