4.8 Article

SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings

Journal

BIOSENSORS & BIOELECTRONICS
Volume 126, Issue -, Pages 355-364

Publisher

ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.bios.2018.10.032

Keywords

CMOS-probe; Multi-electrode-array; Active pixel sensor; Implantable device; Neural recording

Funding

  1. National Institutes of Health (NIH, USA) [1U01NS094190-01]

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Large-scale neural recordings with high spatial and temporal accuracy are instrumental to understand how the brain works. To this end, it is of key importance to develop probes that can be conveniently scaled up to a high number of recording channels. Despite recent achievements in complementary metal-oxide semiconductor (CMOS) multi-electrode arrays probes, in current circuit architectures an increase in the number of simultaneously recording channels would significantly increase the total chip area. A promising approach for overcoming this scaling issue consists in the use of the modular Active Pixel Sensor (APS) concept, in which a small front-end circuit is located beneath each electrode. However, this approach imposes challenging constraints on the area of the in-pixel circuit, power consumption and noise. Here, we present an APS CMOS-probe technology for Simultaneous Neural recording that successfully addresses all these issues for whole-array read-outs at 25 kHz/channel from up to 1024 electrode-pixels. To assess the circuit performances, we realized in a 0.18 mu m CMOS technology an implantable single-shaft probe with a regular array of 512 electrode-pixels with a pitch of 28 mu m. Extensive bench tests showed an in-pixel gain of 45.4 +/- 0.4 dB (low pass, F-3 db = 4 kHz), an input referred noise of 73 +/- 0.67 mu V-RMS (300 Hz to 7.5 kHz) and a power consumption < 6 mu W/pixel. In vivo acute recordings demonstrate that our SiNAPS CMOS-probe can sample full-band bioelectrical signals from each electrode, with the ability to resolve and discriminate activity from several packed neurons both at the spatial and temporal scale. These results pave the way to new generations of compact and scalable active single/multi-shaft brain recording systems.

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