Robust Metal and Semiconductor Phase Transition Memristor Using Ag‐Intercalated Transition Metal Dichalcogenide
Metal (1T/1T')-semiconductor (2H) phase transition memristors (PTMEMs) based on intercalated alkali metal ions (Li<sup>+</sup>) in transition metal dichalcogenides (TMDs) exhibit excellent electrical properties, including heterosynaptic plasticity. However, the low stability of Li<sup>+</sup> ions limits retention and on/off ratios of the PTMEMs. Here, a phase transition in MoTe<sub>2</sub> induced by intercalated Ag<sup>+</sup> ions is demonstrated for the first time, enabling robust memristor operation. The migration of Ag<sup>+</sup> ions, controlled by voltage biases, clearly realizes reversible 2H-1T/1T' phase transitions, as revealed by transmission electron microscopy, X-ray photoelectron spectroscopy, and Raman mapping. The memristive mechanism of MoTe<sub>2</sub> shifts from doping (4-8 h) to phase transition (12 h) as Ag intercalation time increases, achieving a 200 000 on/off ratio at a 4 nm thickness. MoTe<sub>2</sub> exhibits the most evident phase transition due to its low transition barrier (0.84) compared to other TMDs (>1.22). Intercalated Ag<sup>+</sup> ions provide outstanding memristive performance over Li<sup>+</sup> ions, with a 100 times higher on/off ratio, 300 times better retention, and 8 times lower non-linearity (β<sub>Ag</sub> = 0.5-0.6, β<sub>Li</sub> = 4.0). Ag<sup>+</sup>MoTe<sub>2</sub> PTMEM achieves 91.7% accuracy in MNIST recognition, surpassing the 81.7% accuracy of Li<sup>+</sup>MoS<sub>2</sub> PTMEM. These findings demonstrate that Ag<sup>+</sup>MoTe<sub>2</sub> PTMEM holds great potential for advanced memory-based neural network applications.
Authors
- Heejun Yang (ORCID: https://orcid.org/0000-0003-0502-0054)
- Ji Son (ORCID: https://orcid.org/0000-0002-5130-4763)
- Whan Kyun Kim (ORCID: https://orcid.org/0009-0001-3516-0886)
- Woo Jong Yu (ORCID: https://orcid.org/0000-0002-7399-307X)
- Gil Young Cho (ORCID: https://orcid.org/0000-0003-4957-3013)
- Min Seok Kim
- Thi Thanh Huong Vu
- Yong Seon Shin
- Yong Ha Shin
- Huang Yun
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Samsung (South Korea) (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Advanced Materials
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1002/adma.202516115
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46