Work

Compact modeling of Pd–MoS 2 self-rectifying RRAM based on modulated Schottky barrier equation

Year: 2026

Type: article

Abstract: The rapid growth of artificial intelligence computing has increased the demand for energy-efficient hardware accelerators based on crosspoint memory arrays. Resistive random-access memory (RRAM) is a promising candidate for such architectures, but sneak-path current remains a critical obstacle to scalable and reliable array operation. In this work, a palladium (Pd)-molybdenum disulfides (MoS 2 )-based self-rectifying RRAM device was fabricated and experimentally characterized, and a physics-informed phenomenological compact model was developed for circuit-level array evaluation. The device exhibited asymmetric bipolar resistive switching caused by Schottky-barrier-related injection asymmetry at the metal/MoS 2 interface. To reproduce this behavior, a modulated thermionic-emission formulation based on the Richardson–Dushman equation was incorporated into the conventional Lehtonen–Laiho framework. The 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. The validated model was implemented in SPICE crosspoint arrays to compare non-rectifying and self-rectifying RRAM under identical HRS/LRS states, read schemes, and array sizes. Simulations were performed for 5× 5, 10× 10, 25× 25, and 32× 32 arrays using 2/3 V read , 1/2 V read , and 1/3 V read schemes. The 1/3 V read scheme provided the largest read margin, and the self-rectifying array maintained a read margin above 10% up to 10× 10. Region-resolved power analysis further confirmed reduced reverse-bias parasitic current and lower array-level power consumption compared with the non-rectifying reference. These results demonstrate that the proposed compact model provides a practical framework for evaluating read reliability, power consumption, and scalability of self-rectifying RRAM crosspoint arrays.

Source: Nanotechnology

DOI: 10.1088/1361-6528/ae83c0

Authors: Yoojin Shin, DongJun Jang, Min-Woo Kwon

Institutions: Seoul National University of Science and Technology, Sogang University

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,325

Funder: National Research Foundation of Korea