Self-adaptive Terahertz Time-Domain Spectroscopy Technology Based On Hilbert-Huang Transform And Application

Terahertz(THz)lies in the frequency gap between the millimeter wave and the infrared,has attracted extensive attention of considerable researchers owing to its great technological potential in different fields,such as communication,imaging,and contact-less material characterization.One of the most appealing features is to identify material signatures through their spectroscopic fingerprints.THz-TDS based on femtosecond laser is a useful method to realize the THz spectral characterization.Terahertz time-domain spectroscopy system(THz-TDS)allows us to measure the time-resolved and high precision THz electric field directly and then obtain the spectral information by applying a Fourier transform on the time-domain waveform.However,in spite of these advantages,THz-TDS has not been widely applied in real-world due to the fact that the THz signals are strongly absorbed by the atmospheric vapor,increase strongly interference components for the process of identification and characterization of materials,and then obscure the true spectral data.In general,the effect of water vapor noise on THz radiation is removed primarily by purging the propagation path with dry air or non-polar gas such as nitrogen,and alternatively a vacuum is sometimes used.Undoubtedly,the treatments increase the complexity and cost of the system.Here,a self-adaptive method is demonstrated for effectively identifying and eliminating atmospheric vapor noise from THz spectra of an all-fiber THz system with the Hilbert-Huang transform(HHT).The precise characterization of monolayer graphene(MG)is achieved by using the adaptive all-fiber THz-TDS.The main contents of this thesis are as follows:1.In this thesis,the HHT signal processing method is used to study the strong interaction between water vapor and THz wave in the atmosphere.HHT is a time-frequency analysis method for processing nonlinear and non-stationary signals.According to the characteristics of the input signal,it adaptively decomposes the signal into several intrinsic mode functions(IMFs).The basic concepts,basic theories,basic properties and basic principles and steps of signal decomposition are introduced.The effectiveness and adaptability of HHT algorithm for THz signals are discussed.We initially predicted the unique advantages of using HHT algorithm to eliminate water vapor noise in THz spectroscopy.2.We demonstrate a self-adaptive method for effectively identifying and eliminating atmospheric vapor noise from THz spectra,which based on an all-fiber THz-TDS combined with the Hilbert-Huang transform(HHT).The THz time-domain signal is decomposed into eight components in different time scales called the intrinsic mode functions(IMFs).Through the detailed time-frequency analysis,it is found that the decomposed first two modes(IMF1 and IMF2)are related to the sample information,whereas the other high-order modes correspond to the noise.Among them the interference of atmospheric vapor is accurately identified in the third IMF.Therefore,we can only use the first two IMF components to effectively extract sample information and eliminate water vapor noise.3.It is confirmed that the HHT based THz-TDS is strongly self-adaptive in a series of experiments when we performed the algorithm processing on different ambient relative humidity(RH 13.2%,RH 32.8%,RH 51.2%,and RH 70.4%),time window(30 ps,50ps,90 ps,and 140 ps),and different types of samples(PTFE,PET,PVC and PP).The experimental results show that the IMF1+IMF2 component can effectively remove the interference of external noise,reconstruct the real sample information,and accurately identify the interference components of water vapor noise in IMF3 component.4.The accurate characterization of MG was achieved by using the HHT-based self-adaptive all-fiber THz-TDS system.Graphene films were grown on copper foil by chemical vapor deposition.Graphene samples were prepared by PMMA assisted wet transfer technique.Based on the proposed THz system,the THz time domain pulse signal with MG sample information is decomposed into eight IMF components with different time scales.Based on detailed time-frequency analysis,the decomposed low-order mode components are related to graphene,while other high-order modes correspond to external noise.The experimental results show that IMF1+IMF2 can effectively eliminate the strong interference of water vapor noise and reconstruct the characteristic absorption peak near 0.7 THz of MG.Meanwhile,water vapor noise is accurately located in IMF3.The proposed THz-TDS based on HHT provides a more effective method for graphene characterization,which further proves the strong adaptive ability of the THz system.In a word,the perfect combination of HHT algorithm and all-fiber THz-TDS can effectively suppress the interference of atmospheric water vapor noise and achieve the accurate characterization of samples.The characterization results of graphene further prove the excellent performance of the system.We can predict that this system can not only achieve the accurate characterization of graphene,but also be effective for other two-dimensional materials.This method is simple and effective.It has strong adaptive ability to water vapor noise in the atmosphere and can be successfully applied to various humid environments,which promotes the practical application of THz wave technology and THz-TDS in THz communication,material characterization,nondestructive testing and other fields.