3D Stacking Enabled Non‐Volatile Multi‐Level Optically/Electrically Writeable/Readable Memory
Abstract In an era marked by rapid information development and the thriving growth of artificial intelligence, the demand for data communication is not only for faster transmission speeds but also for increased storage and security requirements. In this study, a first attempt of 3D stacking photoelectric memory device composed of quantum dot light‐emitting diodes (QLEDs) (ITO/P3HT‐COOH/(CdSe/ZnS) QDs/ZnO/Ag), resistive random‐access memory (RRAM) (Ag/PMMA/Au), and organic solar cells (OSCs) (Au/ZnO/P3HT:PCBM/MoO 3 /Ag) is presented. The photoelectric memory device can output both optical and electrical signals by tuning the input electric voltage and/or the intensity of external light sources. When the RRAM is in a high‐resistance state, its high resistance prevents the QLED from emitting light. The QLED will only emit light when the RRAM is activated and/or the OSC is exposed to a sufficiently strong external light source. The photoelectric memory exhibits multi‐level performance by allowing for the tuning of optical and electrical signal outputs through varying the external voltage and/or the intensity of external light sources which leads to higher storage density and faster transmission speeds. Additionally, it also features a high ON/OFF ratio and enables parallel optical‐electrical reading. This innovative technology paves a way for broader prospects in future advanced science and technologies.
Authors
- Fang‐Chi Hsu
- Ji‐Lin Shen (ORCID: https://orcid.org/0000-0002-6211-5247)
- Yang‐Fang Chen (ORCID: https://orcid.org/0000-0003-1203-5115)
- Yu‐Chieh Chao (ORCID: https://orcid.org/0000-0002-6346-975X)
- Jiawei Wu (ORCID: https://orcid.org/0000-0003-0273-1686)
- Jiayu Lin (ORCID: https://orcid.org/0000-0002-9525-397X)
- Chia‐Chun Ho
- Meng‐Ching Lai
Institutions
- National United University (TW)
- Chung Yuan Christian University (TW)
- National Taiwan University (TW)
Publication Details
- Journal
- Advanced Materials Technologies
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1002/admt.202401428
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73