Interface-Mediated Self-Rectifying CsCuCl₃ Memristors With Tunable Thresholds for Reconfigurable Neuromorphic Computing

We demonstrate a simple 1R self-rectifying halide perovskite memristor with dynamically tunable thresholds for selector-free crossbar arrays and programmable T-ReLU mapping. The memristors achieve an ON/OFF ratio > 10⁵ and rectification ratio > 10⁴. The switching mechanism involves two synergistic processes: bulk ion migration within CsCuCl₃ provides and sustains the low-resistance state essential for a high ON/OFF ratio, while the Ag-Cl-containing oxidized interfacial layer introduces a Schottky barrier and trap filling/release mechanism that governs the HRS to LRS switching. Systematic simulations using CNN architectures reveal a dataset-dependent threshold sensitivity: MNIST accuracy remains robust (> 98%) regardless of threshold variations, whereas CIFAR-10 accuracy drops sharply as positive thresholds increase. Based on this complexity-dependent trend, we exploited the bi-directional tunability for dual-mode applications: (i) Negative threshold operation in CNNs maintains 91.0% accuracy for low-light image recognition (comparable to the ReLU baseline); (ii) Positive threshold modulation in a customized SparseResNet-18 architecture is explored for sparsity-aware edge computing, where thresholds τ ∈ [0, 0.3] are adjusted according to simulated battery levels to control the sparsity-accuracy trade-off. Under low-battery conditions (τ = 0.3), the simulated network achieves 76.3% activation sparsity while maintaining 90.1% accuracy without retraining. This work highlights the potential of tunable activation thresholds for reconfigurable neuromorphic systems.