Recent advance of artificial intelligence comes from the rapid growth of artificial neural network technology. Memristors play a crucial role in the hardware implementation of artificial neural networks, thanks to the multilevel conductance of memristors by switching behaviors. Here, we propose a synaptic device with a Pr0.7Ca0.3MnO3 (PCMO) switching layer which shows the abrupt SET and gradual RESET switching characteristics. Improved linearity of the synaptic transmission caused by switching characteristics can enhance the classification performance of neuromorphic systems.
|Title of host publication||8th International Winter Conference on Brain-Computer Interface, BCI 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2020 Feb|
|Event||8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of|
Duration: 2020 Feb 26 → 2020 Feb 28
|Name||8th International Winter Conference on Brain-Computer Interface, BCI 2020|
|Conference||8th International Winter Conference on Brain-Computer Interface, BCI 2020|
|Country/Territory||Korea, Republic of|
|Period||20/2/26 → 20/2/28|
Bibliographical noteFunding Information:
This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00451) and the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology under Grant NRF-2018R1D1A1B07042378.
© 2020 IEEE.
- neural network
- neuromorphic system
ASJC Scopus subject areas
- Behavioral Neuroscience
- Cognitive Neuroscience
- Artificial Intelligence
- Human-Computer Interaction