Flexible Parylene C-Based RRAM Array for Neuromorphic Applications

Jo Eun Kim, Boram Kim, Hui Tae Kwon, Jaesung Kim, Kyungmin Kim, Dong Wook Park, Yoon Kim

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Resistive random-access memory (RRAM) has been explored to implement neuromorphic systems to accelerate neural networks. In this study, an RRAM crossbar array using parylene C (PPXC) as both a resistive switching layer and substrate was fabricated. PPXC is a flexible and transparent polymer with excellent chemical stability and biocompatibility. We studied PPXC-based RRAM devices with Ti/PPX-C/Cu and Cu/PPX-C/Ti structures. Devices with the Ti/PPX-C/Cu structure offer stable electrical and mechanical characteristics, such as a low set voltage of < 1 V, good retention time of $> 10^{4}$ s, endurance cycles of >300, conductance ON/OFF ratio >10, and can withstand >350 mechanical bending cycles. Additionally, the switching and conduction mechanisms of the devices were carefully investigated by analyzing their electrical, structural, and chemical properties. Finally, we demonstrated the feasibility of the fabricated RRAM array for neuromorphic applications through system-level simulations using the Modified National Institute of Standards and Technology database. The simulation results reflecting the variations of realistic devices demonstrated that the artificial neural network developed using the PPXC-based RRAM array works satisfactorily in pattern recognition tasks. The findings of this study can aid in the development of future wearable neuromorphic systems.

Original languageEnglish
Pages (from-to)109760-109767
Number of pages8
JournalIEEE Access
StatePublished - 2022


  • Neuromorphic
  • RRAM
  • artificial neural network
  • flexible neuromorphic electronics
  • memristor
  • parylene C


Dive into the research topics of 'Flexible Parylene C-Based RRAM Array for Neuromorphic Applications'. Together they form a unique fingerprint.

Cite this