Date: Monday December 16, 2019
Time: 12.00
Venue: S3 Seminar Room, Third Floor, Physics Building, FIM Department

Speaker: Sabina Spiga
Affiliation: 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 arrigo.calzolari@nano.cnr.it

Date: Thursday March 28, 2019
Time: 14:30 CET
Venue: CNR Nano Modena, S3 Seminar room – FIM Department, III floor

Speaker: Prof. Marco Bernasconi
Affiliation: Department of Materials Science, University of Milano-Bicocca, Milano (IT)

Title: Atomistic Simulations of Phase Change Materials for Data Storage

Abstract: Chalcogenide compounds such as GeTe and GeSbTe alloys are attracting an increasing interest for their application in Phase Change Memories (PCM) [1]. This novel type of electronic non-volatile memory rests on a fast and reversible transformation between the crystalline and amorphous phase of a chalcogenide film induced by Joule heating. The two phases corresponding to the two states of the memory can be discriminated because of their large difference in electronic conductivity.

PCMs are emerging as a leading contender for the realization of the so-called storage class memories that are sought to fill the performance gap between volatile DRAM and non-volatile Flash memories. Storage class memories are believed to usher in seminal changes in the memory and storage hierarchy for all computing platforms. Application of PCM for neuromorphic computing is also an active field of research.

In the last decade, atomistic simulations based on density functional theory (DFT) have provided useful insights on the properties of chalcogenide alloys of interest for PCMs [2]. Still, large simulation cells and long simulation times beyond the reach of DFT simulations are needed to address several key issues of relevance for PCM operation. To overcome these limitations, we have developed an interatomic potential for the prototypical phase change compound GeTe by fitting a large DFT database with a neural network (NN) scheme. Large scale (104 atoms) NN simulations allowed us to get insights on microscopic origin of the high crystallization speed of these materials [3].

In this talk, I will review the results of NN simulations on the kinetics of homogeneous and heterogeneous crystallization of GeTe and on the structural relaxations leading to the aging of the amorphous phase which is a particularly critical issue for PCM operation [4].

[1] M. Wuttig and N. Yamada, Nat. Mater. 6, 824 (2007); W. Zhang et al., Nat. Review Mater. (2019), doi.org/10.1038/s41578-018-0076-x.
[2] S. Caravati, M. Bernasconi, M. Krack, T. Kuehne, M. Parrinello, Appl. Phys. Lett. 91, 171906 (2007); Lencer et al., Adv. Mat. 23, 2030 (2011).
[3] G. C. Sosso, G. Miceli, S. Caravati, J. Behler, M. Bernasconi, Phys. Rev. B B 85, 174103 (2012).
[4] G. C. Sosso, et al. , J. Phys. Chem. Lett. 4, 4241 (2013); S. Gabardi, S. Caravati, G.C. Sosso, J. Behler, and M. Bernasconi, Phys. Rev. B 92, 054201 (2015); S. Gabardi et al., J. Phys. Chem C 121, 23827 (2017).

Host: Arrigo Calzolari arrigo.calzolari@nano.cnr.it