HfOx-Based Conductive Bridge Random Access Memory with Al2O3 Sandglass Nanostructures via Glancing Angle Deposition Technology toward Neuromorphic Applications

Ying Chun Shen, Yu Wen Huang, Tzu Yi Yang, Yi Jen Yu, Hao Chung Kuo, Tseung Yuen Tseng*, Yu Lun Chueh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Conductive bridge random access memory (CBRAM) is one of the promising nonvolatile memories for next-generation technology owing to its high density, low power consumption, and fast switching speed, which is also a potential candidate for implementation of neuromorphic computing. However, CBRAMs suffer from stochastically growing conducting filaments in the insulator layer. Herein, we demonstrated Al2O3 sandglass nanostructures (SNGSs) embedded into HfOx-based CBRAMs via glancing angle deposition technology with the AlN thermal enhanced layer to prevent the overinjection of cations and localize the growth of conducting filaments in the HfOx switching layer. With the assistance of Al2O3 SNGSs and the AlN layer, the Cu/Al2O3 SNGSs/HfOx/AlN/TiN device exhibited a stable on/off ratio of >10 for more than 6000 cycles. Furthermore, with a Te top electrode, the Te/Al2O3 SNGSs/HfOx/AlN/TiN device shows a multilevel cell characteristic by controlling compliance currents. In addition, it possesses excellent potentiation and depression nonlinearities of 1.28 and 0.4, respectively, which is beneficial for future applications in neuromorphic computing.

Original languageEnglish
JournalACS Applied Nano Materials
DOIs
StateAccepted/In press - 2023

Keywords

  • conductive bridge random access memory
  • glancing angle deposition
  • neuromorphic computing system
  • sandglass nanostructures
  • synaptic plasticity
  • thermal enhanced layer

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