Closed-loop neural recording requires a front-end with a wide DR to record small neural signals without distortion in the presence of a DC electrode offset (50mV) and a large stimulation artifact ( 200mVpp). To remove DC offset, a conventional architecture uses an AC-coupled LNA and a subsequent ADC . However, to realize a small HPF cut-off frequency (<1Hz), the LNA requires a large input capacitor, resulting in a reduced input impedance and an increased area. Additionally, the LNA is prone to be saturated by large stimulation transients. To address these issues, direct digitization for neural recording is increasingly popular ,  and offers great potential for reducing area and power consumption . However, to digitize small neural signals without amplification, such ADCs require a high DR (>80dB), a large linear operating range (>250mV), a high DC input impedance (>1GΩ), and a large common-mode rejection (>70dB). Fulfilling all these requirements often leads to ADCs with poor energy-efficiency , . This paper presents a continuous-time delta-sigma modulator (CT-ALM) with Gm -input for closed-loop neural recording. It achieves a high input impedance, 300mVpp linear input range, 80.4dB SNDR, and 76dB CMRR, and consumes only 6.5μW with a signal bandwidth of 10kHz. This corresponds to a 172.3dB FOM.
|Title of host publication||2020 IEEE International Solid-State Circuits Conference, ISSCC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||3|
|Publication status||Published - 2020 Feb|
|Event||2020 IEEE International Solid-State Circuits Conference, ISSCC 2020 - San Francisco, United States|
Duration: 2020 Feb 16 → 2020 Feb 20
|Name||Digest of Technical Papers - IEEE International Solid-State Circuits Conference|
|Conference||2020 IEEE International Solid-State Circuits Conference, ISSCC 2020|
|Period||20/2/16 → 20/2/20|
Bibliographical noteFunding Information:
This work is supported by the Samsung Research Funding & Incubation Center of Samsung Electronics (SRFC-IT1701-08) and the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2019M3C1B8077565).
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering