@misc{10481/91076,
year = {2024},
url = {https://hdl.handle.net/10481/91076},
abstract = {This work demonstrates laser-induced graphene
memristors, fabricated using a patterning-free, low cost and
simple process directly on a flexible polyimide substrate. The
fabricated memristors show repeatable non-volatile bipolar resistive switching with state retention up to 103 seconds. A simple
perceptron network for the classification of black-and-white
images is later implemented using an experimentally extracted
compact model. Successful training of the network by integrating
SPICE model with MATLAB shows the possibility to emulate
the on-chip learning process. Further, by properly modulating
the applied voltage pulse amplitude and period, a reduction in
the energy consumed by training the neural network is achieved.},
organization = {MCIN/AEI/10.13039/501100011033: PID2020-116518GB-I00, TED2021-129769B-I00 FlexPowHar, CNS2023-143727  RECAMBIO},
organization = {European Union NextGenerationEU/PRTR},
organization = {University, Research and Innovation Council of the Board of Andalusia  P21 00149 ENERGHENE},
organization = {European Union's Horizon 2020  No. 101032701},
title = {Flexible Laser-Induced Graphene Memristor: Fabrication and SPICE based Emulation of an Artificial Neural Network},
author = {García Palomo, Mikel and Ganeriwala, Mohit Dineshkumar and Fernández-Sánchez, María del Carmen and Motos-Espada, Roberto and González Marín, Enrique and Ruiz, Francisco G. and Godoy Medina, Andrés},
}