Logic-in-memory based on an atomically thin semiconductor

ORAL

Abstract

Non-von Neumann architectures are now emerging alternatives to traditional processors for specialized applications where energy efficiency becomes a critical parameter. In particular, brain-inspired architectures show promise in efficiently targeting reconfigurable hardware and matrix-vector computations. Although this is a broad area of research that explores co-designing new systems and devices, a solid material platform that suppresses all technology’s needs has not yet been found. In this regard, two-dimensional materials are a promising class of materials due to their excellent electrical and mechanical properties as well as the emergence of new phenomena that could be exploited to create new non-von Neumann architectures. Here we demonstrate the use of MOCVD grown monolayer MoS2 as the semiconductor channel for floating-gate memories. We exploit the fabricated memories as a programmable inverter taking advantage of its precise channel’s conductance control. Next, we demonstrate a programmable NOR gate and we further propose an architecture that can produce a complete set of operations. Our results open a new path towards efficient reconfigurable hardware.

*We acknowledge support from the European Union's Horizon 2020 research and innovation programme under grant agreements 829035 (QUEFORMAL), 785219 and 881603 (Graphene Flagship Core 2 and Core 3), from the Marie Curie-Sklodowska COFUND (665667), from the H2020 European Research Council (ERC, grant 682332) as well as from the CCMX Materials Challenge grant 'Large-area growth of 2D materials for device integration'.

Publication: Migliato Marega, G., Zhao, Y., Avsar, A. et al. Logic-in-memory based on an atomically thin semiconductor. Nature 587, 72–77 (2020). https://doi.org/10.1038/s41586-020-2861-0

Presenters

  • Guilherme Migliato Marega

    • Ecole Polytechnique Federale de Lausanne

Authors

  • Guilherme Migliato Marega

    • Ecole Polytechnique Federale de Lausanne
  • Yanfei Zhao

    • École Polytechnique Fédérale de Lausanne
  • Ahmet Avsar

    • Ecole Polytechnique Federale de Lausanne
  • Zhenyu Wang

    • École Polytechnique Fédérale de Lausanne
  • Mukesh Tripathi

    • École Polytechnique Fédérale de Lausanne
  • Aleksandra Radenovic

    • École Polytechnique Fédérale de Lausanne
  • Andras Kis

    • Ecole Polytechnique Federale de Lausanne