Compact Modeling of Pd-MoS2 Self-rectifying RRAM based on modulated Schottky barrier equation
The rapid growth of artificial intelligence computing has intensified the demand for energyefficient hardware accelerators capable of large-scale matrix-vector multiplication. Resistive random-access memory has attracted significant interest for such applications due to its analog weight storage capability and compatibility with crosspoint array architectures. However, the sneak-path current remains a critical challenge that limits the scalability and reliability of highdensity RRAM arrays. In this work, a palladium (Pd)-MoS 2 based self-rectifying RRAM device was fabricated and experimentally characterized, and a physics-informed compact model is developed to quantitatively evaluate its sneak-pass current suppression capability at the array level. The device exhibited asymmetric bipolar resistive switching originating from Schottkybarrier-controlled carrier injection at the metal/MoS 2 interface. To accurately capture this intrinsic rectifying behavior, a modulated thermionic-emission formulation based on the Richardson-Dushman equation was incorporated into the conventional Lehtonen-Laiho framework. This formulation preserved the essential Schottky barrier physics while ensuring numerical stability in circuit-level simulations. The proposed compact model reproduced the measured current-voltage characteristics, including a rectification ratio of approximately 60, a memory window on the order of 10 3 , and stable bipolar switching behavior. Furthermore, by systematically varying key physical parameters, such as metal work function, MoS 2 electron affinity, and Fermi-level pinning factor, the model enabled predictive estimation of Schottky barrier height and corresponding rectification characteristics for various metal/MoS 2 combinations.
Authors
- Min-Woo Kwon (ORCID: https://orcid.org/0000-0002-6725-1969)
- DongJun Jang
- Yoojin Shin (ORCID: https://orcid.org/0009-0004-0199-5553)
Institutions
- Seoul National University of Science and Technology (KR)
- Sogang University (KR)
Publication Details
- Journal
- Nanotechnology
- Published
- 2026-06-29
- DOI
- https://doi.org/10.1088/1361-6528/ae83c0
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation of Korea