A 1GSps RF sampling pipelined ADC with novel background digital calibration

Peng, Xizhu; Shi, Shuaishuai; Bao, Qingqing; Niu, S.; Tang, He

doi:10.1109/ieee-iws.2018.8400952

Cited by 2 publications

(1 citation statement)

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“…It does not require changing the internal circuit structure of the ADC, but corrects errors in the digital domain by analyzing the digital code output by the ADC. For example, for pipeline ADC, by injecting a pseudo-random sequences [4] , we can use LMS iterative operation to extract error parameters, and then adjust the ADC output digital domain according to the error parameters. However, this method relies on a large amount of data, which is not only poor in versatility but also inefficient.…”

Section: Introductionmentioning

confidence: 99%

Research on an accuracy improvement technology of ADC acquisition system based on pruning neural network

Fang,

Zhang,

Zhou

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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This paper introduces an algorithm for improving the accuracy of the ADC acquisition system based on pruning neural network, which can calibrate the errors caused by the analog-to-digital conversion module and other parts in the acquisition system, and effectively improve the accuracy of the ADC acquisition system. By using techniques such as neuron pruning, weight clustering, and parameter quantization, the network we trained greatly reduces hardware resource consumption while achieving the calibration effect of a fully connected neural network. It enables this network easier to deploy in embedded systems. The simulation results show that in the case of a signal input close to the Nyquist frequency, for a 12bit 12.5MS/s ADC acquisition system , the ENOB can be increased from 5.31 to 8.83, and the SFDR can be increased from 46.3dB to 66.4dB.

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Section: Introductionmentioning

confidence: 99%