Memristors have emerged as a transformative technology in the realm of electronic devices, offering unique advantages such as fast switching speeds, low power consumption, and the ability to sensor-memory-compute. The applications span across non-volatile memory, neuromorphic computing, hardware security, and beyond, prompting memristors to become a versatile solution for next-generation computing and data storage systems. Despite enormous potential of memristors, the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability, device reproducibility, and array scalability. This review systematically explores recent advancements in high-performance memristor technologies, focusing on performance enhancement strategies through material engineering, structural design, pulse protocol optimization, and algorithm control. We provide an in-depth analysis of key performance metrics tailored to specific applications, including non-volatile memory, neuromorphic computing, and hardware security. Furthermore, we propose a co-design framework that integrates device-level optimizations with operational-level improvements, aiming to bridge the gap between theoretical models and practical implementations.

Surface electromyogram (sEMG) signals are valuable in healthcare and human-machine interaction. However, sEMG signals are inherently weak and unstable bioelectrical signals, rendering them highly susceptible to perturbations from various external factors. In this work, we firstly proposed utilizing the industrially producible Gen-4.5 heterogeneous integration technology to design an active 16-channel microelectrode array (MEA) based on amorphous indium–gallium–zinc oxide thin-film transistors (a-IGZO TFTs) capable of capturing and decoding sEMG signals. The a-IGZO TFTs demonstrate exceptional stability under bias (±20 V), temperature (200 ℃), and bending (6 mm, 30000 cycles), with a threshold voltage shift of less than 0.1 V and a standard deviation under 0.07 V for 100 randomly selected devices. Our state-of-the-art 16-channel active MEAs can collect sEMG signals from various hand gestures and analysis of motor unit action potential trains, expanding possibilities for human-machine interaction and electronic healthcare applications. The signal-to-noise ratio of sEMG signals reaches 85 dB, enabling a high average hand gesture recognition accuracy of 96.2%. This work highlights the potential of the scalable sEMG arrays with exceptional stability for multi-channel sEMG signal acquisition, representing a significant advancement in wearable health monitoring and interactive systems.

Neuromorphic devices, inspired by the intricate architecture of the human brain, have garnered recognition for their prodigious computational speed and sophisticated parallel computing capabilities. Vision, the primary mode of external information acquisition in living organisms, has garnered substantial scholarly interest. Notwithstanding numerous studies simulating the retina through optical synapses, their applications remain circumscribed to single-mode perception. Moreover, the pivotal role of temperature, a fundamental regulator of biological activities, has regrettably been relegated to the periphery. To address these limitations, we proffer a neuromorphic device endowed with multimodal perception, grounded in the principles of light-modulated semiconductors. This device seamlessly accomplishes dynamic hybrid visual and thermal multimodal perception, featuring temperature-dependent paired pulse facilitation properties and adaptive storage. Crucially, our meticulous examination of transfer curves, capacitance–voltage (C–V) tests, and noise measurements provides insights into interface and bulk defects, elucidating the physical mechanisms underlying adaptive storage and other functionalities. Additionally, the device demonstrates a variety of synaptic functionalities, including filtering properties, Ebbinghaus curves, and memory applications in image recognition. Surprisingly, the digital recognition rate achieves a remarkable value of 98.8%. These discernments furnish crucial insights for the prospective evolution of intricate neuromorphic systems.