AI 中文总结
该研究提出了基于VO2、GST等相变材料的热卷积神经网络（T-CNN）和热循环神经网络（T-RNN），实现了非电子的辐射热计算，为热深度学习提供了首个辐射式方案，开拓了新的计算范式。
AI 中文摘要
我们提出并分析了一种可编程近场辐射热网络，该网络通过热交换模拟卷积和循环操作。通过集成由VO2（二氧化钒）和GST（锗锑碲）等相变材料构成的辐射热二极管和晶体管，我们构建了两种基础架构：热卷积神经网络（T-CNN）和热循环神经网络（T-RNN）。T-CNN通过依赖温度的发射率调制执行空间模式识别，其中每个门充当可调谐辐射滤波器，用于放大或抑制局部热通量。该系统可通过VO2/GST中的门温度分布进行外部编程，实现加权求和与非线性激活；滞后转变提供可用于存储的状态依赖响应。T-RNN在时间维度扩展了该功能，嵌入辐射反馈和滞后相变以实现存储与序列推理。这些系统共同展现了三态逻辑行为、辐射增益和非易失性热存储，这些都是物理学习的关键属性。每个逻辑节点无需电荷载流子或电路，仅依赖纳米级表面间的近场光子隧穿运行。所得框架可在非接触、节能平台内直接实现卷积、存储保持和反馈学习。本研究实现了热深度学习的首个辐射式实现，揭示了一种新的计算范式，其中温度场作为智能载体，将传热、存储和自适应推理统一在单一非电子系统中。
英文摘要
Near field radiative heat transfer provides a route for information processing in which thermal radiation, rather than charge transport, serves as the physical carrier of signals. Here, we propose and theoretically analyze a programmable near field radiative thermal computing framework in which radiative coupling, phase change nonlinearity, and thermal state memory are mapped onto neural network inspired operations. The framework is constructed from near field radiative thermal diodes, transistors, and multi terminal logic units separated by nanoscale gaps. Radiative heat flux represents the propagated thermal information, while geometry and material dependent radiative coupling provides physically constrained weighting, and the temperature dependent optical response of phase-change materials enables nonlinear modulation and logic state control. Based on these primitives, we formulate a radiative thermal convolutional network for spatial information processing and a radiative thermal recurrent network for history dependent computation. The recurrent response is associated with radiative feedback, thermal relaxation, and phase change hysteresis, with VO2 providing history dependent short term memory and GST offering a possible route toward non volatile phase storage. We further distinguish the physical radiative networks from a separate software based inverse identification study, in which recurrent machine-learning models are trained on simulated near field heat flux temperature characteristics to recover structural parameters. By establishing a bottom up connection between fluctuational electrodynamics, radiative thermal logic, programmable thermal states, and neural network inspired computation, this study provides a physically grounded basis for exploring non contact thermal information processing at the near field limit.
Comments26 pages, 5 figures, 3 tables
