Flexible and Reliable Parylene‐C Resistive Random Access Memory Array with Graphene Barrier for Neuromorphic Systems
Flexible parylene‐C (PPXC)‐based resistive random access memory (RRAM) has garnered attention as a synaptic device suitable for flexible neuromorphic systems due to its low‐power consumption and high‐speed switching characteristics. However, Negative‐SET has been a key factor contributing to reliabi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2025-08-01
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| Series: | Small Structures |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/sstr.202500056 |
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| Summary: | Flexible parylene‐C (PPXC)‐based resistive random access memory (RRAM) has garnered attention as a synaptic device suitable for flexible neuromorphic systems due to its low‐power consumption and high‐speed switching characteristics. However, Negative‐SET has been a key factor contributing to reliability degradation by inducing the overgrowth of conductive filaments (CFs). To address this issue, this study fabricates a PPXC‐based RRAM crossbar array with a graphene barrier inserted between the inert electrode and the resistive switching layer. The incorporation of a graphene barrier layer effectively mitigates the excessive diffusion of metal ions, thereby significantly improving the stability of CF. Furthermore, the graphene layer plays a critical role in regulating the RESET process, ensuring enhanced device reliability. The fabricated devices, featuring a Pt/Graphene/PPXC/Cu structure, demonstrate superior electrical and mechanical performance, including a low operating voltage <2 V, endurance cycles >104, retention time >104 s, conductance ON/OFF ratio >102, and mechanical bending durability exceeding 500 cycles at a bending radius of 3 mm. Furthermore, artificial neural network simulations using the Modified National Institute of Standards and Technology database confirm its applicability as a neuromorphic system. This study establishes a technological foundation for the development of next‐generation flexible neuromorphic systems. |
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| ISSN: | 2688-4062 |