The inverse design of photonic devices through the training of artificial neural networks (ANNs) has been proven as an invaluable tool for researchers to uncover interesting structures and designs that produce optical devices with enhanced performance. Here, we demonstrate the inverse design of ultra-narrowband selective thermal emitters that operate in the wavelength regime of 2-8 µm using ANNs. By training the network on a dataset of around 200,000 samples, wavelength-selective thermal emitters are designed with an average mean squared error of less than 0.006. Q-factors as high as 109.2 are achieved, proving the ultra-narrowband properties of the thermal emitters. We further investigate the physical mechanisms of the designed emitters and characterize their angular responses to verify their use as thermal emitters for practical applications such as thermophotovoltaics, IR sensing and imaging, and infrared heating.

中文翻译：

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人工神经网络设计的超窄带选择性热辐射器的逆向设计

通过人工神经网络 (ANN) 的训练对光子器件进行逆向设计已被证明是研究人员发现有趣结构和设计的宝贵工具，这些结构和设计可生产具有增强性能的光学器件。在这里，我们展示了在 2-8 µ波长范围内工作的超窄带选择性热发射器的逆向设计m 使用人工神经网络。通过在大约 200,000 个样本的数据集上训练网络，波长选择性热发射器的平均均方误差小于 0.006。实现了高达 109.2 的 Q 因子，证明了热发射器的超窄带特性。我们进一步研究了设计发射器的物理机制并表征了它们的角响应，以验证它们作为热发射器在实际应用中的用途，如热光伏、红外传感和成像以及红外加热。