A Neural Network Based Calibration Technique for TI-ADCs with Derivative Information

被引：3
作者：
Peng, Xizhu [1 ,2 ]
Ye, Xiaolei [1 ]
Liu, Hang [1 ]
Lu, Zhifei [1 ]
Xiao, Yao [1 ]
Peng, Yutao [1 ]
Tang, He [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：
Time-Interleaved Analog-to-Digital Converter; Neural Network; Inter-Channel Mismatch; Calibration;
D O I：
10.1109/ISCAS46773.2023.10181825
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 demonstrates a new neural-network-based calibration technique for inter-channel mismatches of time-interleaved ADCs. By providing with signal value and derivative value of each channel, the network could calibrate the gain mismatch, offset mismatch, and timing mismatch of TI-ADCs. By utilizing signal feature fitting, the ground truth for network training could be obtained without an accurate reference ADC nor a precise ADC error model. Simulation results show that the proposed calibration technique can increase the SFDR of a 14-bit 4Gsps TI-ADC from 32.77 dB to 91.71 dB for single-tone signals, and suppress the maximum spur from -48.51 dBFS to -101.23 dBFS for multi-tone signals. A hardware implementation resources estimation is also given in this paper.
引用
收藏
页数：5
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