A Wide-Dynamic-Range Neural-Recording IC With Automatic-Gain-Controlled AFE and CT Dynamic-Zoom <inline-formula> <tex-math notation="LaTeX">$\Delta\Sigma$</tex-math> </inline-formula> ADC for Saturation-Free Closed-Loop Neural Interfaces

Yoontae Jung, Soon Jae Kweon, Hyuntak Jeon, Injun Choi, Jimin Koo, Mi Kyung Kim, Hyunjoo Jenny Lee, Sohmyung Ha, Minkyu Je

Research output: Contribution to journalArticlepeer-review

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> <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> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula>V<inline-formula> <tex-math notation="LaTeX">$_\textrm{rms}$</tex-math> </inline-formula> IRN over 5-kHz bandwidth, resulting in FoM<inline-formula> <tex-math notation="LaTeX">$_\textrm{DR}$</tex-math> </inline-formula> 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 V<inline-formula> <tex-math notation="LaTeX">$_{\text{pp}}$</tex-math> </inline-formula>. Measurements with pulsatile artifacts and experiments <italic>in vivo</italic> validate that the proposed IC is applicable to the closed-loop neural interface.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Journal of Solid-State Circuits
DOIs
StateAccepted/In press - 2022

Keywords

  • Artifact recovery
  • automatic gain control (AGC)
  • bidirectional neural interface
  • closed-loop neuromodulation
  • continuous-time (CT) <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$\Delta\Sigma$</tex-math> </inline-formula> modulator (<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$\Delta\Sigma$</tex-math> </inline-formula>M)
  • digital auto-ranging (DAR)
  • dynamic-zoom ADC
  • Electronics packaging
  • Gain
  • Gain control
  • Integrated circuits
  • neural-recording
  • Power demand
  • Recording
  • Signal to noise ratio
  • wide dynamic range (DR)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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