You, Hao, Xu, Jianxiong, Amirsoleimani, Amirali, Rahimi Azghadi, Mostafa, and Genov, Roman (2023) MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals. In: Proceedings of the IEEE Biomedical Circuits and Systems Conference, Conference Proceedings. From: BioCAS 2023: IEEE Biomedical Circuits and Systems, 19-21 October 2023, Toronto, Canada.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

In this study, we present an analog-to-digital converter (ADC) optimized for implantable neural interfaces. The proposed ADC integrates a series of memristors in both the input and feedback Digital-to-Analog Converter (DAC), significantly boosting the input impedance and making it suitable for neural interfaces. A defining feature of the ADC is the ability of the memristor resistance to adapt to various conditions such as large DC offset, motion, and stimulation artifacts. The model was simulated using 65nm MOSFET technology along with a physical memristor model, yielding an impressive signal-to-noise-and-distortion ratio (SNDR) of 62.7dB and a substantial Nyquist sampling rate of 50kHz. Power consumption is remarkably low, with less than nW for integrators, 5μW for the comparator, and 0.45 μW for the feedback DAC - a key requirement for neural interfaces implanted in the brain. The ADC demonstrates strong resilience against component mismatch, maintaining circuit stability even in variable conditions. Through its ability to adjust input resistance, the ADC can enhance its SNDR. This adaptive and robust ADC design shows promising potential for implantable neural interface applications.

Actions (Repository Staff Only)

Item Control Page