https://hdl.handle.net/10481/14179 Mon, 20 Apr 2026 07:35:22 GMT 2026-04-20T07:35:22Z https://hdl.handle.net/10481/91076 Flexible Laser-Induced Graphene Memristor: Fabrication and SPICE based Emulation of an Artificial Neural Network García Palomo, Mikel; Ganeriwala, Mohit Dineshkumar; Fernández-Sánchez, María del Carmen; Motos-Espada, Roberto; González Marín, Enrique; Ruiz, Francisco G.; Godoy Medina, Andrés 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. Project PID2020-116518GB-I00 funded by MCIN/AEI/10.13039/501100011033; and TED2021-129769B-I00 FlexPowHar and CNS2023-143727 RECAMBIO both funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR. This work also acknowledges the research project P21 00149 ENERGHENE funded by the University, Research and Innovation Council of the Board of Andalusia. M. D. Ganeriwala ac- knowledges funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101032701. https://hdl.handle.net/10481/91076 https://hdl.handle.net/10481/91050 Volatile modulation of oxygen vacancy-related dipoles in gate insulators as a mechanism for non-volatile memories Cuesta-López, Juan; Marín, Enrique G.; Toral López, Alejandro; Ganeriwala, Mohit Dineshkumar; Ruiz, Francisco G.; Pasadas Cantos, Francisco; Godoy Medina, Andrés We simulate voltage-driven ion migration in gate oxides as a potential mechanism to develop non-volatile memories (NVMs) as appropriate candidates for neuromorphic computing applications. Our study aims to give insights about the impact of ion mobility and ion concentration in the device memory window (MW). This work is funded by the FEDER/Junta de Andalucía through the projects A-TIC-646-UGR20 and P20-00633, and the Spanish Government MCIN/AEI/10.13039/501100011033 through the projects PID2020-116518GB-I00 and TED2021-129769B-I00 (NextGenerationEU/PRTR). F. Pasadas acknowledges funding from PAIDI 2020 and the European Social Fund Operational Programme 2014–2020 no. 20804. J. Cuesta-Lopez acknowledges the FPU program FPU019/05132, and M.D. Ganeriwala the EU through project H2020-MSCA-IF 2020. https://hdl.handle.net/10481/91050 https://hdl.handle.net/10481/91037 Physics-based scalable compact model for terminal charge, intrinsic capacitance and drain current in nanosheet FETs Singh, Aishwarya; Ganeriwala, Mohit Dineshkumar; Mohapatra, Nihar This work presents a physics-based SPICE compatible model for Nanosheet FETs, which provides explicit expressions for the drain current, terminal charges and intrinsic capacitances. The drain current model is based on the drift-diffusion formalism for carrier transport. The terminal charge and intrinsic capacitance models are calculated by adopting the Ward–Dutton linear charge partition scheme that guarantees charge conservation. The model uses the novel bottom-up approach to calculate the terminal charges, uses very few empirical parameters and is accurate across device dimensions and bias conditions. https://hdl.handle.net/10481/91037 https://hdl.handle.net/10481/79725 On the improvement of reliability in 2D devices for sensing Márquez González, Carlos https://hdl.handle.net/10481/79725 https://hdl.handle.net/10481/72680 MSCA contingency plan: the lifeguard during the pandemic Márquez González, Carlos https://www.mariecuriealumni.eu/newsletters/29th-mcaa-newsletter/rebuilding-bridges-msca-contingency-plan-lifeguard-during-pandemic https://hdl.handle.net/10481/72680