A wide variety of materials and device architectures have been explored for memristor applications targeting neural network simulations, most of which rely on oxide-based structures that exhibit resistive switching driven by oxygen-vacancy-mediated memory effects. In this study, we present a novel approach for modulating resistive and nonvolatile memory behavior in oxide semiconductors through the controlled injection and extraction of hydrogen. The proposed two-terminal device incorporates a hydrogen source layer that facilitates the diffusion of hydrogen ions into the active oxide matrix, where they form hydroxide (OH) bonds and locally modulate the electron concentration. This process induces a stable and reversible memory effect under an applied electric field. Hydrogen exchange predominantly occurs at the interface between the active and insulating layers, with the latter serving as a buffer to maintain an optimal hydrogen concentration. Furthermore, neural network simulations were performed by utilizing the synaptic characteristics controlled via hydrogen modulation, achieving a recognition accuracy of 97.2% on the MNIST data set. The effects of input data resolution and weight quantization on recognition performance were also systematically investigated and discussed.
Noh et al. (Wed,) studied this question.