Resistive switching memories featuring neuromorphic functionality for advanced wearable electronic platforms
Recent advancements have established stretchable electronics as essential elements for flexible paper-like displays, wearable technology, artificial skin interfaces, and medical implants. Wearable platforms have continuous bending and twisting, requiring embedded circuits to endure deformation while maintaining functionality. Among potential memory options, resistive switching devices present the most appropriate choice for elastic storage. This is due to the straightforward metal-insulator-metal configuration arranged in a crossbar grid, which uses oxygen vacancy migration or metallic filament growth to establish stable conductive routes. The increasing need for interconnected personal electronics drives progress in scalable fabrication methods that significantly reduce costs while enhancing mechanical flexibility. This article details the selection of materials and architectural concepts that allow for flexible resistive switching memories, followed by a discussion on their electrical behavior and the fundamental principles of resistive switching. The descriptions include selections of electrodes, soft dielectric layers, stretchable crossbar configurations, and memristive components that mimic synaptic action. Flexible memristors also play a crucial role in embedded sensor networks by integrating signal conditioning with non-volatile recording capabilities. Although the development stage is still in its early stages, the experimental data indicate significant potential for rapid advancements. • Structural strategies like wrinkling, pre-stretched elastomers, and island-bridge design make fragile electronics durable. • Material-oriented strategies employ intrinsically stretchable electronic materials such as Au and Ag nanometals. • The resistive switching memories and neuromorphic functions can be integrated into woven textile structure.
Authors
- Hyojung Kim (ORCID: https://orcid.org/0009-0002-7120-2243)
Institutions
- Sejong University (KR)
Publication Details
- Journal
- Journal of Science Advanced Materials and Devices
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.jsamd.2025.101007
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73