Atom-Vacancy-Defect-Derived Electric Hysteresis Loops and Stochastic Low-Frequency Noises in Few-Atom Layer MoS 2

• Published in: ACS applied materials & interfaces 2024-11, Vol.16 (46), p.64190-64196
• Main Authors: Kosugi, Mioko, Furuichi, Shunta, Lin, Yung-Chang, Kobayashi, Yusuke, Takaki, Keita, Kikkawa, Takashi, Taniguchi, Takashi, Watanabe, Kenji, Kohno, Takashi, Suenaga, Kazu, Saitoh, Eiji, Maruyama, Shigeo, Haruyama, Junji
• Format: Article
• Language: English
• ISSN: 1944-8244; 1944-8252
• DOI: 10.1021/acsami.4c13147

Atom-vacancy-defects present in various materials yield numerous interesting physical phenomena, even obstructing high performance in some cases. On the other hand, their valuable applications to novel devices, such as nitrogen vacancy centers in diamond for quantum bits, have gathered significant attention. In particular, these tendencies become more substantial in two-dimensional (2D) (atomically) thin van der Waals layers. However, correlations with various kinds of atom defects are still under exploration. Herein, we find the stochastic behaviors of large hysteresis loops with strong photoresponse in the static electrical properties in few-atom layer semiconductors, molybdenum disulfide (MoS ). The temperature dependence and transmission electron microscopy reveal that they arise from pairs of two neighboring in-plane S-vacancy defects, which predominantly present only around the interface at the MoS flake/substrate, with activation energies ∼0.35 eV. The low-frequency ( ) (LF) noise measurements clarify a high shift in the two 1/ -dependent regimes, implying stochastic behaviors of electric charges through the S-vacancy pairs with high-speed charge(spin) transitions across low kinetic energy barriers between narrow discrete states. The shallow energy sates are formed from the highly uniform S-vacancy pairs interacting with Mo atoms, which act like quantum dots. The observed stochastic operation holds promise for various application, particularly for probabilistic neuromorphic computation in artificial intelligence.