A Neural-Recording 0.2-V VCO-based ADC with Machine-Learning-Programmable Coupled Oscillator Ensembles

Future energy-harvesting (EH) brain-machine interfaces (BMI) present fundamental design challenges to analog-to-digital converters (ADC) for neural signal digitisation and recording. Ultra-low power (ULP) consumption, maximised area density, bandwidth and dynamic range as well as amenability to ultra-low voltage (ULV) supply are all desirable performance specifications. This article presents a digitally intensive openloop voltage-controlled-oscillator (VCO)-based ADC operating at an ULV supply of only 0.2V in 28nm CMOS. We introduce a novel design framework harnessing the spatio-temporal dynamics of coupled oscillator ensembles (COE) for open-loop voltage-to-frequency (V -to-f) analog linearisation, as applied to high impedance input transconductor-driven VCOs. A machine learning (ML)-driven foreground calibration engine employing gradient descent automatically optimises the integrated digitally programmable ‘tuning knobs’ of the COE network to guide its self-adaptive linearisation. A Verilog-A calibration engine integrated with the transistor level COE was developed to perform full-system transient simulations (lasting several miliseconds) which reduced the dominant third-order harmonic distortion (HD3) from −35dBc to −70dBc for a near rail-to-rail differential input voltage swing of ±180mV, verifying the calibration process.

I. Introduction

Brain-machine interfaces (BMI) are pivotal for the meaningful progression of technology-driven neurological research and treatment. Continuous, unrestrained, real-time monitoring of large-scale brain neural activity supports deeper clinical understanding and the consequent treatment of neurological diseases [1], as well as the development of neuro-prostheses for restoring mobility to paralysed patients [2]. A fully integrated BMI system-on-chip (SoC) that can sense, digitize and record neural signals spanning several brain regions, with high bandwidth (via 10,000–100,000 of electrode channels [3]), maximal energy autonomy (i.e., power provided through energy harvesting solutions, allowing battery-less operation) and made implantable (requiring wireless connectivity) currently poses significant engineering challenges.