Circuits-Informed Machine Learning Technique for Blind Open-Loop Digital Calibration of SAR ADC
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929638053249024 |
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| author | Bhanushali, Sumukh Maiti, Debnath Bikkina, Phaneendra Mikkola, Esko Sanyal, Arindam |
| author_facet | Bhanushali, Sumukh Maiti, Debnath Bikkina, Phaneendra Mikkola, Esko Sanyal, Arindam |
| contents | This work presents a supervised machine-learning (ML) approach for blind digital calibration of SAR ADCs without requiring prior knowledge of errors. A low-speed reference ADC is used to train a shallow neural network (NN) to estimate errors in a high-speed ADC by comparing the outputs of the ADCs when their sampling instants align and subtracting these errors in the back-end. The proposed NN-calibration improves SFDR of a 28nm, 12-bit, 84MHz ADC by >38dB while consuming 25.8fJ/conversion-step. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Circuits-Informed Machine Learning Technique for Blind Open-Loop Digital Calibration of SAR ADC Bhanushali, Sumukh Maiti, Debnath Bikkina, Phaneendra Mikkola, Esko Sanyal, Arindam Signal Processing This work presents a supervised machine-learning (ML) approach for blind digital calibration of SAR ADCs without requiring prior knowledge of errors. A low-speed reference ADC is used to train a shallow neural network (NN) to estimate errors in a high-speed ADC by comparing the outputs of the ADCs when their sampling instants align and subtracting these errors in the back-end. The proposed NN-calibration improves SFDR of a 28nm, 12-bit, 84MHz ADC by >38dB while consuming 25.8fJ/conversion-step. |
| title | Circuits-Informed Machine Learning Technique for Blind Open-Loop Digital Calibration of SAR ADC |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.14051 |