In high-precision fields such as industrial automation, aerospace, and infrastructure monitoring, the accurate perception of motion states is crucial for ensuring stable system operation.

In this study, the authors propose a bionic neuromorphic soft pressure sensor designed to achieve stable underwater performance and simplified signal processing.

In the field of precision manufacturing, the stable operation of grinding machines directly determines the surface quality and production efficiency of products. Hidden faults such as bearing overheating, grinding wheel wear, and abnormal vibrations can lead to workpiece scrap in mild cases or trigger equipment shutdowns in severe cases, resulting in significant economic losses.

During the machining process, the oil film formed by the lubrication system serves as the core barrier ensuring stable equipment operation. Its thickness directly impacts machining accuracy, part quality, and equipment lifespan.

Current pressure and temperature sensing methods face challenges such as crosstalk, low integration, and difficulties in achieving large-scale arrays, which significantly hinder the practical application value of multifunctional bionic sensors in intelligent bionic robots.

Ultrafast detection and identification techniques for temperature fields are crucial in various applications, spanning environmental sensing, biomedical monitoring, and thermal management in advanced energy systems. Traditional temperature sensing systems typically employ the von Neumann architecture, comprising discrete sensor arrays, memory modules, and processors.