Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs
The inherent non-ideal characteristics of circuit components and inter-channel mismatch errors induce nonlinear amplitude and phase distortions in time-interleaved pipelined analog-to-digital converters (TI-pipelined ADCs), significantly degrading system performance. Limited by prior modeling, conve...
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MDPI AG
2025-06-01
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| author | Yan Liu Mingyu Hao Hui Xu Xiang Gao Haiyong Zheng |
| author_facet | Yan Liu Mingyu Hao Hui Xu Xiang Gao Haiyong Zheng |
| author_sort | Yan Liu |
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| description | The inherent non-ideal characteristics of circuit components and inter-channel mismatch errors induce nonlinear amplitude and phase distortions in time-interleaved pipelined analog-to-digital converters (TI-pipelined ADCs), significantly degrading system performance. Limited by prior modeling, conventional digital calibration methods only correct partial errors, while machine learning (ML) approaches achieve comprehensive calibration at a high computational cost. This work proposes an ensemble calibration framework that combines polynomial modeling and ML techniques. The ensemble calibration framework employs a two-stage correction: a learned Volterra front-end performs forward mapping to compensate static baseline nonlinear distortions, while a lightweight neural network back-end implements inverse mapping to correct dynamic nonlinear distortions and inter-channel mismatch errors adaptively. Experiments conducted on TI-pipelined ADCs show improvements in both the spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratio (SNDR). It is noteworthy that in two ADCs fabricated using 40 nm CMOS technology, the 12-bit, 3000 MS/s silicon-validated four-channel TI-pipelined ADC exhibits SFDR and SNDR improvements from 35.47 dB and 35.35 dB to 79.70 dB and 55.63 dB, respectively, while the 16-bit, 1000 MS/s silicon-validated four-channel TI-pipelined ADC demonstrates an enhancement from 38.62 dB and 40.21 dB to 80.90 dB and 62.43 dB, respectively. Furthermore, a comparison with related studies reveals that our method achieves comprehensive calibration performance for wide-band inputs while substantially reducing computational complexity, requiring only 4.4 K parameters and 8.57 M floating-point operations per second (FLOPs). |
| format | Article |
| id | doaj-art-48c8dae76bb949eabe8dffec8751bcfa |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-48c8dae76bb949eabe8dffec8751bcfa2025-08-20T03:29:02ZengMDPI AGSensors1424-82202025-06-012513405910.3390/s25134059Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCsYan Liu0Mingyu Hao1Hui Xu2Xiang Gao3Haiyong Zheng4College of Electronic Engineering, Ocean University of China, Qingdao 266404, ChinaCollege of Electronic Engineering, Ocean University of China, Qingdao 266404, ChinaCollege of Electronic Engineering, Ocean University of China, Qingdao 266404, ChinaCollege of Electronic Engineering, Ocean University of China, Qingdao 266404, ChinaCollege of Electronic Engineering, Ocean University of China, Qingdao 266404, ChinaThe inherent non-ideal characteristics of circuit components and inter-channel mismatch errors induce nonlinear amplitude and phase distortions in time-interleaved pipelined analog-to-digital converters (TI-pipelined ADCs), significantly degrading system performance. Limited by prior modeling, conventional digital calibration methods only correct partial errors, while machine learning (ML) approaches achieve comprehensive calibration at a high computational cost. This work proposes an ensemble calibration framework that combines polynomial modeling and ML techniques. The ensemble calibration framework employs a two-stage correction: a learned Volterra front-end performs forward mapping to compensate static baseline nonlinear distortions, while a lightweight neural network back-end implements inverse mapping to correct dynamic nonlinear distortions and inter-channel mismatch errors adaptively. Experiments conducted on TI-pipelined ADCs show improvements in both the spurious-free dynamic range (SFDR) and signal-to-noise and distortion ratio (SNDR). It is noteworthy that in two ADCs fabricated using 40 nm CMOS technology, the 12-bit, 3000 MS/s silicon-validated four-channel TI-pipelined ADC exhibits SFDR and SNDR improvements from 35.47 dB and 35.35 dB to 79.70 dB and 55.63 dB, respectively, while the 16-bit, 1000 MS/s silicon-validated four-channel TI-pipelined ADC demonstrates an enhancement from 38.62 dB and 40.21 dB to 80.90 dB and 62.43 dB, respectively. Furthermore, a comparison with related studies reveals that our method achieves comprehensive calibration performance for wide-band inputs while substantially reducing computational complexity, requiring only 4.4 K parameters and 8.57 M floating-point operations per second (FLOPs).https://www.mdpi.com/1424-8220/25/13/4059TI-pipelined ADCcalibrationnonlinear distortionsmismatch errorsVolterra polynomialneural network |
| spellingShingle | Yan Liu Mingyu Hao Hui Xu Xiang Gao Haiyong Zheng Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs Sensors TI-pipelined ADC calibration nonlinear distortions mismatch errors Volterra polynomial neural network |
| title | Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs |
| title_full | Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs |
| title_fullStr | Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs |
| title_full_unstemmed | Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs |
| title_short | Ensembling a Learned Volterra Polynomial with a Neural Network for Joint Nonlinear Distortions and Mismatch Errors Calibration of Time-Interleaved Pipelined ADCs |
| title_sort | ensembling a learned volterra polynomial with a neural network for joint nonlinear distortions and mismatch errors calibration of time interleaved pipelined adcs |
| topic | TI-pipelined ADC calibration nonlinear distortions mismatch errors Volterra polynomial neural network |
| url | https://www.mdpi.com/1424-8220/25/13/4059 |
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