Multiscaled Simulation Methodology for Neuro-Inspired Circuits Demonstrated with an Organic Memristor

Organic memristors are promising molecular electronic devices for neuro-inspired on-chip learning applications. In this paper, we present a numerically efficient compact model suitable for <inline-formula><tex-math notation="LaTeX">$Fe(bpy)_3^{2+}$</tex-math><alternatives><inline-graphic xlink:href="bennett-ieq1-2773523.gif"/></alternatives></inline-formula> organic memristors operating according to an intramolecular charge transfer switching mechanism. This compact model, being physics-based and relying on electrical characterizations and parametric extractions performed on test structures, is especially efficient in pulsed mode and describes the conductance variations for both SET and RESET regimes. Using this model, a dynamic multiscale simulation approach has been set-up to extend the model from individual devices to larger model systems that learn progressively through time. To verify the soundness and highlight emergent properties of the organic memristors, instances of the compact model have been simulated within a simple neuromorphic design that co-integrates with CMOS neurons. In addition, a larger supervised learning system using the new compact model is demonstrated. These successful tests suggest our model might be of interest to neuromorphic designers.

[1]  Zhaohao Wang,et al.  On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron , 2014, 2014 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).

[2]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[3]  Ali Khiat,et al.  Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses , 2016, Nature Communications.

[4]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[5]  Ligang Gao,et al.  High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm , 2011, Nanotechnology.

[6]  Jacques-Olivier Klein,et al.  Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses , 2016, Scientific Reports.

[7]  Chris Yakopcic,et al.  Multiple memristor read and write circuit for neuromorphic applications , 2011, The 2011 International Joint Conference on Neural Networks.

[8]  F. Corinto,et al.  Memristor Model Comparison , 2013, IEEE Circuits and Systems Magazine.

[9]  Shimeng Yu,et al.  An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation , 2011, IEEE Transactions on Electron Devices.

[10]  Jacques-Olivier Klein,et al.  Robust learning approach for neuro-inspired nanoscale crossbar architecture , 2014, ACM J. Emerg. Technol. Comput. Syst..

[11]  Yu Chen,et al.  Polymer memristor for information storage and neuromorphic applications , 2014 .

[12]  Damien Querlioz,et al.  Simulation of a memristor-based spiking neural network immune to device variations , 2011, The 2011 International Joint Conference on Neural Networks.

[13]  R Waser,et al.  Compact modeling of CRS devices based on ECM cells for memory, logic and neuromorphic applications. , 2013, Nanotechnology.

[14]  J. Yang,et al.  State Dynamics and Modeling of Tantalum Oxide Memristors , 2013, IEEE Transactions on Electron Devices.

[15]  Fabien Alibart,et al.  Plasticity in memristive devices for spiking neural networks , 2015, Front. Neurosci..

[16]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[17]  R. Waser,et al.  Nanoionics-based resistive switching memories Many metal – insulator – , 2007 .

[18]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

[19]  Théo Cabaret,et al.  Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés , 2014 .

[20]  J. Yang,et al.  A compact modeling of TiO2-TiO2–x memristor , 2013 .

[21]  V. Derycke,et al.  Electro-grafted organic memristors: Properties and prospects for artificial neural networks based on STDP , 2014, 14th IEEE International Conference on Nanotechnology.

[22]  Jacques-Olivier Klein,et al.  Supervised learning with organic memristor devices and prospects for neural crossbar arrays , 2015, Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH´15).