将一个学习的沃尔特拉多项式与一个神经网络组合在一起,用于联合非线性扭曲和不匹配错误,对时间间隔管道ADC的校准
Yan Liu1, Mingyu Hao1, Hui Xu1
1College of Electronic Engineering, Ocean University of China, Qingdao 266404, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了时间间隔管道ADC的集成校准框架,结合多项式建模和机器学习来纠正扭曲和不匹配,显著提高性能,降低计算成本.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 模拟到数字的转换
背景情况:
- 不理想的电路组件和通道间不匹配导致时间间隔管道ADC的非线性扭曲,降低了性能.
- 现有的数字校准方法提供部分错误校正,而机器学习方法则需要高额的计算成本.
- 对于TI-pipelined ADCs,存在对有效和计算效率高的校准技术的需求.
研究的目的:
- 为 TI 管道 ADCs 提出一个新的整体校准框架.
- 实现对非线性扭曲和道间不匹配的全面纠正.
- 为了减少校准方法的计算复杂性.
主要方法:
- 开发了一个双阶段的集体校准框架,将多项式建模和机器学习结合起来.
- 采用一个熟悉的Volterra前端来进行静态非线性扭曲补偿 (前向映射).
- 使用轻量级的神经网络后端进行自适应动态扭曲和不匹配校正 (反向映射).
主要成果:
- 在非假动态范围 (SFDR) 和信号对噪声和扭曲比率 (SNDR) 中显著改进,用于验证的TI管道ADC.
- 实现了SFDR/SNDR的改进,从35.47 dB/35.35 dB升至79.70 dB/55.63 dB (12位,3000 ms/s) 和38.62 dB/40.21 dB升至80.90 dB/62.43 dB (16位,1000 ms/s).
- 实现了全面的校准,并大大降低了计算复杂度 (4.4K参数,8.57M FLOPs/s).
结论:
- 拟议的整体校准框架有效地弥补了TI管道ADC中的非线性扭曲和通道间不匹配.
- 与传统技术相比,该方法提供了更高的性能,特别是在宽带输入方面.
- 该框架为高性能ADC校准提供了一个计算效率高的解决方案.
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