Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2412.14051 |
| Tags: |
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Inhaltsangabe:
- 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.