@misc{PerezMaldonadoPerezBoschQuesadaetal., author = {P{\´e}rez, Eduardo and Maldonado, David and Perez-Bosch Quesada, Emilio and Mahadevaiah, Mamathamba Kalishettyhalli and Jimenez-Molinos, Francisco and Wenger, Christian}, title = {Parameter Extraction Methods for Assessing Device-to-Device and Cycle-to-Cycle Variability of Memristive Devices at Wafer Scale}, series = {IEEE Transactions on Electron Devices}, volume = {70}, journal = {IEEE Transactions on Electron Devices}, number = {1}, issn = {0018-9383}, doi = {10.1109/TED.2022.3224886}, pages = {360 -- 365}, abstract = {The stochastic nature of the resistive switching (RS) process in memristive devices makes device-to-device (DTD) and cycle-to-cycle (CTC) variabilities relevant magnitudes to be quantified and modeled. To accomplish this aim, robust and reliable parameter extraction methods must be employed. In this work, four different extraction methods were used at the production level (over all the 108 devices integrated on 200-mm wafers manufactured in the IHP 130-nm CMOS technology) in order to obtain the corresponding collection of forming, reset, and set switching voltages. The statistical analysis of the experimental data (mean and standard deviation (SD) values) was plotted by using heat maps, which provide a good summary of the whole data at a glance and, in addition, an easy manner to detect inhomogeneities in the fabrication process.}, language = {en} } @misc{PerezBoschQuesadaMahadevaiahRizzietal., author = {Perez-Bosch Quesada, Emilio and Mahadevaiah, Mamathamba Kalishettyhalli and Rizzi, Tommaso and Wen, Jianan and Ulbricht, Markus and Krstic, Milos and Wenger, Christian and P{\´e}rez, Eduardo}, title = {Experimental Assessment of Multilevel RRAM-based Vector-Matrix Multiplication Operations for In-Memory Computing}, series = {IEEE Transactions on Electron Devices}, volume = {70}, journal = {IEEE Transactions on Electron Devices}, number = {4}, issn = {0018-9383}, doi = {10.1109/TED.2023.3244509}, pages = {2009 -- 2014}, abstract = {Resistive random access memory (RRAM)-based hardware accelerators are playing an important role in the implementation of in-memory computing (IMC) systems for artificial intelligence applications. The latter heavily rely on vector-matrix multiplication (VMM) operations that can be efficiently boosted by RRAM devices. However, the stochastic nature of the RRAM technology is still challenging real hardware implementations. To study the accuracy degradation of consecutive VMM operations, in this work we programed two RRAM subarrays composed of 8x8 one-transistor-one-resistor (1T1R) cells following two different distributions of conductive levels. We analyze their robustness against 1000 identical consecutive VMM operations and monitor the inherent devices' nonidealities along the test. We finally quantize the accuracy loss of the operations in the digital domain and consider the trade-offs between linearly distributing the resistive states of the RRAM cells and their robustness against nonidealities for future implementation of IMC hardware systems.}, language = {en} } @misc{MaldonadoCantudoPerezetal., author = {Maldonado, David and Cantudo, Antonio and P{\´e}rez, Eduardo and Romero-Zaliz, Rocio and Perez-Bosch Quesada, Emilio and Mahadevaiah, Mamathamba Kalishettyhalli and Jimenez-Molinos, Francisco and Wenger, Christian and Roldan, Juan Bautista}, title = {TiN/Ti/HfO2/TiN Memristive Devices for Neuromorphic Computing: From Synaptic Plasticity to Stochastic Resonance}, series = {Frontiers in Neuroscience}, volume = {17}, journal = {Frontiers in Neuroscience}, issn = {1662-4548}, doi = {10.3389/fnins.2023.1271956}, abstract = {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.}, language = {en} } @misc{PerezBoschQuesadaRizziGuptaetal., author = {Perez-Bosch Quesada, Emilio and Rizzi, Tommaso and Gupta, Aditya and Mahadevaiah, Mamathamba Kalishettyhalli and Schubert, Andreas and Pechmann, Stefan and Jia, Ruolan and Uhlmann, Max and Hagelauer, Amelie and Wenger, Christian and P{\´e}rez, Eduardo}, title = {Multi-Level Programming on Radiation-Hard 1T1R Memristive Devices for In-Memory Computing}, series = {14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023}, journal = {14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, isbn = {979-8-3503-0240-0}, doi = {10.1109/CDE58627.2023.10339525}, pages = {4}, abstract = {This work presents a quasi-static electrical characterization of 1-transistor-1-resistor memristive structures designed following hardness-by-design techniques integrated in the CMOS fabrication process to assure multi-level capabilities in harsh radiation environments. Modulating the gate voltage of the enclosed layout transistor connected in series with the memristive device, it was possible to achieve excellent switching capabilities from a single high resistance state to a total of eight different low resistance states (more than 3 bits). Thus, the fabricated devices are suitable for their integration in larger in-memory computing systems and in multi-level memory applications. Index Terms—radiation-hard, hardness-by-design, memristive devices, Enclosed Layout Transistor, in-memory computing}, language = {en} } @misc{PerezMaldonadoMahadevaiahetal., author = {P{\´e}rez, Eduardo and Maldonado, David and Mahadevaiah, Mamathamba Kalishettyhalli and Perez-Bosch Quesada, Emilio and Cantudo, Antonio and Jimenez-Molinos, Francisco and Wenger, Christian and Roldan, Juan Bautista}, title = {A comparison of resistive switching parameters for memristive devices with HfO2 monolayers and Al2O3/HfO2 bilayers at the wafer scale}, series = {14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023}, journal = {14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, isbn = {979-8-3503-0240-0}, doi = {10.1109/CDE58627.2023.10339417}, pages = {5}, abstract = {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}, language = {en} }