We thoroughly investigate the performance of the Dynamic Memdiode Model (DMM) when used for simulating the synaptic weights in large RRAM-based cross-point arrays (CPA) intended for neuromorphic computing. The DMM is in line with Prof. Chua’s memristive devices theory, in which the hysteresis phenomenon in electroformed metal-insulator-metal structures is represented by means of two coupled equations: one equation for the current-voltage characteristic of the device based on an extension of the quantum point-contact (QPC) model for dielectric breakdown and a second equation for the memory state, responsible for keeping track of the previous history of the device. By considering ex-situ training of the CPA aimed at classifying the handwritten characters of the MNIST database, we evaluate the performance of a Write-Verify iterative scheme for setting the crosspoint conductances to their target values. The total programming time, the programming error, and the inference accuracy obtained with such writing scheme are investigated in depth. The role played by parasitic components such as the line resistance as well as some CPA’s particular features like the dynamical range of the memdiodes are discussed. The interrelationship between the frequency and amplitude values of the write pulses is explored in detail. In addition, the effect of the resistance shift for the case of a CPA programmed with no errors is studied for a variety of input signals, providing a design guideline for selecting the appropriate pulse’s amplitude and frequency.
Bibliographical noteKAUST Repository Item: Exported on 2021-10-28
Acknowledgements: This work was supported in part by the Argentine Ministerio de Ciencia, Tecnología e Innovación (MINCyT) under Contract PICTE 2018-0192, Contract PICT 2016/0579, and Contract PME 2015-0196; in part by the CONICET under Project PIP-11220130100077CO; and in part by the UTN-FRBA under Project PID-UTN EIUTIBA4395TC3, Project PID-UTN CCUTIBA4764TC, Project PID-UTN MATUNBA4936, Project PID-UTN CCUTNBA5182, and Project PID-UTN CCUTNBA6615. The work of JS and EM was supported by the TEC2017-84321-C4-4-R project (Spanish Ministerio de Ciencia e Innovación). This work is supported by the EMPIR 20FUN06 MEMQuD project with funds from the EMPIR program co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation program. S.P. is currently also with the Physical Sciences and Engineering Division of the King Abdullah University of Science and Technology.