No abstract

No abstract

The origins of the nonlinear and asymmetric synaptic characteristics of TiO x -based synapse devices were investigated. Based on the origins, a microstructural electrode was utilized to improve the synaptic characteristics. Under an identical pulse bias, a TiO x -based synapse device exhibited saturated conductance changes, which led to nonlinear and asymmetric synaptic characteristics. The formation of an interfacial layer between the electrode and TiO x layer, which can limit consecutive oxygen migration and chemical reactions, was considered as the main origin of the conductance saturation behavior. To achieve consecutive oxygen migration and chemical reactions, structural engineering was utilized. The resultant microstructural electrode noticeably improved the synaptic characteristics, including the unsaturated, linear, and symmetric conductance changes. These synaptic characteristics resulted in the recognition accuracy significantly increasing from 38% to 90% in a neural network-based pattern recognition simulation.

An oxygen-based ionic synaptic transistor (O-IST) is a promising synaptic element for neuromorphic computing. In this study, we demonstrated that the density of the electrolyte plays a key role in achieving excellent synaptic characteristics in an O-IST. In a Pr0.7Ca0.3MnO3-based O-IST, we precisely controlled the density of the HfOx electrolyte and found that a low-density electrolyte could improve the ion mobility. Owing to the improved ion mobility and controlled ion migration, we demonstrated that excellent synaptic characteristics, such as a wide dynamic range, linear weight update, low operating voltage operations, and stable cyclic operation, were achieved. Finally, we confirmed an improved pattern recognition accuracy using an O-IST with an HfOx electrolyte of optimal density.

We perform experiments and device simulations to investigate the origin of current-voltage (I-V) linearity of TaO x-based resistive switching memory (RRAM) devices for their possible application as electronic synapses. By using electrical characterization and simulations, we link the electrical characteristics (linear or nonlinear I-V) to the microscopic properties of the conductive filament (CF). Our findings indicate that the shape and the thermal properties of the CF region are crucial to achieve linear I-V characteristics. These results allow optimizing the I-V curve linearity of TaO x-based RRAM devices, explaining the wide range of linear I-V characteristics experimentally observed on RRAM device obtained. When weight sum operation using SPICE simulations is performed, the read current is improved under the condition of linear I-V characteristics due to current loss minimization. INDEX TERMS I-V linearity, neuromorphic system, resistive switching memory (RRAM), TaO x .