Qualitative SPICE modeling accounting for volatile dynamics of TiO2 memristors

Accurate modeling of memristive devices is a critical condition that will allow the realization of large-scale memristor based circuits. The current methodology regarding modeling focuses on obtaining realistic pinched hysteresis curves, which are memristor signatures, but these do not hold useful information regarding device performance. We divert from this practice and propose a SPICE memristor model constructed based on qualitative verified assumptions of real memristive device operation. Our model introduces volatile effects that render a rate-dependent operation, and also accounts for both bipolar and unipolar switching. We demonstrate its plausibility via a wealth of simulation cases, which are qualitatively similar to several memristor dynamics reported in literature. Finally our model is benchmarked against measured results acquired by solid-state TiO2 memristors.

[1]  C. Toumazou,et al.  A Versatile Memristor Model With Nonlinear Dopant Kinetics , 2011, IEEE Transactions on Electron Devices.

[2]  C. Toumazou,et al.  Memristive devices as parameter setting elements in programmable gain amplifiers , 2012 .

[3]  L. Chua.  Memristor-The missing circuit element , 1971 .

[4]  Jiantao Zhou,et al.  Stochastic Memristive Devices for Computing and Neuromorphic Applications , 2013, Nanoscale.

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

[6]  R. Williams,et al.  Exponential ionic drift: fast switching and low volatility of thin-film memristors , 2009 .

[7]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[8]  Shimeng Yu,et al.  A SPICE Compact Model of Metal Oxide Resistive Switching Memory With Variations , 2012, IEEE Electron Device Letters.

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

[10]  Guoqiang Li,et al.  Unipolar memristors enable “stateful” logic operations via material implication , 2011 .

[11]  Y. Liu,et al.  Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor , 2012 .

[12]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[13]  R. Williams,et al.  How We Found The Missing Memristor , 2008, IEEE Spectrum.

[14]  Dalibor Biolek,et al.  SPICE Model of Memristor with Nonlinear Dopant Drift , 2009 .

[15]  D. Strukov,et al.  Thermophoresis/diffusion as a plausible mechanism for unipolar resistive switching in metal–oxide–metal memristors , 2012, Applied Physics A.

[16]  Tamás Roska,et al.  A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  M. Pickett,et al.  A scalable neuristor built with Mott memristors. , 2013, Nature materials.

[18]  Z. Wei,et al.  Demonstration of high-density ReRAM ensuring 10-year retention at 85°C based on a newly developed reliability model , 2011, 2011 International Electron Devices Meeting.

[19]  Sallee Klein,et al.  Memristive Adaptive Filters , 2010 .

[20]  O. Richard,et al.  10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation , 2011, 2011 International Electron Devices Meeting.

[21]  Fernando Corinto,et al.  Memristive diode bridge with LCR filter , 2012 .

[22]  Pinaki Mazumder,et al.  CMOS and Memristor-Based Neural Network Design for Position Detection , 2012, Proceedings of the IEEE.

[23]  Jae Hyuck Jang,et al.  Atomic structure of conducting nanofilaments in TiO2 resistive switching memory. , 2010, Nature nanotechnology.

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