Accelerate and actualize: Can 2D materials bridge the gap between neuromorphic hardware and the human brain?

Abstract

2D materials represent an exciting frontier for devices and architectures beyond von Neumann computing due to their atomically small structure, superior physical properties, and ability to enable gate tunability. All four major classes of emerging non-volatile memory (NVM) devices (resistive, phase change, ferroelectric, and ferromagnetic) have been integrated with 2D materials and their corresponding heterostructures. Device performance for neuromorphic architectures will be compared across each of these classes, and applications ranging from crossbar arrays for multi-layer perceptrons (MLPs) to synaptic devices for spiking neural networks (SNNs) will be presented. To aid in the understanding of neuromorphic computing, the terms “acceleration” and “actualization” are used, with the former referring to neuromorphic systems that heighten the speed and energy efficiency of existing machine-learning models and the latter more broadly representing the realization of human neurobiological functions in non-von Neumann architectures. The benefits of 2D materials are addressed in both contexts. Additionally, the landscape of 2D materials-based optoelectronic devices is briefly discussed. These devices leverage the strong optical properties of 2D materials for actualization-based systems that aim to emulate the human visual cortex. Lastly, limitations of 2D materials are considered, with the progress of 2D materials as a novel class of electronic materials for neuromorphic computing depending on their scalable synthesis in thin-film form with desired crystal quality, defect density, and phase purity. 2D materials represent an exciting frontier for devices and architectures beyond von Neumann computing due to their atomically small structure, superior physical properties, and ability to enable gate tunability. All four major classes of emerging non-volatile memory (NVM) devices (resistive, phase change, ferroelectric, and ferromagnetic) have been integrated with 2D materials and their corresponding heterostructures. Device performance for neuromorphic architectures will be compared across each of these classes, and applications ranging from crossbar arrays for multi-layer perceptrons (MLPs) to synaptic devices for spiking neural networks (SNNs) will be presented. To aid in the understanding of neuromorphic computing, the terms “acceleration” and “actualization” are used, with the former referring to neuromorphic systems that heighten the speed and energy efficiency of existing machine-learning models and the latter more broadly representing the realization of human neurobiological functions in non-von Neumann architectures. The benefits of 2D materials are addressed in both contexts. Additionally, the landscape of 2D materials-based optoelectronic devices is briefly discussed. These devices leverage the strong optical properties of 2D materials for actualization-based systems that aim to emulate the human visual cortex. Lastly, limitations of 2D materials are considered, with the progress of 2D materials as a novel class of electronic materials for neuromorphic computing depending on their scalable synthesis in thin-film form with desired crystal quality, defect density, and phase purity.