A ΔΣ-Modulation Feedforward Network for Non-Binary Analog-to-Digital Converters

Takao WAHO  Tomoaki KOIZUMI  Hitoshi HAYASHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E104-D   No.8   pp.1130-1137
Publication Date: 2021/08/01
Publicized: 2021/05/24
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020LOP0005
Type of Manuscript: Special Section PAPER (Special Section on Multiple-Valued Logic and VLSI Computing)
Category: Circuit Technologies
delta-sigma modulator,  A/D converter,  artificial neural network,  SNR,  noise shaping,  non-binary,  

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A feedforward (FF) network using ΔΣ modulators is investigated to implement a non-binary analog-to-digital (A/D) converter. Weighting coefficients in the network are determined to suppress the generation of quantization noise. A moving average is adopted to prevent the analog signal amplitude from increasing beyond the allowable input range of the modulators. The noise transfer function is derived and used to estimate the signal-to-noise ratio (SNR). The FF network output is a non-uniformly distributed multi-level signal, which results in a better SNR than a uniformly distributed one. Also, the effect of the characteristic mismatch in analog components on the SNR is analyzed. Our behavioral simulations show that the SNR is improved by more than 30 dB, or equivalently a bit resolution of 5 bits, compared with a conventional first-order ΔΣ modulator.