Two‐Dimensional Materials for Flexible Neuromorphic Devices: Materials, Engineering Strategies, Architectures, and Emerging Applications
ABSTRACT Two‐dimensional (2D) materials offer a distinctive foundation for flexible neuromorphic electronics by combining atomically thin structures, mechanical compliance, and tunable electronic, ionic, and interfacial properties. These features are essential for artificial synapses, memristors, synaptic transistors, and bio‐inspired devices that require low operating voltage, reduced energy consumption, stable conductance modulation, and reliable operation under mechanical deformation. This review provides a materials‐centered analysis of recent progress in 2D‐material‐based flexible neuromorphic devices. Primary 2D material platforms, including graphene‐derived sheets, MXenes, transition‐metal dichalcogenides, black phosphorus, and other layered or nanosheet systems, are distinguished from 2D‐like CNT‐network films and hybrid functional layers and are compared according to their dimensionality, structural form, and device role. Defect engineering, van der Waals heterostructure design, and surface functionalization are then examined as key routes for regulating charge transport, ion migration, synaptic linearity, mechanical robustness, and environmental stability. Flexible memristive devices, synaptic transistors, and bio‐integrated neuromorphic systems are further discussed within this material‐to‐device framework. By clarifying the links among material properties, engineering strategies, device architectures, neuromorphic metrics, and deformation‐stable functionality, this review provides mechanism‐guided design principles for scalable, energy‐efficient, and application‐oriented 2D neuromorphic platforms.
Authors
- Letian Dai (ORCID: https://orcid.org/0000-0002-4518-2368)
- Junzhe Tang (ORCID: https://orcid.org/0009-0007-3396-8981)
Institutions
- Lanzhou University of Technology (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- Lanzhou City University (CN)
- Hubei University of Technology (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Advanced Electronic Materials
- Published
- 2026-08-24
- DOI
- https://doi.org/10.1002/aelm.70543
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Hubei Province