02.12.2021NANO COLLOQUIA S3 Avinash Vikatakavi
Date and Time: December 2, 2021 - 15.00 ONLINE
25.11.2021Producing electricity from heat losses: engineered in Pisa the first device capable of achieving it in a controlled manner
It is now possible to create a new generation of “smart” thermoelectric systems to generate clea...
23.11.2021il progetto RIMMEL @ MECSPE - Bologna 2021
Si svolgerà martedì 23 novembre, dalle 16.45 alle 17.45 (Sala Concerto c/o Centro Servizi – Bolo...
19.11.2021Graphene as a solid lubricant becomes super-slippery
Cnr Nano researchers in collaboration with Sussex University and Rice University studied the frictio...
17.11.2021International Workshop on Advanced Materials-to-Device Solutions for Synaptic Electronics
CNR Nano and ICN2 organized the
03.11.2021The RIMMEL Project @ l'Europa è qui 2021 – VOTE THE VIDEO ONLINE
The RIMMEL project enters the “Europe is here ...
11.10.2021Quantum computers become an experimental physics laboratory
A quantum computer is a machine designed to do calculations. Now a group of physicists from CnrNano,...
05.10.20212021 Nobel Prize for the discoveries on TRPV1 and PIEZO receptors
The seminal discoveries by this year’s Nobel Laureates have explained how heat, cold and touch can...
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strong>Date and Time: Monday December 16, 2019 - 12:00 Venue: S3 Seminar Room, Third Floor, Physics Building, FIM Department Speaker: Sabina Spiga, CNR-IMM Agrate Brianza (IT) Title: Memristive devices for brain inspired computing Abstract: Memristive devices have been receiving an increasing interest for a wide range of applications, such as storage class memory, non-volatile logic switch, in-memory computing and neuromorphic computing. In particular, in bio-inspired systems, memristive devices can act as dispersed memory elements mimicking synapses in nervous systems, or as stochastic and non-linear elements in neuronal units. Among the proposed technologies, oxide-based memristors (also named resistance switching memory, RRAM) are based on redox reactions and electrochemical phenomena in oxides and are very promising because of low power consumption, fast switching times, scalability down to nm scale and CMOS compatibility. In our work, we focus on the switching dynamics of HfO2 RRAMs and on their implementation as electronic synapses in spiking neural networks (SNN). Our results show that the device conductance can be tuned in an analog fashion by using train of identical pulses, to emulate the biological potentiation (conductance increase) and depression (conductance decrease) processes, over several cycles. The update of the conductance values can be achieved by a spike timing and rate dependent plasticity mechanism, which is demonstrated also at hardware level, and can be exploited as learning rule in a SNN. Finally, the experimental data sets are used to simulate a fully connected winner-take-all spiking neural network with leaky integrate and fire neurons equipped with circuitry emulating the temporal dynamics of biological synapses. Host: Arrigo Calzolari e-mail: arrigo.calzolari@nano.cnr.it