Analysis and Design of VCO-Based Neural Front-End With Mixed Domain Level-Crossing for Fast Artifact Recovery

neural signal instrumentation withstanding neural stimulation artifacts is essential for bi-directional neural interfaces to guarantee signal integrity. In this work, different front-end structures and stimulation artifact mitigation techniques are firstly reviewed to benchmark their step response speed. Then, a mixed domain level-crossing scheme is proposed to achieve fast dynamic response with minimized hardware overhead. The benefit of extending the phase detection range of the phase detectors in VCO-based continuous time delta sigma modulators is investigated with stability and noise consideration. Then a shift-register-based phase counter is proposed to extend the phase detectors's detection range, thereby increase quantization resolution and stability margin for in-band noise optimization. The proposed VCO-based neural front-end was fabricated in a 180 nm CMOS process. The prototype achieves 6.38 mu Vrms input-referred noise over 0.5 Hz-10 kHz bandwidth. With a linear input range of 120 mVpp, it exhibits a SNDR of 71.6 dB and a DR of 77.0 dB, which could be further extended up to 100 dB in the artifact adaption mode. Measurements verify that the proposed neural front-end can recover from rail-to-rail differential mode or common mode artifacts within 10 mu s (minimum 6.25 mu s) while the superposed small signal can be recorded uninterruptedly.

Automated summary

This paper reviews the front-end structures and stimulation artifact mitigation techniques for neural signal instrumentation, and proposes a mixed domain level-crossing scheme for fast dynamic response. By extending the detection range of the phase detectors and introducing a phase counter, the noise optimization performance of the neural front-end is improved.