TY - GEN
T1 - Improved Synaptic Characteristics in Bilayer Memristor by Post-Oxide Deposition Annealing for Pattern Recognition
AU - Kumar, Dayanand
AU - Huang, Yi Rong
AU - Pal, Pratibha
AU - Saleem, Aftab
AU - Singh, Amit
AU - Lee, Hoonkyung
AU - Wang, Yeong Her
AU - Tseng, Tseung Yuen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present highly stable bilayer HfO2/TiO2 memristive synapse for neuromorphic computing applications. The memristive synapse shows repetitive 220 Long-term potentiation and depression cycles without any breakdown with total number of 220k pulses. The nonlinear values of potentiation and depression are 2.52 and -2.63, respectively with 1000 conductance pulses (500 pulses of potentiation and 500 pulses of depression). The experimental data of potentiation and depression was used to train HNN for pattern recognition of 28×28 pixels comprising 784 synapses. In 16 iterations, the HNN can be successfully trained to identify the input image with a training accuracy of about 97%. Moreover, the device has good retention (104 s) at 120 °C. These excellent synaptic characteristics of annealed device allows it for artificial intelligence systems in near future.
AB - We present highly stable bilayer HfO2/TiO2 memristive synapse for neuromorphic computing applications. The memristive synapse shows repetitive 220 Long-term potentiation and depression cycles without any breakdown with total number of 220k pulses. The nonlinear values of potentiation and depression are 2.52 and -2.63, respectively with 1000 conductance pulses (500 pulses of potentiation and 500 pulses of depression). The experimental data of potentiation and depression was used to train HNN for pattern recognition of 28×28 pixels comprising 784 synapses. In 16 iterations, the HNN can be successfully trained to identify the input image with a training accuracy of about 97%. Moreover, the device has good retention (104 s) at 120 °C. These excellent synaptic characteristics of annealed device allows it for artificial intelligence systems in near future.
UR - http://www.scopus.com/inward/record.url?scp=85130461520&partnerID=8YFLogxK
U2 - 10.1109/VLSI-TSA54299.2022.9771007
DO - 10.1109/VLSI-TSA54299.2022.9771007
M3 - Conference contribution
AN - SCOPUS:85130461520
T3 - 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022
BT - 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022
Y2 - 18 April 2022 through 21 April 2022
ER -