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Memristive systems represent today a disruptive technology for the semiconductor industry towards several applications such as data storage (non-volatile memories), non-volatile logic, analog circuits, biomimetic devices, novel computing paradigms such as neuromorphic computation.

Among the various proposed approaches, we are working on those memristive technologies (RRAM) exploiting redox reactions and electrochemical phenomena in metal oxides to build devices that exhibit a non-volatile and reversible change of their resistance or conductance according to external electrical stimuli. The reversible conductance change in oxide RRAM is are  based on the formation and dissolution of nanoscale filamentary paths in a ionic conductive oxide, and oxide RRAM are currently very promising in term of low power consumption, fast switching times, scalability down to nm scale or atomic level, and CMOS compatibility.

Based on an expertise build in the last ten years in the field of oxide-based RRAM memory devices, we are currently also exploiting these devices for new application towards neuromorphic computing and neural networks. Indeed, the proposed RRAM nanotechnology has the potential of integrating memory and computation locally, reducing dramatically the power-cost in data communication, and it is possible to establish unconventional computation formalisms that find applications in adaptive systems.

The current running activities as are directed to:

RRAM - resistance switching memories

- materials engineering and understanding of switching mechanism at the nanoscale in oxide based RRAM

- fabrication of nanoscale devices by block-copolymer lithography

Memristive devices for neuromorphic computing

- study of the device switching dynamics, compact and physical model of the switching

- development of devices to emulate the synaptic behavior in a neuromorphic networks

- simulation of a Spiking Neuromorphic Network in MATLAB implementing unsupervised STDP protocols based on experimental data

These research activities have a TRL (Technology Readiness Level) between 2 and 4.  The main perspectives are : (i)  ultra-low power data processing coupled to sensors in autonomous systems (Internet of Things) (ii)  energy efficient large data processing in servers and networks.

Selected Recent Publications:

-  S. Brivio, J. Frascaroli, E. Covi and S. Spiga , "Stimulated Ionic Telegraph Noise in Filamentary Memristive Devices", Scientific Reports 9, 6310 (2019)

- S. Brivio, D. Conti, M. V. Nair, J. Frascaroli, E. Covi, C. Ricciardi, G. Indiveri and S. Spiga, "Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics", Nanotechnology 30, 015102 (2019)

- J. Frascaroli, S. Brivio, E. Covi, and  S. Spiga, "Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing", Scientific Reports Vol. 8, 7178 (2018)

E. Covi, R. George, J. Frascaroli, S. Brivio, C. Mayr, H. Mostafa, G. Indiveri and S. Spiga, "Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons", Journal of Physics D: Applied Physics Vol. 51 Page 344003 (2018)

 - S. Brivio, J. Frascaroli, and S. Spiga, "Role of Al doping in the filament disruption in HfO2 resistance switches", Nanotechnology Vol. 28 Page 395202 (2017)

- S. Brivio and S. Spiga, "Stochastic circuit breaker network model for bipolar resistance switching memories", Journal of Computation Electronics Vol. 16 Page 1154 (2017)

- H.-Yu Chen, S. Brivio, C.-C. Chang, J. Frascaroli, T.-H. Hou, B. Hudec, M. Liu, H. Lv, G. Molas, J. Sohn, S. Spiga, V. M. Teja, E. Vianello, H.-S. P.p Wong, Resistive random access memory (RRAM) technology: From material, device, selector, 3D integration to bottom-up fabrication, J Electroceramics, Volume 39, Issue 1–4, pp 21–38 (2017)

- E. Covi, S. Brivio, A. Serb, T. Prodromakis, M. Fanciulli, and S. Spiga, “Analog memristive synapse in spiking networks implementing unsupervised learning’ under review”, Front. Neurosci., vol. 10, p. 482 ( 2016)

- S Brivio, E Covi, A Serb, T Prodromakis, M Fanciulli, and S Spiga, Experimental study of gradual/abrupt dynamics of HfO2-based memristive devices, Applied Physics Letters 109, 133504 (2016).

- Sabina Spiga, Takeshi Yanagida, Tomoji Kawai, “Bottom‐Up Approaches for Resistive Switching Memories”, Chapter 23 (pp 661-694) in Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device Applications, ed. Wiley‐VCH Verlag GmbH & Co. KGaA (2016); DOI: 10.1002/9783527680870.ch23

- J. Frascaroli, S.Brivio, F. F. Lupi , G. Seguini , L. Boarino , M. Perego and S. Spiga , "Resistive Switching in High-Density Nanodevices Fabricated by Block Copolymer Self-Assembly" , ACS Nano 9, 2518–2529 (2015) 

- S. Brivio, J. Frascaroli, and S. Spiga, "Role of metal-oxide interfaces in the multiple resistance switching regimes of Pt/HfO2/TiN devices", Appl. Phys. Lett. 107, 023504 (2015)  

- E. Covi, S. Brivio, M. Fanciulli, and S. Spiga, "Synaptic potentiation and depression in Al:HfO2-based memristor", Microelectronic Engineering 147, 41-44 (2015)

- J. Frascaroli, F. G. Volpe, S. Brivio, and S. Spiga, "Effect of Al doping on the retention behavior of HfO2 resistive switching memories", Microelectronic Engineering 147, 104-107 (2015)

- S. Brivio, G. Tallarida, E. Cianci and S. Spiga, “Formation and disruption of conductive filaments in a HfO2/TiN structure", Nanotechnology  25, 385705  (2014)

- S. Brivio, D. Perego, G. Tallarida, M. Bestetti, S. Franz and S. Spiga, “Bipolar resistive switching of Au/NiOx/Ni/Au heterostructure nanowires”, Appl. Phys. Lett. 103, 153506 (2013)

- S. Brivio, G. Tallarida, D. Perego, D. Deleruyelle, S. Franz, Ch. Muller, and S. Spiga, ”Low power resistive switching Au/NiO/Au nanowire arrays”, Appl. Phys. Lett. 101, 223510 (2012)

Running European Projects:

NeuRAM3-  NEUral computing aRchitectures in Advanced Monolithic 3D-VLSI nano-technologies (Horizon 2020, grant. 687299, 2016-2018); web site: http://www.neuram3.eu/

MemoCiS - Memristors-Devices, Models, Circuits, Systems and Applications (COST Action IC1401, 2014-2018 )

Relevant Past Projects:

RAMP – Real neurons-nanoelectronics Architecture with Memristive Plasticity (FP7-grant , 2013-2017); web site: http://www.rampproject.eu/

MORE - Advanced Metal-Oxide heterostructures for nanoscale RERAM (Funded by Cariplo Fondation; 2010-2013)

EMMA - Emerging Materials for Mass-storage Architectures (EU-ICT-FP6; 2006-2009)

Contact Person: Sabina Spiga