Memristive systems represent a large class of emerging nanoscale devices which exploit various physical mechanisms to achieve a controlled state-dependent and persistent conductance variation upon electrical stimuli. Industry compatible memristive devices are today of large interest as new building blocks for brain-inspired computing architectures where memory and computational units are co-localized. In this framework, resistance switching memories (RRAM) based on redox reactions and electrochemical phenomena in oxides  have been proposed as synaptic elements in spiking neural networks for hybrid CMOS/RRAM hardware in view of big data applications as well as for real time and low power computation systems .
In this talk, we will present our work toward the development of analog HfO2-based electronic synapses. We investigate the dynamics of conductance evolution in a wide space of pulse time widths and voltages. Our results show that in memristive devices based on filamentary switching it is possible to tune the device conductance in an analog way upon application of identical electrical pulses [2-3], by using pulses with duration close to the intrinsic switching time. The use of weak programming conditions leads to an enhanced variability and a reduced memory window with respect the one for digital switching. Salient characteristics of the analog conductance control are the state dependent weight update as a function of the number of incoming pulses and the slow approach to conductance-end saturation value, thus realizing a softly bounded conductance update. This behavior has been recognized in literature  as a feature able to improve memory capacity. In order to analyze consistently a large set of data, we use a behavioral model implementing a multiplicative update rule with a weight-dependent weight update which describes the observed slow approach to boundary conductance values. We also discuss various non-idealities such as asymmetric processes for conductance increase (potentiation) and decrease (depression), pulse-to-pulse and cycle-to-cycle variability, to depict a complete picture of analog weighting in HfO2-based electronic synapses. 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. The network is benchmarked against the classification of MNIST digits by implementing a semi-supervised spike-based learning protocol based on a generalization of spike time and rate dependent plasticity . The effect on the recognition rate of the number of available analog levels is discussed.
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