S-parameters are widely used to detail the scattering parameters of radio frequency (RF) components and microwave circuit modules. The vector network analyzer (VNA) is the most commonly used device for measuring S-parameters. Given the multiple frequency points, complex values, and intricate uncertainty propagation involved, accurately assessing the uncertainty of S-parameter measurements is difficult. In this study, we proposed a new method for assessing S-parameter uncertainty based on the covariance matrices, tracing back to the nominal uncertainty of calibration standards. First, we analyzed the relevant theory of uncertainty assessment using covariance matrices and subsequently deduced the mechanism of Type B uncertainty propagating from calibration standards to error model coefficients and S-parameter measurements to evaluate Type B measurement uncertainty. In this study, a novel measurement system was constructed for measuring grounded coplanar waveguides by using a VNA and calibration standards with 8- and 12-error models. Initially, the model assessed the Type B uncertainty of measuring four S-parameters of a grounded coplanar waveguide. Next, the VNA calibrated with the 12-error model was used to conduct multiple repeated measurements to assess the Type A uncertainty of the grounded coplanar waveguide. Finally, the composite uncertainty was constructed, which demonstrated that the proposed method can be used for assessing the uncertainty of S-parameters.

A new prediction algorithm based on Empirical model decomposition (EMD) and Support vector machine (SVM) is put forward in this paper, and this algorithm solves the problem of the hydrogen atomic clock differences prediction, which is affected by the non-linearity and non-stability. The clock differences were decomposed into Intrinsic mode functions (IMF) and the residual series. The suitable kernel function and parameters were chosen to build the different SVM for predicting each IMF and the residual series. Each prediction result was summed to obtain the clock differences prediction. Results show that the EMD-SVM algorithm is effective compared with the linear regression and single SVM. The relative prediction error is reduced from 0.4327% to 0.2371%, and the dispersion is less than other methods.

This study proposes a method to quantify uncertainty in the scattering parameter (S-parameter) measurements when using de-embedding techniques. After calibrating the measurement setup with reference standards, de-embedding algorithms are employed to extract the intrinsic S-parameter of the device under test (DUT). This process introduces additional complexity to the uncertainty analysis. This study investigates the sources of uncertainty inherent to vector network analyzer (VNA) measurements. Subsequently, a covariance matrix-based approach is employed to propagate these uncertainties, culminating in the quantification of S-parameter uncertainty. The effectiveness of the proposed is determined by comparing the measured S-parameters of power dividers and couplers to their nominal values, considering parameters such as balance, coupling, and voltage standing wave ratio (VSWR). Additionally, an uncertainty analysis is conducted for the power divider’s S-parameters, tracing the uncertainty sources back to the calibration standards.

A grounded coplanar waveguide (GCPW), as a millimeter wave special transmission line, can be used to calibrate broadband oscilloscope probes. A method to change the through-hole structure to widen the GCPW is investigated in this paper. The effect of the through-hole array on the band-width of the GCPW is investigated and verified using COMSOL Multiphysics simulation software. Finally, the S-parameters of the fabricated GCPWs are measured by a vector network analyzer, and the results show that they have an insertion loss > −3 dB and return loss < −10 dB in the frequency range of DC to 60 GHz, which satisfies the design requirements.

The atomic clock prediction is the key step for constructing atomic clock time scales and implementing atomic clocks steering, and its prediction level directly affects the accuracy and stability of atomic clock time scales. Aiming at the non‐linear and non‐stationary characteristics of the hydrogen atom clock, the least squares support vector machine (LSSVM) hydrogen clock prediction algorithm is proposed, which is verified by the actual hydrogen atomic clock data of the timekeeping laboratory in National Institute of Metrology, China. The results show that compared with the linear prediction algorithm and the support vector machine (SVM) prediction algorithm, the least squares SVM prediction algorithm improves the accuracy of the clock prediction, and its root mean square error is, respectively, reduced by 50% and 29%.

Accurate characterization of picosecond broadband pulse signals is important for radio-frequency electronics, radar detection and high-speed communications. However, bandwidth-limited oscilloscopes attenuate high-frequency components, broaden rising edges, reduce peak amplitudes and introduce ringing distortion. Conventional regularized deconvolution relies heavily on manual parameter tuning and is vulnerable to noise, while Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models suffer from long training time and insufficient capability to capture global temporal dependencies. Here we present a waveform reconstruction method integrating convolutional neural networks, Transformer and learnable physics-guided deconvolution constraints. The model simultaneously extracts local waveform details and models global temporal correlations, which improves training efficiency and noise robustness, and restores pulse rising edges, peak amplitudes and overall waveform morphology with higher precision. The proposed method offers a low-cost software solution to realize high-precision measurement of high-speed pulse signals without hardware upgrades.