Self-Adaptive Pseudo-Resistors Enabling Millisecond-Level Artifact Recovery and High-Linearity for Neural Recording Front-Ends

The therapeutic efficacy of closed-loop neuromodulation is critically undermined by stimulation artifacts that create a prolonged amplifier blind period', obscuring neural biomarkers. While state-of-the-art solutions mitigate this by adding complexity around the amplifier-such as active reset, blanking, or digital cancellation-they introduce trade-offs like data loss or computational overhead. In a distinct departure from these approaches, this paper solves the problem at its root by introducing a state-aware feedback element: a self-adaptive pseudo-resistor (A-PR). The A-PR architecture integrates two key innovations: an adaptive Floating Power Supply (FPS) that senses DC errors and autonomously collapses the feedback resistance for rapid recovery, and a process-insensitive Self-Biased Current Source (SBCS) that ensures robust, uniform performance against PVT variations. A complete neural recording front-end featuring the A-PR was fabricated in a 40-nm CMOS process. Measurement results validate the core claims, demonstrating a sub-3-ms recovery time from a 1-V artifact, an input-referred noise of 5.23 µV_rms, and a tunable high-pass corner, all while consuming only 2.3 µW and occupying 0.015 mm². By eliminating the trade-off between fast recovery and high fidelity, the A-PR provides a scalable, low-power solution for next-generation, high-resolution closed-loop neural interfaces.