TY - GEN A1 - Romero-Zaliz, Rocio A1 - Cantudo, Antonio A1 - Perez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors T2 - Electronics (MDPI) N2 - We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on HfO2 dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed thatNNaccuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc. KW - RRAM KW - memristive device KW - neural network Y1 - 2021 U6 - https://doi.org/10.3390/electronics10243141 SN - 2079-9292 VL - 10 IS - 24 ER - TY - GEN A1 - Perez, Eduardo A1 - Maldonado, David A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez-Bosch Quesada, Emilio A1 - Cantudo, Antonio A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - A comparison of resistive switching parameters for memristive devices with HfO2 monolayers and Al2O3/HfO2 bilayers at the wafer scale T2 - 14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023 N2 - Memristive devices integrated in 200 mm wafers manufactured in 130 nm CMOS technology with two different dielectrics, namely, a HfO2 monolayer and an Al2O3/HfO2 bilayer, have been measured. The cycle-to-cycle (C2C) and device-todevice (D2D) variability have been analyzed at the wafer scale using different numerical methods to extract the set (Vset) and reset (Vreset) voltages. Some interesting differences between both technologies were found in terms of switching characteristics KW - RRAM Y1 - 2023 SN - 979-8-3503-0240-0 U6 - https://doi.org/10.1109/CDE58627.2023.10339417 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Maldonado, David A1 - Cantudo, Antonio A1 - Perez, Eduardo A1 - Romero-Zaliz, Rocio A1 - Perez-Bosch Quesada, Emilio A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - TiN/Ti/HfO2/TiN Memristive Devices for Neuromorphic Computing: From Synaptic Plasticity to Stochastic Resonance T2 - Frontiers in Neuroscience N2 - We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature.It is shown that this effect is important and greatly depends on the noise statistical characteristics. KW - RRAM KW - Neural network Y1 - 2023 U6 - https://doi.org/10.3389/fnins.2023.1271956 SN - 1662-4548 VL - 17 ER -