Interface Engineering for Scalable Optoelectronic Reservoir Computing

Two-dimensional (2D) materials offer an attractive platform for optoelectronic reservoir computing (RC) and neuromorphic hardware, promising energy-efficient in-sensor processing for edge intelligence. However, scaling such systems to practical large-scale arrays is hindered by substantial device-to-device variability due to the stochastic distribution of intrinsic defects in these materials. Here, we demonstrate a scalable and highly uniform reservoir array based on vertical p-GaN/n-MoS₂ heterojunctions via an interface engineering strategy. A controlled thermal pretreatment process produces a uniform GaO_X interlayer with a high density of statistically homogeneous defects, which serve as reproducible carrier trapping centers to generate reliable memory effects. This approach ensures highly consistent nodal responses across the array, overcoming a key bottleneck in 2D material-based neuromorphic hardware. The system exhibits robust spatiotemporal processing capabilities, experimentally realizing dynamic trajectory reconstruction, an 87.24% accuracy in static digit classification, and a normalized mean squared error of 7.01 × 10^-5 in predicting second-order nonlinear dynamics. These results establish interface engineering as a decisive route to overcoming the uniformity bottlenecks of 2D materials, advancing the practical implementation toward wafer-scale optoelectronic neuromorphic hardware.