Coupled dual-channel memristors for hardware-native trustworthy Bayesian intelligence

Bayesian neural networks (BNNs) enable trustworthy edge intelligence by quantifying predictive uncertainty. However, hardware BNN implementations face a bottleneck: digital approaches suffer from high latency, while memristors are limited by the intrinsic coupling between their conductance state (mean) and stochastic noise (variance). Here we report a coupled dual-channel memristor (CDCM) based on an ion gel/ZnO heterostructure that breaks this fundamental trade-off. Utilizing vertical ion-gating to establish two tunable memristive channels, our device defines synaptic weight as the differential conductance between the two channels. This architecture enables the hardware-native orthogonal control over the synaptic weight mean (μ) and standard deviation (σ), allowing for the precise synthesis of decoupled Gaussian weights. We validate our approach with a hardware-calibrated BNN for multimodal human activity recognition, achieving 79.08% accuracy while reliably detecting unseen activities as out-of-distribution anomalies. This work provides a scalable, physics-driven paradigm for energy-efficient, inherently trustworthy probabilistic computing. Probabilistic computing, such as Bayesian neural networks, quantifies predictive uncertainty to prevent overconfident predictions. Liu et al. demonstrate a coupled dual-channel memristor that independently controls synaptic weight mean and variance, enabling trustworthy AI in safety-critical edge applications.