Pulsed Terahertz waves are widely used as non-destructive technique for dielectric characterization of dielectric materials. In the Terahertz band, a family of materials with a great applicative perspective is represented by some of the most common plastics (including Polymethyl methacrylate, Polyethylene terephthalate, Teflon etc..). In general, lenses for THz frequencies can be fabricated with this type of plastics. Refractive index, absorption and thickness of a material are generally obtained exploiting time and frequency domain measurements in the Terahertz band. These data could be well used for classification purposes combined with chemometric methods. Although this approach is still highly regarded, is gradually being flanked - and sometime even overcome - by introducing intelligent techniques based on artificial neural networks. Since the first step in classification problems is generally represented by features extraction, and this could heavily affect the classification performances, it deserves detailed discussions. In this systematic work, several tuples of features are extracted from Terahertz time domain signals reflected from a set of THz optical materials (common plastics). These tuples are suggested to be used as input data for training and testing an artificial neural network based on Multilayer Perceptron. The performance of the network for materials and thickness classification purposes is hence discussed taking into account the number and type of features, the network architecture and the proportion used to split the dataset into the training and the testing datasets. Extending this approach beyond THz optical materials, an automated methodology for the analysis and classification of potentially each unknown dielectric material could be established.

脉冲太赫兹波被广泛用作介电材料介电特性的非破坏性技术。在太赫兹带中，具有最广泛应用前景的一系列材料以某些最常见的塑料（包括聚甲基丙烯酸甲酯，聚对苯二甲酸乙二醇酯，特氟隆等）为代表。通常，可以用这种类型的塑料制造太赫兹频率的透镜。通常利用太赫兹频带中的时域和频域测量来获得材料的折射率，吸收率和厚度。这些数据可以很好地用于结合化学计量学方法进行分类的目的。尽管这种方法仍然受到高度重视，但通过引入基于人工神经网络的智能技术，这种方法正逐渐被边缘化，甚至有时被克服。由于分类问题的第一步通常由特征提取来表示，这可能严重影响分类性能，因此值得进行详细讨论。在这项系统的工作中，从太赫兹时域信号中提取了几个元组特征，太赫兹时域信号是从一组太赫兹光学材料（普通塑料）反射而来的。建议将这些元组用作训练和测试基于多层感知器的人工神经网络的输入数据。因此，考虑到特征的数量和类型，网络架构以及用于将数据集划分为训练数据集和测试数据集的比例，讨论了用于材料和厚度分类目的的网络性能。将这种方法扩展到THz光学材料之外，