Abstract

This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system.

Highlights

  • Recent advancements in neuropotential recording pave the way for observing and understanding the various neurophysiological disorders [1,2,3]

  • The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the analog-to-digital converter (ADC)

  • The successive approximation register analog-to-digital converter (SAR-ADC) featuring a simple architecture is suitable for a low-frequency neural signal recording system at a sampling rate of kHz order of magnitude

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Summary

Introduction

Recent advancements in neuropotential recording pave the way for observing and understanding the various neurophysiological disorders [1,2,3]. Developing a device with biomarker detection and controlling stimulation at a high spatial and temporal resolution, simultaneously recording from multiple sites, is imperative [6,7,8] This necessitates the design of a low-power neural signal recording system with a very small footprint [9]. Since a large number of recording sites would dissipate high power, the single-unit amplifier requires very high energy efficiency (noise efficiency factor (NEF) ∼1) [31,32]. Most of them suffer from oversampling data conversion compared to Nyquist-rate, large area, and high power consumption Considering all these limitations, SAR is the most widely used ADC architecture due to its high energy efficiency and modest resolution data conversion at a low sampling rate [46]. The SAR-ADC featuring a simple architecture is suitable for a low-frequency neural signal recording system at a sampling rate of kHz order of magnitude.

Amplifier Architecture
ADC Architecture
Charge-Scaling DAC
Comparator
Amplifier Characterization
ADC Characterization
Neural Signal Amplification and Digitization
Conclusions
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