Search

Noise-shaping (NS) successive approximation register (SAR) analog-to-digital converters (ADCs) using passive integrators have drawn increasing attention owing to their simplicity and power efficiency. However, a capacitor array with a passive integrator result in a huge number of unit capacitors and power consumption. This paper presents a second-order fully passive NS-SAR ADC with a segmented capacitor array and a hybrid switching procedure. We use a most-significant-bit(MSB) sampling-segmented capacitor array to reduce the number of unit capacitors, and a common mode voltage(VCM)-based hybrid switching procedure to reduce the average switching power. Compared to fully passive NS-SAR ADCs with conventional capacitor array and switching procedure, the total number of unit capacitors is reduced by about 84%, and the average switching power is also reduced by about 97.8%. The prototype was fabricated using 180nm complementary metal oxide semiconductor(CMOS) technology, it only uses 478 unit capacitors, resulting in an area of 0.086 mm2. It consumes $16.5~\mu \text{W}$ , achieves 73.7 dB peak signal-to-noise-and-distortion ratio(SNDR), and a Schreier figure of merit(FoMs) of 165.5 dB.

To further improve the detection ability of residual current in low-voltage distribution networks, an adaptive residual current detection method based on variational mode decomposition (VMD) and dynamic fuzzy neural network (DFNN) is proposed. First, using the general $K$ -value selection method of VMD proposed in this study, the residual current signal is decomposed into $K$ intrinsic mode functions (IMFs). By introducing the cross-correlation coefficient $R$ and the time-domain energy entropy ratio $E$ as two classification indexes, IMFs are divided into three categories: effective IMFs, noise IMFs and aliasing IMFs. Then, the aliasing IMFs are denoised by recursive least squares (RLS), and the denoised IMFs are superimposed with the effective IMFs to obtain the reconstructed signal. Finally, the dynamic fuzzy neural network (DFNN) is adjusted by the minimum output method to achieve the detection of the reconstructed residual current signal, and the network is used to predict the residual current according to the detection results. The detection results of the simulation and measured data show that the proposed algorithm has high detection accuracy and is superior to the wavelet neural network, empirical mode decomposition-thresholding, and wavelet entropy-auto encoder-back propagation neural network methods in terms of mean square error, goodness of fit and running time. This method provides a reference for further research on new adaptive residual current protection devices.