A Neural Recording Amplifier Based on Adaptive SNR Optimization Technique for Long-Term Implantation

Long-term neural recording which can consistently provide good signal-to-noise ratio (SNR) performance over time is important for stable operation of neuroprosthetic systems. This paper presents an analysis for the SNR optimization in a changing environment which causes variations in the tissue-electrode impedance, Z(TE). Based on the analysis result, a neural recording amplifier (NRA) is developed employing the SNR optimization technique. The NRA can adaptively change its configuration for in situ SNR optimization. The SNR is improved by 4.69% to 23.33% as Z(TE) changes from 1.59 M Omega to 31.8 M Omega at 1 kHz. The NRA is fabricated in a 0.18-mu m standard CMOS process and operates at 1.8-V supply while consuming 1.6 mu A. It achieves an input-referred noise of 4.67 mu V-rms when integrated from 1 Hz to 10 kHz, which leads to the NEF of 2.27 and the (NEFVDD)-V-2 of 9.28. The frequency reponse is measured with a high-pass cutoff frequency of 1 Hz and a low-pass cutoff frequency of 10 kHz. The midband gain is set to 40 dB while occupying 0.11 mm(2) of a chip area.