Title
A Wide-Dynamic-Range Neural-Recording IC With Automatic-Gain-Controlled AFE and CT Dynamic-Zoom ΔΣ ADC for Saturation-Free Closed-Loop Neural Interfaces
Abstract
This article presents a neural-recording IC with automatic gain control (AGC) according to the input signal level. AGC enhances the dynamic range (DR) of the recording IC by more than 30 dB and allows it to take the benefits of the front-end amplification-based and direct-conversion-based recording structures concurrently. By adaptively controlling the analog front-end (AFE) gain, the input-referred noise (IRN) of the overall system is greatly reduced while ensuring a wide DR. A continuous-time (CT) dynamic-zoom <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> ADC (CT-Zoom-ADC) is used for power-efficient two-step conversion. The coarse conversion output is reused for AGC, and the fine conversion resolution is adjusted adaptively by modifying the oversampling ratio according to the varying AFE gain. The presented neural-recording IC achieves 99.5-dB DR and 6.1- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{V}_{\textrm {rms}}$ </tex-math></inline-formula> IRN over 5-kHz bandwidth, resulting in FoM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR</sub> of 185.2 dB, the effective number of bits (ENOB) of 11.4 bits, and tolerance against artifacts with differential voltage amplitudes up to 1.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{V}_{\text {pp}}$ </tex-math></inline-formula> . Measurements with pulsatile artifacts and experiments <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> validate that the proposed IC is applicable to the closed-loop neural interface.
Year
DOI
Venue
2022
10.1109/JSSC.2022.3188626
IEEE Journal of Solid-State Circuits
Keywords
DocType
Volume
Artifact recovery,automatic gain control (AGC),bidirectional neural interface,closed-loop neuromodulation,continuous-time (CT) ΔΣ modulator (ΔΣM),digital auto-ranging (DAR),dynamic-zoom ADC,neural-recording,wide dynamic range (DR)
Journal
57
Issue
ISSN
Citations 
10
0018-9200
0
PageRank 
References 
Authors
0.34
24
9
Name
Order
Citations
PageRank
Yoontae Jung182.16
Soon-Jae Kweon200.68
Hyuntak Jeon300.34
Injun Choi481.48
Jimin Koo500.34
Mi Kyung Kim600.34
Hyun Joo Lee7299.68
Sohmyung Ha801.01
Minkyu Je933358.17