A hollow-core anti-resonant fiber for the THz regime is proposed and demonstrated. The proposed fiber is the hexagonal core shape which is directly extruded using a conventional 3D printer. Experimental results show that by using cyclic olefin copolymer (COC), the proposed fiber design provides a low attenuation of ∼3 dB∕m at ∼ 0.86 THz and ∼15 dB∕m at ∼ 0.42 THz.

Terahertz time-domain spectroscopy (THz-TDS) is a proven technique whereby the complex refractive indices of materials can be obtained without requiring the use of the Kramers-Kronig relations, as phase and amplitude information can be extracted from the measurement. However, manual pre-processing of the data is still required and the material parameters require iterative fitting, resulting in complexity, loss of accuracy and inconsistencies between measurements. Alternatively approximations can be used to enable analytical extraction but with a considerable sacrifice of accuracy. We investigate the use of machine learning techniques for interpreting spectroscopic THz-TDS data by training with large data sets of simulated light-matter interactions, resulting in a computationally efficient artificial neural network for material parameter extraction. The trained model improves on the accuracy of analytical methods that need approximations while being easier to implement and faster to run than iterative root-finding methods. We envisage neural networks can alleviate many of the common hurdles involved in analyzing THz-TDS data such as phase unwrapping, time domain windowing, slow computation times, and extraction accuracy at the low frequency range.

Terahertz time-domain spectroscopy (TDS) is a powerful characterization technique which allows for the frequency-dependent complex refractive index of a sample to be determined. This is achieved by comparing the time-domain of a pulse transmitted through air to a pulse transmitted through a material sample; however, the requirement for an independent reference scan can introduce errors due to laser fluctuations, mechanical drift, and atmospheric absorption. In this paper, we present a method for determining complex refractive index without an air reference, in which the first pulse transmitted through the sample is compared against the “echo”, where the internal reflections delay the transmission of the echo pulse. We present a benchmarking experiment in which the echo reference method is compared to the traditional air method, and show that the echo method is able to reduce variation in real refractive index.