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The implementation of highly anticipated hardware neural networks (HNNs) hinges largely on the successful development of a low-power, high-density, and reliable analog electronic synaptic array. In this study, we demonstrate a two-layer Ta/TaO x /TiO2/Ti cross-point synaptic array that emulates the high-density three-dimensional network architecture of human brains. Excellent uniformity and reproducibility among intralayer and interlayer cells were realized. Moreover, at least 50 analog synaptic weight states could be precisely controlled with minimal drifting during a cycling endurance test of 5000 training pulses at an operating voltage of 3 V. We also propose a new state-independent bipolar-pulse-training scheme to improve the linearity of weight updates. The improved linearity considerably enhances the fault tolerance of HNNs, thus improving the training accuracy.

A two-terminal analog synaptic device that precisely emulates biological synaptic features is expected to be a critical component for future hardware-based neuromorphic computing. Typical synaptic devices based on filamentary resistive switching face severe limitations on the implementation of concurrent inhibitory and excitatory synapses with low conductance and state fluctuation. For overcoming these limitations, we propose a Ta/TaOx/TiO2/Ti device with superior analog synaptic features. A physical simulation based on the homogeneous (nonfilamentary) barrier modulation induced by oxygen ion migration accurately reproduces various DC and AC evolutions of synaptic states, including the spike-timing-dependent plasticity and paired-pulse facilitation. Furthermore, a physics-based compact model for facilitating circuit-level design is proposed on the basis of the general definition of memristor devices. This comprehensive experimental and theoretical study of the promising electronic synapse can facilitate realizing large-scale neuromorphic systems.

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