A sub-1-volt analog metal oxide memristive-based synaptic device for   energy-efficient spike-based computing systems
  Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient1-3. In such systems, memristors represent the native electronic analogues of the biological synapses. However, the characteristics of the existing memristors do not fully support the key requirements of synaptic connections: high density, adjustable weight, and low energy operation. Here we show a bilayer memristor that is forming-free, low-voltage (~|0.8V|), energy-efficient (full On/Off switching at ~2pJ), and reliable. Furthermore, pulse measurements reveal the analog nature of the memristive device, that is it can be directly programmed to intermediate resistance states. Leveraging this finding, we demonstrate spike-timing-dependent plasticity (STDP), a spike-based Hebbian learning rule4. In those experiments, the memristor exhibits a marked change in the normalized synaptic strength (>30 times) when the pre- and post-synaptic neural spikes overlap. This demonstration is an important step towards the physical construction of high density and high connectivity neural networks. 
Cheng-Chih Hsieh, Anupam Roy, Yao-Feng Chang, Davood Shahrjerdi, and   Sanjay K. Banerjee
cond-mat.mes-hall