Shining light on devices: New perspectives in non-volatile memory device and material investigation
Non-volatile memory (NVMe) technologies, including resistive random access memory (RAM), 2D memristors, and ferroelectric RAM, offer major improvements in data storage and computational systems. Despite tremendous promise, these technologies present several obstacles to large-scale commercialization, especially related to material stability, switching processes, and performance consistency. Conventional methods such as as transmission electron microscopy, scanning electron microscopy, scanning tunneling microscopy, conductive atomic force microscopy, and XPS, among several others, provide basic insights into structural and compositional features. However, these approaches typically demonstrate limits in recording dynamic, ambient measurements, in-operando opto-electrical, and quantum-scale processes. Emerging characterization methods, such as nanoscale plasmonic microscopy, plasmon-enhanced probe microscopy, interferometric scattering microscopy, and atomic plasmonic switches, provide atomistic, ambient temperature and pressure, simultaneous optical and electrical probing methods with competitive resolution and sensitivity. These approaches allow real-time monitoring of nanoscale electrical transitions, defect dynamics, and interfacial morphology, which are important to understanding NVMe device performance. This review presents current and innovative characterization methodologies for comprehensively assessing NVMe material qualities and operational features and suggests that enhanced characterization approaches are critical for unraveling underlying processes influencing NVMe functioning. By combining several analytical approaches, researchers may solve important difficulties and speed the development of next-generation non-volatile memory systems.
Authors
- Atif Jan (ORCID: https://orcid.org/0000-0001-5746-3450)
- D. M. Kelly (ORCID: https://orcid.org/0000-0002-6315-7342)
- Giuliana Di Martino (ORCID: https://orcid.org/0000-0001-5766-8384)
Institutions
- University of Cambridge (GB)
Publication Details
- Journal
- APL Electronic Devices
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1063/5.0255061
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 3.65
Funders
- Royal Society
- Cambridge Trust
- Engineering and Physical Sciences Research Council