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Programmable mixed-kernel based on MoTe2/MoS2 heterojunction for support vector machine learning
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DOI：10.1088/1674-4926/25070039.png)
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Abstract
En 中文
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-intensive artificial intelligence applications. A promising approach involves designing specialized hardware with on-chip parameter tunability, which directly accelerates machine learning functions. This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure. We designed and fabricated a MoTe2/MoS2 type-Ⅱ vertical heterojunction phototransistor, which exhibits a non-monotonic, Gaussian-like optoelectronic response owing to its unique interlayer charge transfer mechanism. This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics. Furthermore, the hardware kernel can be continuously modulated by in-situ tuning of external optical stimuli. The mixed-kernel exhibited exceptional performance, achieving precision, accuracy, and area under the curve (AUC) values of 95.8%, 96%, and 0.9986, respectively, significantly outperforming conventional kernels. By successfully embedding a complex, adaptable mathematical function into the intrinsic physical properties of a single device, this work pioneers a novel pathway toward next-generation, energy-efficient intelligent systems with hardware-level adaptability.
Keywords:
MoTe2/MoS2 heterojunction
mixed-kernel function
support vector machine
optoelectronic response
hardware-level adaptability
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5.3
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277
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4.0K
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