A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry

Miniaturized and wireless near-infrared (NIR)-based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, the main challenge for the NIR-based neural recording integrated circuits (ICs) is to maintain robust operation in the presence of light-induced parasitic short-circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300
$\mu \text{W}$
/mm
²
of light exposure. It achieves the best-in-class power consumption of 0.57
$\mu \text{W}$
at 38 °C with a 4.1 noise efficiency factor (NEF) pseudo-resistor-less amplifier, an on- chip neural feature extractor, and individual mote-level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with a correlation coefficient up to 0.870 and 0.569, respectively, with individual mote-level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom photovoltaic (PV)/light-emitting diode (LED) GaAs chip wire bonded to the proposed IC.