Accounting for Memristor I-V Non-linearity in Low Power Memristive Amplifiers

Detecting neuronal activity for rehabilitation/assistive devices is an example of extreme edge computing, featuring stringent requirements for data bandwidth from implantable acquisition system, low-power consumption and ideally also low latency. The proposed neural recording system which detects neural spikes directly on the signals collected from electrophysiological probes. The system achieves power efficiency by utilizing a combination of integrative sensing and ultra-fine offset compensation. A central component of this design is a memristive load, which is utilised as a trimming device along the differential branches of the core amplifier, ultimately allowing system offset tuning with µV precision. In this paper, we study the impact of memristor IV non-linearity on the effective gain and offset compensation capability of the system. Results show that the non-linearity experimentally measured from our in-house metal-oxide memristor technology only induces a small gap between nominal resistive state and static RS (as reflected on the IV). This leads to a very small degradation of gain (nearly ≈ 2.5%) and offset compensation (≈ 50% increased offset tuning sensitivity), but very crucially proves that introducing IV non-linearity does not materially change either the extreme offset trimming precision or the overall performance.so for simulating and observing the output wave forms of the proposed we used the LT-Spice in windows environment.