Integrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices

Abstract

Artificial neural networks (NNs) integrated with emerging devices are one promising approach to overcoming the limitations of artificial intelligence hardware based on existing CMOS technologies. Two-terminal memristive devices consisting of metal-oxide WOx/MgO were fabricated and investigated in order to add synaptic memory functions to NN hardware. The device showed analog conductance changes similar to a biological synapse’s spike-timing dependent plasticity as well as its binarized characteristics, which depend on waveforms of voltage spikes. To exploit such properties of the memristive devices for practical hardware, NN simulations were executed taking into account analog and binary synapses, resulting in good accuracy. For rapid NN prototyping with resistive synapses, we developed an analog-to-digital mixed circuit with variable resistors, which was fabricated using a standard CMOS process. Thirty-two analog neurons, where 1 neuron comprises 32 synapses, performed 1k synaptic operations at approximately 10 mW and generate neuronal firing at approximately less than 2 μs.