2D Material-Based Memristors for Low Energy Consumption Artificial Synapses for Neuromorphic Responses

Abstract

The potential of two-dimensional (2D) transition metal dichalcogenides (TMDs), especially molybdenum telluride (MoTe₂), in sophisticated electrical and low-energy neuromorphic applications, has attracted a lot of interest. The creation, characteristics, and uses of MoTe₂-based memristive devices are summarized in this review paper, with an emphasis on their potential as artificial synapses for neuromorphic computing. We thoroughly examine the special properties of MoTe₂, such as its remarkable resistance switching response, excellent linearity in synaptic potentiation, and customizable phase states. These characteristics make it possible to implement basic computational functions with minimal energy consumption, including decimal arithmetic operations and the commutative principles of addition and multiplication. In addition to simulating intricate synaptic processes such as long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), the article emphasizes experimental performances of MoTe₂ memristors, which include their capacity to execute exact decimal arithmetic operations. The demonstration of centimeter-scale 2D MoTe₂ film-based memristor arrays attaining over 90% recognition accuracy in handwritten digit identification tests further demonstrates the devices great scalability, stability, and incorporation capabilities. Notwithstanding these developments, issues such poor environmental robustness, phase transition sensitivity, and low thermal stability still exist. The creation of hybrid or composite materials, doping, and structural alteration are some of the methods to get beyond these obstacles that are covered in the paper. The need for scalable, economical synthesis techniques and a better comprehension of the materials mechanical, optical, and electrical properties through modeling and experiments are emphasized.