Channel error calibration of waveform digitization system based on machine learning

Jiao Xixiang; Han Te; Yang Jing; Guo Yuhui

doi:10.11884/HPLPB202537.240349

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Jiao Xixiang, Han Te, Yang Jing, et al. Channel error calibration of waveform digitization system based on machine learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202537.240349

Citation:

Jiao Xixiang, Han Te, Yang Jing, et al. Channel error calibration of waveform digitization system based on machine learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202537.240349

Jiao Xixiang, Han Te, Yang Jing, et al. Channel error calibration of waveform digitization system based on machine learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202537.240349

Citation:

Jiao Xixiang, Han Te, Yang Jing, et al. Channel error calibration of waveform digitization system based on machine learning[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202537.240349

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Channel error calibration of waveform digitization system based on machine learning

doi: 10.11884/HPLPB202537.240349

1.
School of Artificial Intelligence and Big Data, Hefei University, Hefei 230026, China
2.
Institute of modern physics, Chinese Academy of Sciences, Lanzhou 730000, China

Received Date: 2024-10-08
Accepted Date: 2025-01-24
Rev Recd Date: 2025-03-17

Available Online: 2025-04-07

Abstract

Abstract

In order to improve the performance of waveform digital readout systems based on Analog to Digital Converter (ADC) technology, this paper proposes a multi-channel mismatch error estimation calibration method. Used two domestically produced high-speed ADCs to form a Time-interleaved A/D Conversion (TIADC) system, and the estimation of channel mismatch error (Gain, Time-skew and Offset) can be obtained by integrating particle swarm optimization (PSO) algorithm and gradient descent (GD) method. Meanwhile, using filter equations and Kaiser window truncation to obtain compensation calibration filter coefficient values. This compensation method can be directly implemented on the TIADC hardware platform using Field-Programmable Gate Array(FPGA) as the central processing unit. Moreover this algorithm can achieve online reconstruction of sampling system data. The experimental results show that the algorithm can effectively compensate for channel mismatch errors, and using the behavior level simulation of Vivado development software, the spurious free dynamic range (SFDR) is increased from 32.1 dBFS to 53.1 dBFS, Improve the SFDR to 60.8 dBFS during hardware platform testing. Also this signal reconstruction method is easy to implement in hardware systems and is not limited by the number of channels. has high engineering applicability. This method has high engineering applicability and is simple and easy to implement.
- waveform digitization system,
- analog-to-digital,
- mismatch error calibration,
- time-interleaved A/D Conversion,
- machine learning algorithms,
- wideband signal

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References(18)

References

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