A Neural Network-Based Harmonic Suppression Algorithm for Medium-to-High Resolution ADCs

被引：2

作者：

Peng, Xizhu ^{[1
]}

Mi, Yihang ^{[1
]}

Zhang, Yunfan ^{[1
]}

Xiao, Yao ^{[1
]}

Zhang, Wei ^{[1
]}

Tang, Yong ^{[1
]}

Tang, He ^{[1
,2
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Guangdong Inst Elect Informat Engn, Chengdu, Sichuan, Peoples R China

来源：

2021 5TH IEEE ELECTRON DEVICES TECHNOLOGY & MANUFACTURING CONFERENCE (EDTM) | 2021年

基金：

中国国家自然科学基金;

关键词：

high precision ADC; harmonic distortion; digital calibration; neural network;

D O I：

10.1109/EDTM50988.2021.9420899

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a novel high-precision ADC digital calibration algorithm based on neural network (NN). The algorithm is to suppress the harmonic distortion caused by the non-ideal factors in ADC. As a result, regardless of the non-ideal types, it can be universally used as a back-end calibration black box in various ADC structures. A 14-bit ADC with trained NN reaches more than 12.3-bit of ENOB in behavioral simulation. We also applied the NN to a practical 12-bit sigma-delta ADC and achieved a significant improvement of more than 1-bit of ENOB.

引用

页数：3

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