Memristive system in 2D materials: redefining the landscape of future nanoelectronics

Memristors, recognized as the fourth fundamental circuit element, have emerged as a transformative technology in non-volatile memory and neuromorphic computing. Their inherent ability to store information through resistive states and emulate synaptic behaviour makes them highly promising for energy-efficient, brain-inspired electronics. Recent advancements have demonstrated that integrating two-dimensional (2D) nanomaterials-such as transition metal dichalcogenides, graphene, and hexagonal boron nitride (h-BN)-can significantly enhance memristor performance due to their atomic-scale thickness, high carrier mobility, and tunable electronic properties. These 2D material-based memristors exhibit improved switching speed, endurance, and scalability, making them ideal candidates for next-generation computing architectures. This review provides a comprehensive overview of recent developments in memristor devices using 2D nanomaterials, including discussions on material properties, defect engineering, switching mechanisms, and device configurations. Furthermore, it examines key applications in neuromorphic systems, logic-in-memory (LiM) computing, and flexible electronics. Despite rapid progress, challenges remain in achieving large-scale uniformity, reliable integration with complementary metal-oxide-semiconductor technology, and long-term stability. Addressing these issues requires synergistic efforts in material science, device engineering, and computational modelling. Finally, the review outlines future research directions and strategies to harness the full potential of 2D material-based memristors for scalable, low-power, and intelligent electronic systems.

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Publication Details

Journal
Nanotechnology
Published
2026-02-09
DOI
https://doi.org/10.1088/1361-6528/ae435a
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Memristive system in 2D materials: redefining the landscape of future nanoelectronics

Prerona Singha, Pradip Kumar Kumar Kalita
Nanotechnology
Advanced Memory and Neural Computing
article

Memristive system in 2D materials: redefining the landscape of future nanoelectronics

Prerona Singha, Pradip Kumar Kumar Kalita
article en

Abstract

Memristors, recognized as the fourth fundamental circuit element, have emerged as a transformative technology in non-volatile memory and neuromorphic computing. Their inherent ability to store information through resistive states and emulate synaptic behaviour makes them highly promising for energy-efficient, brain-inspired electronics. Recent advancements have demonstrated that integrating two-dimensional (2D) nanomaterials-such as transition metal dichalcogenides, graphene, and hexagonal boron nitride (h-BN)-can significantly enhance memristor performance due to their atomic-scale thickness, high carrier mobility, and tunable electronic properties. These 2D material-based memristors exhibit improved switching speed, endurance, and scalability, making them ideal candidates for next-generation computing architectures. This review provides a comprehensive overview of recent developments in memristor devices using 2D nanomaterials, including discussions on material properties, defect engineering, switching mechanisms, and device configurations. Furthermore, it examines key applications in neuromorphic systems, logic-in-memory (LiM) computing, and flexible electronics. Despite rapid progress, challenges remain in achieving large-scale uniformity, reliable integration with complementary metal-oxide-semiconductor technology, and long-term stability. Addressing these issues requires synergistic efforts in material science, device engineering, and computational modelling. Finally, the review outlines future research directions and strategies to harness the full potential of 2D material-based memristors for scalable, low-power, and intelligent electronic systems.

NanotechnologyVol. 37(7)
Rajiv Gandhi University (IN)
Affordable and clean energy
Openalex Percentile: Top 60%
Advanced Memory and Neural Computing
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