A Fast Mismatch Calibration Method Based on Frequency Domain Orthogonal Decomposition for Time-Interleaved Analog-to-Digital Converters

Abstract

This paper proposes a fully digital background calibration method for time-interleaved analog-to-digital converter (TIADC) mismatches. The method analyzes the frequency and phase of spurious signals caused by three types of mismatches in TIADCs in the frequency domain. By utilizing the Hilbert transform and frequency shifting, orthogonal basis signals located at the mismatch frequencies can be constructed. The calibration of mismatches is achieved by linearly combining the orthogonal basis signals with the estimated coefficients and subtracting them from the original signal. The estimation of coefficients is determined by evaluating the correlation between the linear combination of orthogonal basis signals and the calibrated signal. Furthermore, an exponential moving average (EMA) and least mean square (LMS) algorithm are introduced to expedite the coefficient estimation process. The entire calibration process converges in merely 600 samples, significantly improving the convergence speed. By monitoring the amplitude of the input signal and adjusting the LMS step, the algorithm is functional under different amplitude signals, enhancing the robustness. An off-chip calibration is conducted based on a commercial 14-bit, 8-channel, 2.4GSPS TIADC. Results indicate that all spurious signals are suppressed below 80 dB, and the convergence rate is consistent with the simulation.

An All-Digital Timing Mismatch Calibration Algorithm Based on Reference Channel for TIADC

“…Relative to the calibration methodologies that have been recently reported by our group, the algorithm in this article shows significant advantages [30,31]. In contrast to the approach in Ref.…”

Estimation of an Extent of Sinusoidal Voltage Waveform Distortion Using Parametric and Nonparametric Multiple-Hypothesis Sequential Testing in Devices for Automatic Control of Power Quality Indices

“…The essence of this phenomenon is the distribution of power of spectral components over the analyzed frequency range. This effect causes new frequencies that are not in the original signal, that is, the powers of real frequencies "leak" into other ranges [30]. For this reason, DFT (FFT) is not practical for analyzing non-stationary signals with PQI deviation.…”

“…The latter transmits the minimum value of the distances d min (expression (29)) to the input of comparison circuit 7. The threshold value A m , obtained from expression (30) is received from memory unit 9 at the other input of comparison circuit 7(n). The condition d min ≤ A m (n) is checked, and if it is met, the multiple-hypothesis sequential testing procedure ends.…”

“…where λ n m (m, q) is the threshold determined by expression (30), and r is a positive constant. According to [69], the calculations using expression (31) with r = 1 significantly reduce the average number of required observations and have virtually no effect on the classifier error probabilities.…”

“…Let us calculate the threshold values using expression (31) by specifying the probability matrix P to determine the probability of errors and correct decisions when classifying distortions of the sinusoidal voltage waveform of a three-phase system involved in expression (30). For example, given the total number of distortion options M = 5, matrix P is set in the form:…”