Aguirre, F.L.; Piros, E.; Kaiser, N.; Vogel, T.; Petzold, S.; Gehrunger, J.; Oster, T.; Hochberger, C.; Suñé, J.; Alff, L.;
et al. Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks. Micromachines 2022, 13, 2002.
https://doi.org/10.3390/mi13112002
AMA Style
Aguirre FL, Piros E, Kaiser N, Vogel T, Petzold S, Gehrunger J, Oster T, Hochberger C, Suñé J, Alff L,
et al. Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks. Micromachines. 2022; 13(11):2002.
https://doi.org/10.3390/mi13112002
Chicago/Turabian Style
Aguirre, Fernando Leonel, Eszter Piros, Nico Kaiser, Tobias Vogel, Stephan Petzold, Jonas Gehrunger, Timo Oster, Christian Hochberger, Jordi Suñé, Lambert Alff,
and et al. 2022. "Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks" Micromachines 13, no. 11: 2002.
https://doi.org/10.3390/mi13112002
APA Style
Aguirre, F. L., Piros, E., Kaiser, N., Vogel, T., Petzold, S., Gehrunger, J., Oster, T., Hochberger, C., Suñé, J., Alff, L., & Miranda, E.
(2022). Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks. Micromachines, 13(11), 2002.
https://doi.org/10.3390/mi13112002