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Carlo Ricciardi   Dr.  Senior Scientist or Principal Investigator 
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Carlo Ricciardi published an article in January 2019.
Top co-authors See all
Paola Rivolo

198 shared publications

Dipartimento di Scienza Applicata e Tecnologia (DISAT), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy

Francesca Frascella

187 shared publications

Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy

Micaela Castellino

66 shared publications

Center for Sustainable Future Technologies @ POLITO, Istituto Italiano di Tecnologia, Via Livorno 60, Turin 10144, Italy

Marco Arlorio

58 shared publications

Università del Piemonte Orientale, Dipartimento di Scienze del Farmaco, Largo Donegani 2, 28100 Novara (Italy)

Mauro Tortello

56 shared publications

Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino-sede di Alessandria, viale Teresa Michel 5, 15121 Alessandria, Italy

Publication Record
Distribution of Articles published per year 
(2001 - 2018)
Total number of journals
published in
Publications See all
Article 0 Reads 0 Citations Electrochemical metallization ReRAMs (ECM) - Experiments and modelling: general discussion Elia Ambrosi, Philip Bartlett, Alexandra I. Berg, Stefano Br... Published: 01 January 2019
Faraday Discussions, doi: 10.1039/c8fd90059k
DOI See at publisher website
Article 0 Reads 0 Citations Synaptic and neuromorphic functions: general discussion Alexandra I. Berg, Stefano Brivio, Simon Brown, Geoffrey Bur... Published: 01 January 2019
Faraday Discussions, doi: 10.1039/c8fd90065e
DOI See at publisher website
Article 0 Reads 0 Citations Resistive switching in sub-micrometric ZnO polycrystalline films Daniele Conti, Marco Laurenti, Samuele Porro, Cecilia Giovin... Published: 13 December 2018
Nanotechnology, doi: 10.1088/1361-6528/aaf261
DOI See at publisher website ABS Show/hide abstract
Resistive switching devices are considered as the most promising alternative to conventional random access memories. They interestingly offer effective properties in terms of device scalability, low power-consumption, high read/write operation time, endurance and state retention. Moreover, neuromorphic circuits and synapse-like devices are envisaged with resistive switching modeled as memristors, opening the route toward beyond-Von Neumann computing architectures and intelligent systems. This work investigates how the resistive switching properties of zinc oxide thin films are related to both sputtering deposition process and device configuration, i.e. valence change memory (VCM) and electrochemical metallization memory (ECM). Different devices, with an oxide thickness ranging from 50 to 250 nm, are fabricated and deeply characterized. The electrical characterization evidences that, differently from typical nanoscale amorphous oxides employed for resistive RAMs (HfOx, WOx, etc..), sub-micrometric thicknesses of polycristalline ZnO layers with ECM configuration are needed to achieve the most reliable devices. The obtained results are deeply discussed, correlating the resistive switching mechanism to material nanostructure.
Article 0 Reads 0 Citations Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities Gianluca Milano, Michael Luebben, Zheng Ma, Rafal Dunin-Bork... Published: 04 December 2018
Nature Communications, doi: 10.1038/s41467-018-07330-7
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The ability for artificially reproducing human brain type signals’ processing is one of the main challenges in modern information technology, being one of the milestones for developing global communicating networks and artificial intelligence. Electronic devices termed memristors have been proposed as effective artificial synapses able to emulate the plasticity of biological counterparts. Here we report for the first time a single crystalline nanowire based model system capable of combining all memristive functions – non-volatile bipolar memory, multilevel switching, selector and synaptic operations imitating Ca2+ dynamics of biological synapses. Besides underlying common electrochemical fundamentals of biological and artificial redox-based synapses, a detailed analysis of the memristive mechanism revealed the importance of surfaces and interfaces in crystalline materials. Our work demonstrates the realization of self-assembled, self-limited devices feasible for implementation via bottom up approach, as an attractive solution for the ultimate system miniaturization needed for the hardware realization of brain-inspired systems.
Article 3 Reads 0 Citations Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics Stefano Brivio, Daniele Conti, Manu V Nair, Jacopo Frascarol... Published: 31 October 2018
Nanotechnology, doi: 10.1088/1361-6528/aae81c
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Spiking neural networks employing memristive synapses are capable of life-long online learning. Because of their ability to process and classify large amounts of data in real-time using compact and low-power electronic systems, they promise a substantial technology breakthrough. However, the critical issue that memristor-based SNNs have to face is the fundamental limitation in their memory capacity due to finite resolution of the synaptic elements, which leads to the replacement of old memories with new ones and to a finite memory lifetime. In this study we demonstrate that the nonlinear conductance dynamics of memristive devices can be exploited to improve the memory lifetime of a network. The network is simulated on the basis of a spiking neuron model of mixed-signal digital-analogue sub-threshold neuromorphic CMOS circuits, and on memristive synapse models derived from the experimental non-linear conductance dynamics of resistive memory devices when stimulated by trains of identical pulses. The network learning circuits implement a spike-based plasticity rule compatible with both spike-timing and rate-based learning rules. In order to get an insight on the memory lifetime of the network, we analyse the learning dynamics in the context of a classical benchmark of neural network learning, that is hand-written digit classification. In the proposed architecture, the memory lifetime and the performance of the network are improved for memristive synapses with nonlinear dynamics with respect to linear synapses with similar resolution. These results demonstrate the importance of following holistic approaches that combine the study of theoretical learning models with the development of neuromorphic CMOS spiking neural networks with memristive devices used to implement life-long on-chip learning.
Article 0 Reads 0 Citations A multi-level memristor based on atomic layer deposition of iron oxide Samuele Porro, Katarzyna Bejtka, Alladin Jasmin, Marco Fonta... Published: 05 October 2018
Nanotechnology, doi: 10.1088/1361-6528/aae2ff
DOI See at publisher website