Sub- $\mu$ Vrms-Noise Sub- $\mu$ W/Channel ADC-Direct Neural Recording With 200-mV/ms Transient Recovery Through Predictive Digital Autoranging

Abstract

Integrated recording of neural electrical potentials from the brain poses great challenges due to stringent dynamic range requirements to resolve small-signal amplitudes buried in noise amidst large artifact and stimulation transients, as well as stringent power and volume constraints to enable minimally invasive untethered operation. Here, we present a 16-channel neural recording system-on-chip with greater than 90-dB input dynamic range and less than 1-μVrms input-referred noise from dc to 500 Hz, at 0.8-μW power consumption, and 0.024-mm2area per channel in a 65-nm CMOS process. Each recording channel features a hybrid analog-digital second-order oversampling analog-to-digital converter (ADC), with the biopotential signal coupling directly to the second integrator for high conversion gain and dynamic offset subtraction in the digital domain. This bypasses the need for high-pass filtering pre-amplification in neural recording systems, which often leads to signal distortion. The integrated ADC-direct neural recording offers record figureof-merit with a noise efficiency factor (NEF) of the combined front end and ADC of 1.81, and a corresponding power efficiency factor (PEF) of 2.6. Predictive digital autoranging of the binary quantizer further supports rapid transient recovery while maintaining fully dc-coupled operation. Hence, the neural ADC is capable of recording ≤ 0.01-Hz slow potentials as well as recovering from ≥ 200-mVpp transients within ≤ 1 ms that are important prerequisites to effective electrocortical recording for brain activity mapping. In vivo recordings from marmoset primate frontal cortex demonstrate its unique capabilities in resolving ultra-slow local field potentials indicative of subject arousal state.