Nitrogen-Induced Enhancement of Synaptic Weight Reliability in Titanium Oxide-Based Resistive Artificial Synapse and...

With the significant technological developments in recent times, the neuromorphic system has been receiving considerable attention owing to its parallel arithmetic, low power consumption, and high scalability. However, the low reliability of artificial synapse devices disturbs calculations and causes inaccurate results in neuromorphic systems. In this paper, we propose a stable resistive artificial synapse (RAS) device with nitrogen-doped titanium oxide (TiO:N)-based resistive switching (RS) memory. The TiO:N-based RAS, compared to the TiO-based RAS, demonstrates more stable RS characteristics in current-voltage (I-V) and pulse measurements. In terms of resistance variability, the TiO:N-based RAS demonstrates five times lower resistance variability at 1.38%, compared to 6.68% with the TiO-based RAS. In addition, we verified the relation between the neuromorphic system and the resistance reliability of the synapse device for the first time. The pattern recognition simulation is performed using an artificial neural network (ANN) consisting of artificial synapse devices using the Modified National Institute of Standards and Technology dataset. In the simulation, the ANN with the TiO:N-based RAS exhibited significant pattern recognition accuracy of 64.41%, while the ANN with TiO-based RAS demonstrated only low recognition accuracy of 22.07%. According to the results of subsequent simulations, the pattern recognition accuracy exponentially decreases when the resistance variability exceeds 5%. Therefore, for implementing a stable neuromorphic system, the synapse device in the neuromorphic system has to maintain low resistance variability. The proposed nitrogen-doped synapse device is suitable for neuromorphic systems because reliable resistance variability can be obtained with only simple process steps.