太赫兹波具有高穿透性、低能性及指纹谱性等特征，被广泛应用于探测领域，因此，设计太赫兹波成像光学系统具有重要的意义和广泛的应用前景。首先，以四块透镜构成的天塞物镜为参考结构，应用近轴光学系统像差理论构建系统像差平衡方程，给出了系统初始结构参数求解函数和方法，再结合光学设计软件进一步校正系统像差，最终设计了一种用于太赫兹波探测的大孔径光学成像系统。该光学系统由4块同轴折射透镜构成，焦距为70 mm，F数为1.4，全视场角为8°，在奈奎斯特频率10 lp/mm处全视场角范围内的调制传递函数（MTF）值均大于0.32，各视场内的弥散斑均方根（RMS）半径均小于艾里斑半径。最后对系统各种公差进行分析和讨论。设计结果表明，本文设计的太赫兹波探测光学成像系统具有孔径大、结构简单且紧凑、成像质量较好且加工性易于实现等特点，满足设计要求，它在太赫兹波段高分辨率探测领域具有重要应用价值。

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.