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Interface Engineering for Scalable Optoelectronic Reservoir Computing
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DOI：10.1002/smll.74516.png)
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Abstract
En 中文
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 GaOX 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.
Keywords:
2D materials
defects
GaN
interface engineering
neuromorphic computing
optoelectronic reservoir
thermal pretreatment