An Ultra Compact Neural Front-End with CT-NEO Based Spike Detection for Implantable Applications

High-density large-scale implantable electrode arrays enable neural recording with cellular level resolution. Active electrodes with integrated instrumentation and processing are necessary for single neuron activity recording to minimize interference and reduce bandwidth for the signal conversion and transmission. This introduces design challenges for circuits miniaturization to match the electrode density and meet the dynamic range requirement. This paper proposes a clockless, area efficient spike detection neural front-end enabled by a VCO-based level-crossing analog to digital converter (LC-ADC) and spike detection algorithm based on continuous time nonlinear energy operator (CT-NEO). Level-crossing events and corresponding timing information can be directly generated by the proposed LC-ADC. A continuous time NEO algorithm is proposed and implemented, featuring low complexity and high detection accuracy, MATLAB simulation shows that the algorithm can still achieve 70% accuracy at an estimated signal-to-noise ratio (SNR) of -4dB. Implemented in a 65 nm CMOS process, the proposed front-end consumes 3.15 $\mu$W and 0.00385 mm² active area.