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Recent advances in neuroscience together with nanoscale electronic devicetechnology have resulted in huge interests in realizing brain-like computinghardwares using emerging nanoscale memory devices as synaptic elements.Although there has been experimental work that demonstrated the operation ofnanoscale synaptic element at the single device level, network level studieshave been limited to simulations. In this work, we demonstrate, usingexperiments, array level associative learning using phase change synapticdevices connected in a grid like configuration similar to the organization ofthe biological brain. Implementing Hebbian learning with phase change memorycells, the synaptic grid was able to store presented patterns and recallmissing patterns in an associative brain-like fashion. We found that the systemis robust to device variations, and large variations in cell resistance statescan be accommodated by increasing the number of training epochs. We illustratedthe tradeoff between variation tolerance of the network and the overall energyconsumption, and found that energy consumption is decreased significantly forlower variation tolerance.

Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array