| 刘胜剑,刘连胜,张益维,彭宇.FI-DAC峰值幅频非线性误差预校正方法[J].电子测量与仪器学报,2026,40(1):133-142 |
| FI-DAC峰值幅频非线性误差预校正方法 |
| Pre-correction of peak amplitude-frequency nonlinear distortions in FI-DAC |
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| DOI： |
| 中文关键词: 频率交织数模转换器 支持向量回归-局部加权学习 峰值非线性幅频预校准器 |
| 英文关键词:frequency interleaved digital-to-analog converter support vector regression-locally weighted learning peak nonlinear amplitude frequency precalibrator |
| 基金项目:黑龙江省重点研发项目（2023ZX01A13）资助 |
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| Author | Institution |
| Liu Shengjian | School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150000, China |
| Liu Liansheng | School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150000, China |
| Zhang Yiwei | School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150000, China |
| Peng Yu | School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150000, China |
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| 中文摘要: |
| 任意波形发生器（arbitrary waveform generator, AWG）的输出带宽受到数模转换器（digital to analog converter, DAC）模拟带宽的限制。频率交织数模转换器（frequency interleaved DAC, FI-DAC）能够有效实现带宽提升。然而，模拟器件的非理想特性和FI-DAC系统的分频特性将导致输出信号在边缘频带区域存在典型的峰值幅频误差，从而降低了输出信号的平坦性，严重影响系统的性能。因此，提出了一种针对FI-DAC系统的改进预校准器，专注于解决FI-DAC系统中峰值非线性幅频误差问题。首先，通过理论推导校准FI-DAC系统两通道线性相位误差；其次，该方法基于支持向量回归（support vector regression, SVR），通过构建精确的回归模型，设计预校准器以对幅频误差进行初步校准；然后，结合局部加权学习（locally weighted learning, LWL）方法，对频带边缘区域分配对应权重，从而更加精准地聚焦关键误差区域，进一步提升预校准器的设计效果和校准精度；最后，通过FI-DAC技术的应用，针对双路采样率为1.25 GSa/s的DAC，实现了850 MHz的输出带宽，提升了信号输出的频带范围。并且基于SVR-LWL算法设计的预校准器被集成到FI-DAC系统中，校正后系统输出信号幅频特性通带内的最小平坦度为-0.061 dB，最大平坦度为0.032 dB，接近理想平坦度0 dB。在5 GSa/s实验平台上进一步验证表明，基于SVR-LWL的预校准器在校正FI-DAC系统中峰值型幅频误差方面，相较其他算法表现出更高的精度与有效性。 |
| 英文摘要: |
| The output bandwidth of the arbitrary waveform generator (AWG) is limited by the bandwidth of the digital to analog converter (DAC). The frequency interleaved DAC (FI-DAC) could enhance bandwidth enhancement effectively. However, non-ideal characteristics of analog components cause peak amplitude frequency errors in the edge frequency bands of FI-DAC. These errors reduce the flatness of the output signal and degrade system performance. To address this issue, this article proposes an improved FI-DAC precalibrator that focuses on addressing the peak nonlinear amplitude frequency errors. Firstly, the linear phase error between the two channels of the FI-DAC is calibrated. Secondly, the algorithm utilizes support vector regression (SVR) to realize a precalibrator by formulating a regression model for the initial calibration of amplitude frequency errors. Thirdly, the algorithm integrates locally weighted learning (LWL) to assign adaptive weights to the edge frequency band. Finally, with a single-channel DAC sampling rate of 1.25 GSa/s, the application of the FI-DAC achieves an output bandwidth of 850 MHz, improving the signal output frequency range. Experimental results show that the minimum flatness within the passband is -0.061 dB, and the maximum flatness is 0.032 dB, which is close to the ideal flatness of 0 dB. Further validation is conducted on the 5 GSa/s experimental platform. Compared with other algorithms, the SVR-LWL precalibrator achieves higher accuracy in calibrating peak amplitude frequency errors in the FI-DAC. |
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