Ultra-low-energy three-dimensional oxide-based electronic synapses for implementation of robust high-accuracy neuromorphic computation systems

Neuromorphic computing is an attractive computation paradigm that complements the von Neumann architecture. The salient features of neuromorphic computing are massive parallelism, adaptivity to the complex input information, and tolerance to errors. As one of the most crucial components in a neuromorphic system, the electronic synapse requires high device integration density and low-energy consumption. Oxide-based resistive switching devices have been shown to be a promising candidate to realize the functions of the synapse. However, the intrinsic variation increases significantly with the reduced spike energy due to the reduced number of oxygen vacancies in the conductive filament region. The large resistance variation may degrade the accuracy of neuromorphic computation. In this work, we develop an oxide-based electronic synapse to suppress the degradation caused by the intrinsic resistance variation. The synapse utilizes a three-dimensional vertical structure including several parallel oxide-based resistive switching devices on the same nanopillar. The fabricated three-dimensional electronic synapse exhibits the potential for low fabrication cost, high integration density, and excellent performances, such as low training energy per spike, gradual resistance transition under identical pulse training scheme, and good repeatability. A pattern recognition computation is simulated based on a well-known neuromorphic visual system to quantify the feasibility of the three-dimensional vertical structured synapse for the application of neuromorphic computation systems. The simulation results show significantly improved recognition accuracy from 65 to 90% after introducing the three-dimensional synapses.