Time Domain Reflectometry Measurements Research Articles

Unveiling the Sub-10 GHz Performance of SMA Connectors: A Comparative Analysis

This research review article provides a detailed examination of SMA (SubMiniature version A) connectors, which are integral components in high-frequency electronic systems. Through extensive S-parameter and time-domain reflectometry (TDR) measurements conducted on various SMA connector constructions, this study aims to evaluate the performance and impact of SMA connectors on signal integrity. Results reveal insights into the comparative performance of different SMA connector types mounted on PCB land pads, highlighting their strengths and limitations. Additionally, this paper explores the application of reference plane cut-outs for discontinuity impedance compensation, aiming to enhance the frequency response of SMA connectors. By linking measured performance parameters with relative market prices, this study offers valuable insights into the economic viability of different SMA connector types. The best and worst performing SMA connector measurements reveal an S11 < −10 dB bandwidth of more than 8 GHz and 1.5 GHz and a transition impedance of 46.5 Ω and 21 Ω, respectively. Overall, this research contributes to advancing the understanding and selection of SMA connectors for RF applications in telecommunications, aerospace, medical devices, and beyond.

Detection of air- and water-filled cavities beneath concrete plate using electromagnetic and acoustic waves

Underground excavation and tunneling may result in cavities filled with air or water. This study aims to detect cavities beneath underground concrete walls or tunnel linings and assess their risks by estimating the cavity thickness and width using electromagnetic and acoustic waves. In this study, air- and water-filled cavities are simulated, and nondestructive testing methods such as ground penetrating radar (GPR), time domain reflectometry (TDR), and microphones are used to investigate the thickness and width of a cavity beneath a reinforced concrete plate. The results show that GPR signals converted from temporal to spatial scales, combined with TDR measurements, can be used to accurately examine the cavity thickness. However, the examination of the cavity width based on GPR signals remains challenging because of the strong parabolic patterns caused by steel bars. Therefore, this study employs flexibility, which is the ratio of the particle velocity of the structure to the load signal according to the frequency. The flexibility of the concrete plate calculated using microphone signals increases in the cavity section for both air- and water-filled cavities because the flexural vibration mode prevails at the cavity–concrete plate interface. The characteristics of the microphone signals analyzed in the context of flexibility enabled the prediction of the cavity width beneath the concrete plate in both air- and water-filled cavities. GPR and TDR signatures combined with microphone signals can be used for the detection and risk assessment of cavities beneath underground structures when subjected to variations in the groundwater level.

Potential of ground-penetrating radar to calibrate electromagnetic induction for shallow soil water content estimation

Ground-penetrating radar (GPR) and electromagnetic induction (EMI) are used to determine and map soil water content (SWC) in the agricultural landscape. While GPR provides a straightforward estimation of SWC, EMI requires site-specific calibrations mainly based on point-scale measurements such as time domain reflectometry (TDR). However, there is a significant difference in the measurement volumes between EMI and point-scale TDR measurements. This study aimed to enhance the calibration of EMI for estimating SWC in the agricultural landscape by leveraging the larger sampling volumes provided by GPR. Apparent electrical conductivity (ECa) from a multi-coil EMI sensor and dielectric constant from GPR with two center frequencies (500 MHz and 250 MHz) were collected as soil proxies along with TDR measurements. Calibration models were developed under irrigated conditions and the model evaluation was conducted under natural moisture conditions. Correlations were assessed between the proxies from GPR and EMI with TDR-derived SWCs. Simple linear regression (SLR) models were developed between EMI and GPR data to predict SWC. Strong positive correlations (r ≥ 0.80) were observed between the proxies (GPR and the shorter inter-coil spacing (0.32 m) of EMI) and TDR-measured SWC. ECa data from vertical and horizontal coil orientations of the shorter inter-coil spacings were selected to develop SLR models with GPR. The SLR models with the GPR 500 MHz frequency showed a higher coefficient of determination (R2 > 0.70) for both coil orientations of EMI. Results showed the potential of using GPR to calibrate EMI for shallow SWC estimation (below 0.5 m). Nevertheless, the EMI estimated SWCs were not effectively verified with GPR-estimated SWCs, which could be attributed to specific site conditions in the study area. Further research must focus on improving the calibration and model evaluation by incorporating the variability of other soil parameters (soil porosity, pore-water conductivity, and soil salinity) that may affect the SWC–ECa relationship.

Estimation of Soil Moisture Content of Farmlands Based on AEA Method of GPR

We used ground penetrating radar (GPR) to estimate the soil moisture content and its spatial distribution of farmland under different tillage modes in this paper. GPR data are collected on the farmlands with no-tillage (NT) and deep ploughing (DP), respectively. The average envelope amplitude (AEA) of GPR signal is used to estimate the soil moisture content which is constrained by time domain reflectometry (TDR) measurement. The findings of the field test indicate that the error of the moisture content obtained by AEA method is in a reasonable range. In the shallow layer of farmland, the AEA method of GPR for detecting the soil moisture content is feasible and different tillage modes can affect the change of soil moisture content.

Crosstalk Reduction in High-Density Radio Frequency Printed Circuit Boards: Leveraging FR4 Coating Layers

The escalating component density in radio frequency (RF) systems presents a growing challenge related to the coupling of adjacent microstrip lines in high-density printed circuit boards (PCBs). As a result, to tackle this prominent issue, there is a continuous pursuit of innovative techniques to effectively minimize the coupling effects among closely spaced microstrip lines. This paper proposes a reduction in the coupling of adjacent lines by utilizing a coating (stiffener) layer, which is commonly used in rigid-flex PCB fabrication. For this purpose, a reference 50 Ohm coupled line performance was compared to three coupled lines with track widths of 1.39 mm, 1.30 mm, and 1.25 mm, respectively, all at a fixed distance between the tracks. These decreasing widths were used to achieve the same 50 Ohm impedance for the coupled lines when covered with different coating layers. Each of these three coupled lines was covered with different coating (stiffener) layers, measuring 0.1 mm, 0.3 mm, and 0.5 mm in thickness, respectively. The manufactured device under test (DUT) structures underwent time-domain reflectometry (TDR) and S-parameter measurements. The TDR measurements of the DUT structures with coating layers demonstrated excellent conformity to the 50 Ohm reference coupled line. Meanwhile, the S21 measurements indicated a significant decrease in the crosstalk. For example, for a coating layer thickness of 0.3 mm, the crosstalk decreased by approximately 5–6 dB within the frequency range up to 5 GHz. When the coating layer thickness was 0.5 mm, the crosstalk decreased by approximately 10 dB or more.

Uniplanar multirod TDR probe for soil water content measurement over a larger spatial scale

AbstractThis study developed a uniplanar multirod time domain reflectometry (TDR) probe and evaluated it in laboratory experiments for soil water measurement over a larger spatial scale. The sampling volume of the probe was determined by simulating the electric potential distribution around the probe rods by using the HYDRUS‐2D software package and verified by comparison with the measured dielectric constant in saturated sand. The performance of the probe was evaluated by comparing its dielectric measurements in wet sand over a wide range of water content during evaporative drying with that measured by CS640 probes. Additionally, the dependency of visible light reflection on the water content of the sand was evaluated. The results revealed that the average effective thickness of the probe's sampling volume was 5–6 cm on both sides of the plane of the probe rods. The probe accurately measured the soil water content over a 0.88 m2 spatial area. The visible light reflectance of the variably saturated sand surface and the corresponding water content of the sand follow a soil water retention function‐type relationship. The usage of the multirod TDR measurement system at a larger spatial scale carries practical implications, especially in increasing the accuracy of remote sensing data.

Estimating soil water and salt contents from field measurements with time domain reflectometry using machine learning algorithms

Soil water and salt contents are key soil physical parameters that play a crucial role in soil-related hydrological, ecological, environmental, and agricultural processes. Time domain reflectometry (TDR) is commonly used to measure in-situ soil water and salt contents, and provide possible solutions to quickly obtain soil bulk density (BD). However, the measurement accuracy is greatly influenced by the interaction of soil water and salt contents on the measured soil dielectric constant and electrical conductivity, especially for salinized soils. To accurately estimate the soil gravimetric (GWC) and volumetric (VWC) water contents, soil salt content (TS), and BD based on the TDR measurements, we designed different model input schemes to quantify the effect of different soil factors, and applied eight machine learning algorithms to map the non-linear relationship between model inputs and each target soil property. Results of a case study in Hetao Irrigation District in Northwest China indicated that soil particle-size fractions (psfs) are important inputs to predict all the above soil properties. Furthermore, BD mainly contributes to the prediction of soil GWC, and soil surface temperature (T) is effective in improving the GWC and TS estimations. Among eight machine learning algorithms used, extreme gradient boosting (XGB) and gradient boosting regression tree (GBRT) showed good robustness and strong learning capacity. It is recommended to apply XGB to precisely estimate GWC and BD, which resulted in the coefficients of determination (R2) of 0.80 and 0.69, respectively. On the other hand, GBRT precisely estimated the VWC and TS with R2 of 0.71 and 0.84, respectively. The evaluation of spatial distribution characteristic indicated that it is reliable to obtain the spatial distributions of the above soil properties from the TDR measurements based on the recommended model input schemes and machine learning algorithms.

Reflection characteristics of water treeing insulation in medium voltage cables

This paper presents an analytical model based on wave reflection of dual interfaces for studying the reflection due to water treeing in the cross-linked polyethylene (XLPE) insulation of medium voltage cables. The model is expected to predict the reflection characteristics more accurately than that only considering reflection of a single interface. Experiments on a cable sample with mimic water trees are conducted to confirm the effectiveness of the presented model. The model can potentially be used to relate time-domain reflectometry (TDR) measurement results with characteristics of water treeing insulation in the cables.

Surface-Mount Zero-Ohm Jumper Resistor Characterization in High-Speed Controlled Impedance Transmission Lines.

Zero-ohm resistors, also known as jumpers, are commonly used in early radio frequency (RF) prototypes as they can help engineers identify the most optimal engineering solution for their system or create application-specific hardware configurations in products. One of the key considerations when using zero-ohm jumpers in RF circuits is the potential for signal loss and interference. Every circuit connection creates a small amount of resistance and impedance, eventually adding up over long distances or in complex circuits. This paper proposes a quantitative characterization summary of standard 0201-, 0402-, 0603-, and 0805-size surface-mount package jumpers, as well as lead-free and lead solder wires, in high-frequency applications by means of time domain reflectometry (TDR) and S-parameter measurements. The typical offset from the target 50 Ω impedance was measured to be around 3 Ω, or 5.8% relative to the measured reference value. According to S-parameter measurement results, no visible impact on attenuation was spotted up to 5 GHz compared to the reference S21 curve.

Local Calibration of TDR Measurements for Determining Water and Organic Carbon Contents of Peaty Soils

Time domain reflectometry (TDR) measurements of the volumetric water content (θ) of soils are based on the dielectric permittivity (ε), relating ε to θ, using an empirical calibration function. Accurate determination of θ for peaty soils is vital but complicated by the complexity of organic soils and the lack of a general calibration model. Site-specific calibration models were developed to determine θ from TDR measurements for a heterogenous peatland across gradients of peat decomposition and organic carbon (OC) content; derived by soil organic matter conversion. The possibility of predicting OC contents based on the corrected θ (θcor); ε; electrical impedance (Ζ); and a categorical predictor variable was explored. The application of plot-specific and local area calibration models resulted in similar results. Compared to common calibrations, the threshold for accurate determination of θ was at ε = 5; with higher ε underestimating θ by up to 25%. Including the von Post degree of peat humification as a bioindicator, the OC content could be modelled across the area and the full range of θ with an accuracy of ±1.2% for 496 measurements. In conclusion, a strong indication was found for determining OC in peatlands in situ using TDR and a site-specific calibration model for θ together with indices of peat decomposition.

Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks

AbstractInterpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution.

Investigation of Frequency Models to Predict Dynamic Behavior of ESD Protection Networks

With the shrinking dimension of technologies and safety requirements, electrostatic discharge (ESD) protections play a more and more important role. Consequently, the prediction of the transient behavior's protection network is becoming difficult for system level designers in order to guarantee systems safety. A model reproducing the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> behavior is needed to obtain a precise simulation's protection strategy network, especially during the protection triggering. In this article, we propose a complete measurement and computation setup to access an equivalent frequency model of devices under strong pulse injections for a rapid model construction. Details of the measurement and data computation to obtain a frequency model using a transmission line pulse generator combined with time-domain reflectometry measurement will be provided. To validate our proposed frequency model, a comparison between the measurements and simulations is first performed on passive and linear components, and then on two protection devices (transient voltage suppressor). A simulation on a protection network, at the board level, will show that the combination of the frequency models could be done to predict its response to ESD pulse with an acceptable precision, without the tedious step of component modeling.

In situ estimation of soil hydraulic and hydrodispersive properties by inversion of electromagnetic induction measurements and soil hydrological modeling

Abstract. Soil hydraulic and hydrodispersive properties are necessary for modeling water and solute fluxes in agricultural and environmental systems. Despite the major efforts in developing methods (e.g., laboratory-based, pedotransfer functions), their characterization at applicative scales remains an imperative requirement. Accordingly, this paper proposes a noninvasive in situ method integrating electromagnetic induction (EMI) and hydrological modeling to estimate soil hydraulic and transport properties at the plot scale. To this end, we carried out two sequential water infiltration and solute transport experiments and conducted time-lapse EMI surveys using a CMD Mini-Explorer to examine how well this methodology can be used to (i) monitor water content dynamic after irrigation and to estimate the soil hydraulic van Genuchten–Mualem parameters from the water infiltration experiment as well as (ii) to monitor solute concentration and to estimate solute dispersivity from the solute transport experiment. We then compared the results with those estimated by direct time domain reflectometry (TDR) and tensiometer probe measurements. The EMI significantly underestimated the water content distribution observed by TDR, but the water content evolved similarly over time. This introduced two main effects on soil hydraulic properties obtained by the two methods: (i) similar water retention curve shapes, but underestimated saturated water content from the EMI method, resulting in a scaled water retention curve when compared with the TDR method; the EMI-based water retention curve can be scaled by measuring the actual saturated water content at the end of the experiment with TDR probes or by weighing soil samples; (ii) almost overlapping hydraulic conductivity curves, as expected when considering that the shape of the hydraulic conductivity curve primarily reflects changes in water content over time. Nevertheless, EMI-based estimations of soil hydraulic properties and transport properties were found to be fairly accurate in comparison with those obtained from direct TDR measurements and tensiometer probe measurements.

Drone-based ground-penetrating radar (GPR) application to snow hydrology

Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.

Condition monitoring of power module using S-parameters, TDR, and TDT

Condition monitoring of power electronics is gaining interest as power electronics expends to mission-critical applications. This paper offers a preliminary experimental study of the viability of using S-parameters and time-domain reflectometry measurements to monitor the degradation of a power module. To this end, measurements were performed on a standard high current power module which was artificially aged by cutting wire bonds one by one. The study demonstrates a clear trend in the evolution of measured parameters, and thereby the possibility to monitor the power module degradation under the specific test conditions. This opens a new condition monitoring technology for power electronics systems.

Testing of a commercial vector network analyzer as low-cost TDR device to measure soil moisture and electrical conductivity

Time Domain Reflectometry (TDR) is a non-destructive technique to determine the soil apparent dielectric constant, εa, the volumetric water content, θ, and bulk electrical conductivity, σ. However, the high cost of TDR devices may limit its use. This study evaluates two different low-cost Vector Network Analyzers (VNA) commercially available (NanoVNA), with 1.5 (VNA1.5) and 3.0 (VNA3.0) GHz maximum operating frequency. NanoVNA can be used for measurements of Frequency Domain Reflectometry (FDR) or, after suitable post-processing, for θ and σ TDR measures. Although FDR and TDR are dual procedures, TDR is easier to interpret for soil experiments. The TDR waveforms and εa measured with NanoVNA connected to 10 and 20 cm length three-rod probes immersed in air, distilled water, and a soil column with different θ were compared to those measured using a TDR100 (Campbell Sci.) instrument. The capacity of VNAs to measure σ was evaluated by immersing a 10 cm length three-rod probe in different NaCl-water solutions. Measurements obtained with the VNA and TDR100 were compared in a field test using two-rod 22 cm length TDR probes inserted in soil plots with increasing water content. A robust fit was observed between TDR waveforms registered with the two VNAs and the TDR100. Although VNA3.0 doubles the frequency range of VNA1.5, both devices allowed for good estimates of εa (εaVNA1.5, 3.0 = 1.001 εaTDR100 – 0.2125; R2 = 0.999). These results indicate that the low-cost VNA devices can measure soil water content with similar accuracy and precision as the TDR100. A good agreement (σVNA1.5, 3.0 = 0.999 σCM + 0.0023; R2 = 0.999) was also observed between the σ measured using a conductivity meter (CM) and that estimated with the VNAs. Finally, a good correlation was also observed between θ measured in the field experiment with TDR100 and the VNA1.5 and VNA3.0 devices.

Optical Transmitter Equalization With Tunable Mismatched Terminations in a Silicon Modulator

We demonstrate a 2.5 mm silicon photonics traveling-wave Mach-Zehnder modulator with on-chip tunable termination to study termination mismatching as a technique for optimizing modulator performance by leveraging the monolithic integration of electronic and photonic devices. Time domain reflectometry measurements demonstrate the tunability of the termination impedance with respect to the modulator transmission line Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Electro-optic frequency response measurements show the bandwidth enhancement effect of the termination mismatch, whereby a 3 dB bandwidth above 40 GHz was achieved. Optical link time-domain measurements were performed to optimize the termination mismatch for maximum eye-diagram opening at 50 Gb/s. Our measurements demonstrate that active on-chip termination can be utilized to optimize the modulator performance and mitigate the risk incurred from fabrication variations which can result in a sub-optimal termination impedance.

Face-to-Face Cable Interconnect Scheme for Thin Flexible Superconducting Stripline Cables

To address the challenge of dense connectors for cryogenic systems, we describe a face-to-face cable connection approach for connecting microwave superconducting stripline cables. The approach provides a space-efficient and reliable interconnect technology for future cryogenic and quantum technology applications at microwave frequencies. A self-aligned assembly was used and was comprised of bottom and top alignment molybdenum pieces and flexible superconducting stripline cables. SU-8, a photodefinable epoxy-based photoresist, was used to form structures defined on the bottom and top Mo pieces to provide alignment and distribute mechanical pressure in the contact regions of the cables. We measured critical transition temperatures in the range of 8.6-9.0 K and an average critical current of 10.2 mA. Time-domain reflectometry measurements at 4.2 K showed a relatively small impedance discontinuity at the connection point and minimal microwave power dependency was observed. This cable-to-cable connection approach for thin, flexible superconducting cables shows promising results as a solution for dense signal integration for microwave and DC interconnects at cryogenic temperatures.

Comparison Study of Time Domain Reflectometry (TDR) Measurement Methods for Detecting Wire Interconnect Related Open-Contact and Short-Contact Failures in Power Semiconductor

Open-contact and short-contact are 2 common failures that causing power semiconductor to have electrical malfunctioned. One of the reasons of these failures relate to defective in the wire interconnect. Time domain reflectometry (TDR) is a common method widely used to detect failures in semiconductor devices. This study focused on comparing 3 different TDR measurement methods in detecting wire related open and short failure for power semiconductor, namely Single-Ended method, Single-Pin Grounded method and All-Pin Grounded method. A special custom-made jig is used during the measurements to ensure all the methods are measured under the same conditions. This study shows that all the 3 TDR measurement methods are able to detect wire related open and short failures in power semiconductor. The Single-Pin grounded method is the preferred method because it is easy in term of setup and having the good capability in detecting both open and short failures. Furthermore, the study also shows that the TDR is able to detect the location of open and short failures that is not able to achieve by using the conventional electrical testing method.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Comparison study on Single-Ended, Single-Pin Grounded and All-Pin Grounded TDR measurement methods in detecting open-contact and short-contact failure in power semiconductor&#x0D; The study shows Single-Pin Grounded TDR measurement method is the preferred method since it is easy in term of setup and able to detect both the open-contact and short-contact failures&#x0D; TDR measurement method is also able to locate the location of open-contact and short-contact failures&#x0D; &#x0D; GRAPHICAL ABSTRACT

Estimation of bulk electrical conductivity in saline medium with contaminated lead solution through TDR coupled with machine learning

Time-domain reflectometry (TDR) has been used for the characterization of media; however, the results of TDR tests significantly differ according to the types of solutions. The objective of this study is to suggest a new relationship between TDR output values and bulk electrical conductivity based on a machine learning algorithm for enhancing the reliability of TDR measurement. Various salinities (0%, 1%, 2%, and 3%) and lead concentrations (0, 0.5, 1, 2, 5, and 10 mg/L) are applied along with silica sand, classified as SP in USCS, to create media. A laboratory test is performed to measure the TDR waveform at the bottom of the cylindrical cell, and a resistance probe is also installed to obtain the true bulk electrical conductivity in the cell. A deep neural network machine learning algorithm is applied to establish the relationship between the TDR output value and the bulk electrical conductivity at each frequency of 0.1, 0.12, 1, 10, and 100 kHz. The highly important variables are also defined through random forest. This study demonstrates that the TDR can be reliably converted into bulk electrical conductivity when two different solutions are mixed.