Recently, deep learning has been used to establish the nonlinear and nonintuitive mapping between physical structures and electromagnetic responses of meta-atoms for higher computational efficiency. However, to obtain sufficiently accurate predictions, the conventional deep-learning-based method consumes excessive time to collect the data set, thus hindering its wide application in this interdisciplinary field. We introduce a spectral transfer-learning-based metasurface design method to achieve excellent performance on a small data set with only 1000 samples in the target waveband by utilizing open-source data from another spectral range. We demonstrate three transfer strategies and experimentally quantify their performance, among which the “frozen-none” robustly improves the prediction accuracy by ∼26 % compared to direct learning. We propose to use a complex-valued deep neural network during the training process to further improve the spectral predicting precision by ∼30 % compared to its real-valued counterparts. We design several typical teraherz metadevices by employing a hybrid inverse model consolidating this trained target network and a global optimization algorithm. The simulated results successfully validate the capability of our approach. Our work provides a universal methodology for efficient and accurate metasurface design in arbitrary wavebands, which will pave the way toward the automated and mass production of metasurfaces.

研究了一种基于注入锁定技术的888 nm 半导体激光器(LD)泵浦的高功率单频可调谐1342 nm Nd:YVO₄激光器。采用最大输出功率20 mW分布式反馈单频半导体1342 nm激光器作为注入种子，利用lock-in (LI)技术，对LD端泵的Nd:YVO₄环形腔激光器进行种子注入，实现了单频可调谐激光输出。激光器最大平均输出功率为13.9 W，测量的线宽为41 MHz，调谐范围为1341.6774~1341.8025 nm。x轴和y轴的光束质量$ M^{2} $因子分别为$ M_x^2 $= 1.30和$ M_y^2 $= 1.23。实验结果表明：与先前文献报道的注入锁定1342 nm可调谐激光的结果相比，所需种子功率大幅减小，输出功率也有所提升。

壁面光谱发射率求解是飞行器红外隐身的关键技术之一。首先设计了壁面反射光路和光源，通过光谱辐射计获取壁面反射的辐射亮度序列，为尽可能地消除外界干扰对于光谱发射率求解精度的影响，基于双向长短期记忆网络，设计了Bi-LSTM亮度回归网络模型，并对测试样本进行训练学习。基于BRDF的壁面发射率求解模型及基于Bi-LSTM网络的亮度回归模型求解壁面的发射率。计算结果显示，提出的基于双向反射分布函数的壁面发射率求解方法的相对误差为12.21%，满足工程测试需求。

空间太阳观测光学载荷在轨期间需要对日定向长期工作，入射窗位于载荷最前端，直接面对太阳，工作环境十分恶劣，因此入射窗的设计尤为重要。首先对入射窗进行了光-机-热一体化太阳直射热流影响抑制的设计，然后研究了入射窗的热分析方法以评估其对太阳热流的抑制能力，并设计了专门的热平衡试验以验证设计的合理性与分析方法的正确性，最后对热分析数学模型进行了修正，修正后模型的分析结果与试验结果吻合良好。将修正后的模型用于预测入射窗组件的实际在轨温度水平，分析得到窗口玻璃的最高温度为26.3℃，同时主镜温度可以稳定控制在22±2℃，说明所设计的入射窗组件能够满足在轨使用需求并留有一定的余量。