Hydrogen‐Stabilized Self‐Rectifying Memristor Arrays for Reliable Multilevel Synapses in Transformer‐Based Keyword Spotting

ABSTRACT This study proposes a strategy to simultaneously improve conductance uniformity and data retention characteristics by introducing the incremental step pulse with verify algorithm (ISPVA) technique and hydrogen (H ₂ ) annealing into a non‐filamentary TiN/Ti/HfO ₂ /TiO _x /TiN resistive switching memory device. The high Schottky barrier formed at the Ti/HfO ₂ interface induces asymmetric electron injection and limits reverse current flow, resulting in a rectifying ratio of approximately 1442. This self‐rectifying characteristic provides an intrinsic advantage in suppressing sneak currents in crossbar arrays. The ISPVA technique improves the linearity and uniformity of conductance modulation, enabling the implementation of up to 6‐bit multilevel states within a few‐µA current range. In addition, H ₂ annealing stabilized conduction by forming hydrogen bonds with oxygen vacancies in the oxide layer and suppressing oxygen ion–vacancy recombination. As a result, data retention over 10 ⁴ s and endurance exceeding 10 ⁴ cycles were achieved even under a low energy consumption of 36.3 pJ. Furthermore, the experimentally obtained long‐term potentiation and depression characteristics were implemented in a Transformer‐based keyword spotting (KWS) model, achieving a recognition accuracy of 92.5%. These results suggest that the proposed device enables controlled analog conductance modulation with improved stability, showing its potential for Transformer‐based neuromorphic computing applications.