A Convolutional Neural Network Based Calibration Scheme for Pipelined ADC

被引：7

作者：

Liu, Hang ^{[1
]}

Lu, Zhifei ^{[1
]}

Ye, Xiaolei ^{[1
]}

Xiao, Yao ^{[1
]}

Peng, Yutao ^{[1
]}

Zhang, Wei ^{[1
]}

Tang, Yong ^{[1
]}

Tang, He ^{[1
,2
]}

Peng, Xizhu ^{[1
,2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Integrated Circuit Sci & Engn, Chengdu, Peoples R China

[2] Univ Elect Sci & Technol China, Chongqing Inst Microelect Ind Technol, Chongqing, Peoples R China

来源：

2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS | 2023年

关键词：

Pipelined ADC; neural network; calibration;

D O I：

10.1109/ISCAS46773.2023.10181892

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a convolutional neural network (CNN) based error calibration scheme for pipelined ADC. The output of the pipelined ADC is taken as the input data of the network, and the network produces error compensation values. The network is applied in a 14-bit 1GSps pipelined ADC model with nonlinear errors including interstage gain error (IGE), DAC errors, thermal noise and sampling jitter for verification. The trained network scheme is verified with various types of signals including single-tone, dual-tone, amplitude modulation (AM) and frequency modulation (FM) signals. Simulation results show that, the SFDR and SNDR of the pipelined ADC are improved from 62.58dB and 58.82dB to 89.86dB and 66.66dB after calibration. Meanwhile, after calibration, the spurs of the dual-tone, AM and FM signals have been effectively suppressed.

引用

页数：5

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