Integrated Memristor Network for Physiological Signal Processing

Abstract

Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.

Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

“…134,135 Specifically, OIM-based memristors offer distinct advantages in neural simulation, brain–computer interfaces and the efficient photoelectric effect, due to their utilization of ionic–electronic materials with exceptional properties. 136–138 Some distinct properties of OIM-based memristors should be noted. Firstly, OIM-based memristors have good biocompatibility and can be directly integrated into organisms, providing the possibility to build bioelectronic interfaces.…”

“…Such characteristics will greatly actuate the advancement of efficient light control devices and optical memory. 136 OIMs have broad application prospects in the fields of biomimetic electronics, intelligent computing and medical electronics.…”

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

“…STDP was attained in an integrated device, viz., a nonvolatile Pt/TaO x /Ta RSM element and a volatile Pt/SiO x N y : Ag/Pt RSM element, owing to the comparable ionic dynamics in volatile RSM layers to chemical synapses. [406,415] For MTM layer-based synapses, binary MTJ configurations are utilized in neuromorphic computing to store binary information in magnetic layers, with the magnetization direction pointing up or down in next-generation memory elements. However, synaptic weights in neural networks, resembling synapses in the brain, are generally real valued.…”