TY - GEN
T1 - Implementation of hardware-based neural network using memristors with abrupt SET and gradual RESET characteristics
AU - Pyo, Yeon
AU - Nahm, Sahn
AU - Jeong, Jichai
AU - Shin, Jaeyoung
N1 - Funding 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.
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
KW - memristor
KW - neural network
KW - neuromorphic system
KW - synapse
UR - http://www.scopus.com/inward/record.url?scp=85084058524&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084058524&partnerID=8YFLogxK
U2 - 10.1109/BCI48061.2020.9061641
DO - 10.1109/BCI48061.2020.9061641
M3 - Conference contribution
AN - SCOPUS:85084058524
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
Y2 - 26 February 2020 through 28 February 2020
ER -