A 15.2-ENOB Continuous-Time ΔΣ ADC for a 7.3μW 200mVpp-Linear-Input-Range Neural Recording Front-End

📋 论文概要

本文提出一个15.2-ENOB连续时间ΔΣ ADC，功耗仅7.3μW，与电容耦合斩波仪表放大器结合，构成一个能在200mVpp刺激伪影下记录1Hz-5kHz神经信号的神经记录前端，解决了闭环神经调控中刺激伪影导致传统前端饱和的问题。

💡 主要创新点

• 采用连续时间ΔΣ调制器架构，在极低功耗（7.3μW）下实现15.2位ENOB，FOM达到187dB，能效极高。
• 与8倍增益电容耦合斩波仪表放大器集成，使前端能耐受200mVpp线性输入范围内的伪影，同时保持对微弱神经信号（<2mVpp）的高精度采集。

🏷 关键词

📄 原文摘要

Closed-loop neuromodulation with simultaneous stimulation and sensing is desired to advance deep brain stimulation (DBS) therapies. However, stimulation generates large artifacts (~100mV) at the recording sites that saturate traditional front-ends. We present a 15.2b-ENOB CT ΔΣM with 187dB FOM, which along with an 8×-gain capacitively coupled chopper instrumentation amplifier (CCIA), realizes a front-end that can digitize neural signals (<2mVpp) from 1Hz to 5kHz in the presence of 200mVpp artifacts. Neural recording front-ends need to function within a power budget of 10μW/ch, input-referred noise of 4-8μVrms in 1Hz–5kHz, DC input impedance Zin,DC>1GΩ and high-pass (HP) cutoff <1Hz [1]. Prior work has addressed power and noise [1]-[2], but has limited dynamic-range and bandwidth (BW), making them incapable of performing true closed-loop operation. Prior art has digitized neural signals without amplification, with a VCO-ADC in [1],

👥 作者与机构

Hariprasad Chandrakumar, Dejan Marković

University of California, Los Angeles, CA