Spike-time dependent plasticity of tailored ZnO nanorod-based resistive memory for synaptic learningopen access
- Authors
- Patil, Shubham V.; Mullani, Navaj B.; Nirmal, Kiran; Hyun, Gihwan; Alimkhanuly, Batyrbek; Kamat, Rajanish K.; Park, Jun Hong; Kim, Sanghoek; Dongale, Tukaram D.; Lee, Seunghyun
- Issue Date
- Dec-2023
- Publisher
- Elsevier B.V.
- Keywords
- Analog memristor; Nanochannel switching; Neuromorphic electron devices; Spike-time dependent plasticity
- Citation
- Journal of Science: Advanced Materials and Devices, v.8, no.4
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Science: Advanced Materials and Devices
- Volume
- 8
- Number
- 4
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/67626
- DOI
- 10.1016/j.jsamd.2023.100617
- ISSN
- 2468-2284
2468-2179
- Abstract
- 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. © 2023 Vietnam National University, Hanoi
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