Reliable Ru‐Based CBRAM Synapses for Energy‐Efficient Neuromorphic Computing: Suppressing Filament Overgrowth and Read Noise via Ru‐Pt Alloy and AlO x Engineering

Transition metal‐based conductive‐bridge RAM offers superior non‐volatility and stable low‐resistance states in the MΩ resistance range compared to noble metal (Ag, Cu) counterparts; however, achieving stable multi‐level operation with low read noise under low‐current conditions remains a critical challenge. This study reports a dual‐material engineering strategy altering filament transport by combining a Ru–Pt alloy electrode with an AlO x switching layer. Pt incorporation suppresses excessive Ru ion injection, while the low‐defect‐density AlO x matrix stabilizes filament evolution, leading to a nanocluster‐based conductive pathway rather than a continuous metallic filament. Temperature‐dependent electrical measurements reveal thermally activated hopping conduction (Ea ∼0.44 eV), and current–voltage analysis indicates hopping distances of 1.0–1.9 nm across resistance states, confirming gradual inter‐cluster spacing modulation. This filament structure enables 16 distinct conductance states with reduced read noise over TaO x ‐ and HfO x ‐based devices. Incorporated into a convolutional spiking neural network framework, the device achieves 80.97% FMNIST inference accuracy (post‐training quantization only) and ultralow inference energy of 0.817 pJ/inference in the FC layer—roughly three orders of magnitude lower than Ag‐ and Cu‐based ECM systems. These results establish a direct link between materials design, filament physics, and system‐level performance, highlighting dual‐material engineering as a pathway toward energy‐efficient, high‐density analog synaptic devices.