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)

Hou Linjie; Liu Yvtao; Xie WeikunDai ZhijianYang WanyvZhao Yijiu

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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)

机译：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)

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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%.

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来源
《Review of Scientific Instruments》 |2022年第8期|084701-1-084701-8|共8页
作者
Hou Linjie; Liu Yvtao; Xie WeikunDai ZhijianYang WanyvZhao Yijiu;
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作者单位

Shenzhen Institute for Advanced Study, UESTC;

Testing Center The 58th Research Institute of China Electronics Technology Corporation;

School of Automation Engineering of University of Electronic and Technology of China;

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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)

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