Statistical neural network (SNN) for predicting signal-to-noise ratio (SNR) from static parameters and its validation in 16-bit, 125-MSPS analog-to-digital converters (ADCs).

In the analog-to-digital converter (ADC) test process, the static and dynamic performance parameters are the most important, and the tests for these parameters account for the bulk of the ADC test cost. These two types of parameters follow certain relationships, which are incorporated into the ADC test to reduce the cost. In this paper, we focus on the signal-to-noise ratio (SNR), a key indicator of the dynamic performances of ADCs. A statistical neural network (SNN) with two hidden layers was constructed to predict the SNR from the feature variables, which were extracted from the static parameters. A 16-bit, 125-MSPS ADC was used to evaluate the proposed prediction model. Compared to the measured SNR obtained by traditional fast Fourier transform based test methods, the predicted value had a mean average error of only 0.75 dB. In addition, the Shapley additive explanations interpreter was adopted to analyze the feature dependences of the SNN model, and the results demonstrated that the deterioration of the integral nonlinearity-curve-related features could significantly decrease the SNR, which is consistent with previous research results. The reported results demonstrated that, at the cost of a slight loss of accuracy, the proposed SNN can significantly reduce the test complexity, avoid dynamic parameter measurements, and reduce the total test time by about 4%.

Eco-Friendly Ferrimagnetic-Humic Acid Nanocomposites as Superior Magnetic Adsorbents.

Recyclable, cheap, eco-friendly, and efficient adsorbent materials are very important for the removal of pollution. In this work, we report the design and implementation of ferrimagnetic-humic acid nanocomposites as superior magnetic adsorbent for heavy metals. Ferrimagnetic and ferrimagnetic-humic acid nanocomposite particles with different morphologies were prepared using the coprecipitation method and hydrothermal synthesis method, respectively. The results show that the morphology of the nanoparticles prepared by the coprecipitation method is more uniform and the size is smaller than that by the hydrothermal synthesis method. Adsorption experiments show that the ferrimagnetic-humic acid nanoparticles prepared by the coprecipitation method has high sorption capacity for cadmium, and the maximum adsorption capacity is about 763 µg/g. At the same time, magnetic technology can be used to realize the recycling of ferrimagnetic-humic acid adsorbents.