Abstract
In this brief, a high-resolution successive-approximation-register analog-to-digital-conversion architecture for biomedical data acquisition is proposed. A filtered least-significant-bit segment is employed as a dither to improve the resolution. Theoretical analysis and behavioral simulations show that the error of a most-significant-bit segment can be converted into shaped noise if the input signal is sufficiently small. The proposed self-dithering technique can be used, together with averaging, to improve the signal-to-noise ratio and the differential nonlinearity (DNL) performance. The performance improvement is similar to that of a conventional nonsubtractive scheme using a uniform deterministic dither but with simplified hardware and reduced computation complexity.
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