Improved self-rectifying characteristics observed in ZnO/IGZO bilayer RRAM cells using eco-friendly indirect post-treatment
Research on transparent RRAM (T-RRAM) is imperative for achieving high integration levels, necessitating the resolution of interference issues arising from sneak-path currents in the array. Here, we propose a fully transparent ITO/ZnO/IGZO/ITO device structure featuring a ZnO resistive switching (RS) layer and an IGZO rectifying layer, as well as an eco-friendly indirect treatment method, i.e., microwave treatment (MWT), demonstrating self-rectifying RS characteristics capable of overcoming interference problems without supplementary elements. In detail, the proposed T-RRAM exhibits superior transmittance (>80 %) in the visible region, uniform RS of >10 2 cycles, and stable retention for >10 4 s. The device particularly showed a read margin of 1,700, indicating the reliable operation of RS up to 41 × 41 without any degradation in the array structure. These findings suggest the potential for developing superior rectification properties for eco-friendly advanced industries by incorporating ZnO/IGZO bilayers and the post-MWT method. • Demonstration of RRAM devices based on eco-friendly treatment method with self-rectifying characteristics. • The proposed transparent bilayer T-RRAM guarantees prolonged operation stability due to its superior transmittance of 83 %. • Achieved a read margin of 1700 with the proposed device treated by the MWT method for 120 s. • Ensured reliability up to a 41 x 41 crossbar array without interference.
Authors
- Myoungsu Chae (ORCID: https://orcid.org/0000-0001-5067-7990)
- Hee‐Dong Kim (ORCID: https://orcid.org/0000-0003-1555-7509)
- Doowon Lee (ORCID: https://orcid.org/0000-0001-6635-5953)
- Yuseong Jang (ORCID: https://orcid.org/0000-0002-9057-482X)
Institutions
- Kongju National University (KR)
- Sejong University (KR)
- The University of Tokyo (JP)
Publication Details
- Journal
- Materials Today Sustainability
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1016/j.mtsust.2025.101105
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46