A General Neural Network Model for Complex Refractive Index Extraction of Low-Loss Materials in the Transmission-Mode THz-TDS

The complex refractive index for low-loss materials is conventionally extracted by either approximate analytical formula or numerical iterative algorithm (such as Nelder-Mead and Newton-Raphson) based on the transmission-mode terahertz time domain spectroscopy (THz-TDS). A novel 4-layer neural netwo...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 7877
Main Authors: Zhou, Zesen, Jia, Shanshan, Cao, Lei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2022
MDPI
Subjects:
Accuracy
Algorithms
Artificial intelligence
Computational linguistics
Equipment and supplies
extraction
Fiber optics
Language processing
Natural language interfaces
neural network
Neural networks
optical parameters
Silicon compounds
Spectrum analysis
terahertz (THz) spectroscopy
Thin films
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The complex refractive index for low-loss materials is conventionally extracted by either approximate analytical formula or numerical iterative algorithm (such as Nelder-Mead and Newton-Raphson) based on the transmission-mode terahertz time domain spectroscopy (THz-TDS). A novel 4-layer neural network model is proposed to obtain optical parameters of low-loss materials with high accuracy in a wide range of parameters (frequency and thickness). Three materials (TPX, z-cut crystal quartz and 6H SiC) with different dispersions and thicknesses are used to validate the robustness of the general model. Without problems of proper initial values and non-convergence, the neural network method shows even smaller errors than the iterative algorithm. Once trained and tested, the proposed method owns both high accuracy and wide generality, which will find application in the multi-class object detection and high-precision characterization of THz materials.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22207877