Joonyeon Hwang

Memristors enable parallel computation in next‐generation memory systems. Among the materials investigated, colloidal quantum dots (QDs) are attractive owing to their size‐tunable electronic structure, solution processability, and versatile surface chemistry. Nevertheless, most QD‐based memristors exhibit digital resistive switching (D‐RS) with abrupt binary transitions, limiting their neuromorphic potential. Here, an InP/ZnSe/ZnS QD‐based memristor exhibiting analog resistive switching (A‐RS) is demonstrated through a multilayer ZnO/QD/ZnO architecture engineered to promote uniform areal charge trapping. ZnO nanoparticle interlayers enhance electron transport, block hole injection to suppress recombination, and induce interfacial trap sites via controlled oxidation of the QD surface. Additional thinning of the QD layer mitigates vertical trap accumulation, ensuring stable and spatially distributed conduction. The resulting device exhibits robust trap‐controlled A‐RS and reproduces synaptic behaviors, including excitatory/inhibitory postsynaptic currents and long‐term potentiation/depression. Benchmarking with hardware‐level neural network simulations demonstrates excellent performance, achieving recognition accuracies of 92% in the multilayer perceptron model and 87.3% in the convolutional neural network trained on the CIFAR‐10 dataset, both approaching the performance of an ideal memristor. These results establish a viable pathway for exploiting the intrinsic properties of colloidal QDs in high‐performance memristors, demonstrating clear advantages over conventional D‐RS counterparts and underscoring their potential for neuromorphic compute‐in‐memory architectures.