Electrolyte Bonding Engineering for Highly Uniform GeTe-Based CBRAM and Parallel Hebbian Learning in Selector-Free Hopfield-Type Associative Networks.

Hopfield networks offer a hardware-friendly framework for energy-efficient associative memory, yet their practical realization in memristor crossbar arrays is critically hindered by device-to-device (D2D) variability, which prevents reliable parallel programming. Here, we address this bottleneck through systematic composition engineering of the Ge-Te solid electrolyte in conductive bridge random access memory (CBRAM) devices. By varying the Ge:Te ratio, we identify Ge3.5Te1 as an optimal composition, exhibiting the smallest average coefficient of variation in characteristic parameters compared to GeSe-based devices. Raman spectroscopy reveals that this improvement is associated with a narrower distribution of Ge-centered tetrahedral coordination environments, which we propose narrows the spread of Cu+ migration barriers and thereby renders filament formation more reproducible. Combining this electrolyte optimization with pore-size scaling to 200 nm, we fabricate a selector-free 16 × 16 Cu/Ge3.5Te1 CBRAM crossbar array and demonstrate a 4 × 4 Hopfield-type associative network capable of learning and recalling binary pattern pairs via fully parallel programming using a half-selection scheme. Successful pattern recall is achieved for up to two stored associations despite the absence of selector elements, establishing a proof-of-concept for selector-free hardware implementations of associative memory. These results highlight the critical role of electrolyte bonding structure in determining memristor uniformity and provide a materials-driven pathway toward scalable, parallel neuromorphic computing systems.