Authors
Kun Zheng, Yixiao Li, Tong Li, Changde Luo, Yi Wang, Yutao Song, Chan Ye, Sanxia Yin, Xuming Zou, Lei Liao, Rong Yang
Published in
Small (Weinheim an der Bergstrasse, Germany). Pages e74516. Jul 10, 2026. Epub Jul 10, 2026.
Abstract
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.
PMID:
42429063
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.
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