journal article·2025-06-24

Modeling of Conductive Filament Behaviors According to Inorganic Concentration Changes in Hybrid Polymeric Ultra-Thin Films

Ji In Kim, Yeong Kwon Kim, Byung Chul Jang, Min Ju Kim

ACS Applied Electronic Materials· Volume 7· Issue 12
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Abstract

Resistive random-access memory (ReRAM) has attracted considerable attention as a promising memory technology for the internet of things, artificial intelligence, and neuromorphic computing, driving the advancements in the Fourth Industrial Revolution. Among various approaches, ultrathin hybrid films fabricated via an initiated chemical vapor deposition (iCVD) process (iCVD) have emerged as viable candidates for ReRAM devices, offering tunable performance through precise control of the organic–inorganic ratios. However, electrical and chemical properties of hybrid-film-based ReRAM devices have not yet been systemically investigated, making it difficult to establish a comprehensive understanding of conductive filaments (CF) behavior that governs device performance. In this study, we systematically investigate the effects of Al, Hf, and Zr composition variations on the resistive switching behavior of ultrathin hybrid-film based ReRAM (H-ReRAM). Our results demonstrate that the inorganic content of H-ReRAMs gradually reduces the low-resistance state in all H-ReRAM. Conversely, while the high-resistance state (HRS) of Al H-ReRAMs decreases significantly, the HRS of Hf- and Zr-based H-ReRAMs increases, resulting in an improved on/off ratio. Furthermore, we propose a physics-based numerical model that elucidates CF dynamics as a function of the inorganic composition within the hybrid films. This model provides key insights into the material characteristics and switching mechanisms, offering a fundamental understanding that is crucial for optimizing H-ReRAM performance. Our findings contribute to the development of high-performance memory and neuromorphic devices, supporting the next generation of advanced computing systems.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Conducting polymers and applications · Advanced Sensor and Energy Harvesting Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Foundry Engineering, Dankook University, 152 Jukjeon-ro, Yong-in, Gyeonggi-do 16890, Republic of Korea

School of Electronics and Electrical Engineering Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea

Department of Semiconductor Convergence Engineering, Dankook University, 152 Jukjeon-ro, Yong-in, Gyeonggi-do 16890, Republic of Korea

Department of Electronics & Electrical Engineering, Dankook University, 152 Jukjeon-ro, Yong-in, Gyeonggi-do 16890, Republic of Korea

Funding

National Research Foundation of Korea · Dongil Culture and Scholarship Foundation

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