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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.

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In this study, we investigated the effect of an Al2O3 barrier layer in an all-solid-state inorganic Li-based nano-ionic synaptic transistor (LST) with Li3PO4 electrolyte/WO x channel structure. Near-ideal synaptic behavior in the ultralow conductance range (∼50 nS) was obtained by controlling the abrupt ion migration through the introduction of a sputter-deposited thin (∼3 nm) Al2O3 interfacial layer. A trade-off relationship between the weight update linearity and on/off ratio with varying Al2O3 layer thickness was also observed. To determine the origin of the Al2O3 barrier layer effects, cyclic voltammetry analysis was conducted, and the optimal ionic diffusivity and mobility were found to be key parameters in achieving ideal synaptic behavior. Owing to the controlled ion migration, the retention characteristics were considerably improved by the Al2O3 barrier. Finally, a highly improved pattern recognition accuracy (83.13%) was achieved using the LST with an Al2O3 barrier of optimal thickness.

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.