TY - JOUR
T1 - A sub-1-volt analog metal oxide memristive-based synaptic device with large conductance change for energy-efficient spike-based computing systems
AU - Hsieh, Cheng Chih
AU - Roy, Anupam
AU - Chang, Yao Feng
AU - Shahrjerdi, Davood
AU - Banerjee, Sanjay K.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient. In such systems, memristors represent the native electronic analogues of the biological synapses. In this work, we show cerium oxide based bilayer memristors that are forming-free, low-voltage (∼|0.8 V|), energy-efficient (full on/off switching at ∼8 pJ with 20 ns pulses, intermediate states switching at ∼fJ), and reliable. Furthermore, pulse measurements reveal the analog nature of the memristive device; that is, it can directly be programmed to intermediate resistance states. Leveraging this finding, we demonstrate spike-timing-dependent plasticity, a spike-based Hebbian learning rule. 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.
AB - Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient. In such systems, memristors represent the native electronic analogues of the biological synapses. In this work, we show cerium oxide based bilayer memristors that are forming-free, low-voltage (∼|0.8 V|), energy-efficient (full on/off switching at ∼8 pJ with 20 ns pulses, intermediate states switching at ∼fJ), and reliable. Furthermore, pulse measurements reveal the analog nature of the memristive device; that is, it can directly be programmed to intermediate resistance states. Leveraging this finding, we demonstrate spike-timing-dependent plasticity, a spike-based Hebbian learning rule. 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.
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U2 - 10.1063/1.4971188
DO - 10.1063/1.4971188
M3 - Article
AN - SCOPUS:85001889503
VL - 109
JO - Applied Physics Letters
JF - Applied Physics Letters
SN - 0003-6951
IS - 22
M1 - 223501
ER -