Journal of Science: Advanced Materials and Devices (Dec 2023)

Spike-time dependent plasticity of tailored ZnO nanorod-based resistive memory for synaptic learning

  • Shubham V. Patil,
  • Navaj B. Mullani,
  • Kiran Nirmal,
  • Gihwan Hyun,
  • Batyrbek Alimkhanuly,
  • Rajanish K. Kamat,
  • Jun Hong Park,
  • Sanghoek Kim,
  • Tukaram D. Dongale,
  • Seunghyun Lee

Journal volume & issue
Vol. 8, no. 4
p. 100617

Abstract

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Metal oxide resistive memory is a potential device that can substantially influence the current roadmap for nonvolatile memory and neuromorphic computing. However, common amorphous oxide-based resistive random-access memory suffers from high forming voltages that complicate circuit design and abrupt SET behavior incompatible with analog weight updates. To overcome such limitations, wurtzite ZnO nanorods were synthesized on a fluorine-doped tin oxide (FTO) substrate and a bipolar resistive memory with the Ag/w-ZnO/FTO stacking sequence was fabricated. The hexagonal NR morphology of w-ZnO with controlled vertical growth and nanochannel formation between the NRs were produced by in situ crystalline growth. This morphology enabled a forming-free switching and an analog switching effect that emulated neuromorphic functionalities such as potentiation–depression and complex spike-time dependent plasticity-based Hebbian learning rules. Importantly, the device exhibited nonabrupt switching behavior suitable for analog weight updates in neuromorphic computing in contrast to conventional resistive memory.

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