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A 13µW 87dB dynamic range implantable ΔΣ modulator for full-spectrum neural recording | IEEE Conference Publication | IEEE Xplore

A 13µW 87dB dynamic range implantable ΔΣ modulator for full-spectrum neural recording


Abstract:

Experiment analysis on in-vivo data sequences suggests a wide system dynamic range (DR) is required to simultaneously record local field potentials (LFPs), extra-cellular...Show More

Abstract:

Experiment analysis on in-vivo data sequences suggests a wide system dynamic range (DR) is required to simultaneously record local field potentials (LFPs), extra-cellular spikes, and artifacts/interferences. In this paper, we present a 13 μW 87 dB DR ΔΣ modulator for full-spectrum neural recording. To achieve a wide DR and low power consumption, a fully-differential topology is used with multi-bit (MB) quantization scheme and switched-opamp (SO) technique. By adopting a novel fully-clocked scheme, a power-efficient current-mirror SO is developed with 50% power saving, which doubles the figure-of-merit (FOM) over its counterpart. A new static power-less multi-bit quantizer with 96% power and 69% area reduction is also introduced. Besides, instead of metal-insulator-metal (MIM) capacitor, three high-density MOS capacitor (MOSCAP) structures are employed to reduce circuit area. Measurement results show a peak signal-to-noise and distortion ratio (SNDR) of 85 dB with 10 kHz bandwidth at 1.0 V supply, corresponding to an FOM of 45 fJ/conv.-step. which is implemented in a 0.18 μm CMOS.
Date of Conference: 03-07 July 2013
Date Added to IEEE Xplore: 26 September 2013
Electronic ISBN:978-1-4577-0216-7

ISSN Information:

PubMed ID: 24110300
Conference Location: Osaka, Japan
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Computation and Neural Systems, California Institute of Technology
Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Computation and Neural Systems, California Institute of Technology
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
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