Transition Metal Dichalcogenides-Based Memristors for Neuromorphic Electronics

Authors

  • Xiaofei Wu Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
  • Prashant Dhakal Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
  • Jae Gwang Kim Department of Material Science and Engineering, Texas A&M University, College Station, TX, USA
  • Aolin Hou Department of Material Science and Engineering, Texas A&M University, College Station, TX, USA
  • Shiren Wang Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA

Keywords:

TMD materials, memristors, artificial synapses, neuromorphic computing, neuromorphic electronics

Abstract

Due to the rapid advancement of neuromorphic computing, novel hardware is required to achieve high energy efficiency and performance. Memristors, a new electronic device, have attracted considerable interest owing to their excellent non-volatile properties. These days, scientists are starting to focus on TMD materials as proper materials for making memristors to achieve neuromorphic computing due to their atomically thin nature, higher carrier mobility, weak Van der Waals (vdW) force between layers, bandgap tunability, and ability to possess multiple polymorphs. This article summarizes the latest developments in TMD material-based memristors as artificial synapses, focusing on three common mechanisms: filament formation, phase transition, and charge-trapping. This paper highlights how TMD materials contribute to achieving memristor’s resistive switching (RS) behavior, emulating biological synapses, and advancing neuromorphic electronics. Furthermore, the current challenges and future directions in this research area are discussed, indicating the potential for breakthroughs in neuromorphic computing.

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Published

2024-05-08

How to Cite

Wu, X., Dhakal, P., Kim, J. G., Hou, A., & Wang, S. (2024). Transition Metal Dichalcogenides-Based Memristors for Neuromorphic Electronics . Journal of Neuromorphic Intelligence, 1(1). Retrieved from https://sci-access.org/index.php/nsej/article/view/4