Unconventional computing with diffusive memristors

Diffusive memristors with Ag active metal species are volatile threshold switches featuring spontaneous rupture of conduction channels at small electrical bias. The unique temporal dynamics of the conductance evolution originates from the underlying electrochemical and diffusive dynamics of the active metals in dielectrics, which can be explored for a variety of novel applications in unconventional computing. The superior I-V nonlinearity enables large crossbar arrays for high density non-volatile memories. The relaxation dynamics and the delay dynamics of the conductance evolution lead to faithful synaptic emulators and single-device threshold logic neurons, respectively. Unsupervised learning has been demonstrated with a fully memristive neural network consisting of these artificial synapses and neurons for the first time. In addition, the intrinsic stochasticity of the delay mechanism has been used to realize a true random number generators for security solutions.

[1]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[2]  Qing Luo,et al.  Cu BEOL compatible selector with high selectivity (>107), extremely low off-current (∼pA) and high endurance (>1010) , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[3]  H. Barnaby,et al.  Volatile and Non-Volatile Switching in Cu-SiO2 Programmable Metallization Cells , 2016, IEEE Electron Device Letters.

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

[5]  Kate J. Norris,et al.  Anatomy of Ag/Hafnia‐Based Selectors with 1010 Nonlinearity , 2017, Advanced materials.

[6]  J. Yang,et al.  Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.

[7]  Sungho Kim,et al.  Intrinsic threshold switching responses in AsTeSi thin film , 2016 .

[8]  Hyunsang Hwang,et al.  Field-induced nucleation in threshold switching characteristics of electrochemical metallization devices , 2017 .

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

[10]  Manuel Le Gallo,et al.  Stochastic phase-change neurons. , 2016, Nature nanotechnology.

[11]  Qing Wu,et al.  A novel true random number generator based on a stochastic diffusive memristor , 2017, Nature Communications.

[12]  H. Hwang,et al.  Excellent Selector Characteristics of Nanoscale $ \hbox{VO}_{2}$ for High-Density Bipolar ReRAM Applications , 2011, IEEE Electron Device Letters.

[13]  D. Ielmini,et al.  SiOx-based resistive switching memory (RRAM) for crossbar storage/select elements with high on/off ratio , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[14]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[15]  S. Datta,et al.  Ag/HfO2 based threshold switch with extreme non-linearity for unipolar cross-point memory and steep-slope phase-FETs , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).