Developing electronic skins (e‐skins) that are comparable to or even beyond human tactile perception holds significant importance in advancing the process of intellectualization. In this context, a machine‐learning‐motivated micropyramid array bimodal (MAB) e‐skin based on capacitive sensing is reported, which enables spatial mapping applications based on bimodal sensing (proximity and pressure) implemented via fringing and iontronic effects, such as contactless measurement of 3D objects and contact recognition of Braille letters. Benefiting from the iontronic effect and single‐micropyramid structure, the MAB e‐skin in pressure mode yields impressive features: a maximum sensitivity of 655.3 kPa⁻¹ (below 0.5 kPa), a linear sensitivity of 327.9 kPa⁻¹ (0.5–15 kPa), and an ultralow limit of detection of 0.2 Pa. With the assistance of multilayer perceptron and convolutional neural network, the MAB e‐skin can accurately perceive 6 materials and 10 surface shapes based on the training and learning using the collected datasets from proximity and pressure modes, thus allowing it to achieve the precise perception of different objects within one proximity‐pressure cycle. The development of this MAB e‐skin opens a new avenue for robotic skin and the expansion of advanced applications.

This article presents a high-sensitivity, quantified, linear, and mediator-free resonator-based microwave biosensor for glucose sensing application. The proposed biosensor comprises an air-bridge-type asymmetrical differential inductor (L) and a center-loaded circular finger-based inter-digital capacitor (C) fabricated on Gallium Arsenide (GaAs) substrate using advanced micro-fabrication technology. The intertwined asymmetrical differential inductor is used to achieve a high inductance value with a suitable Q-factor, and the centralized inter-digital capacitor is introduced to generate an intensified electric field. The designed microwave sensor is optimized to operate at a low resonating frequency that increases the electric field penetration depth and interaction area in the glucose sample. The microwave biosensor is tested with different glucose concentrations (0.3–5 mg/ml), under different ambient temperatures (10–50 °C). The involvement of advanced micro-fabrication technology effectively miniaturized the microwave biosensor (0.006λ₀ × 0.005λ₀) and enhanced its filling factor. The proposed microwave biosensor demonstrates a high sensitivity of 117.5 MHz/mgmL^-1 with a linear response (r² = 0.9987), good amplitude variation of 0.49 dB/mgmL^-1 with a linear response (r² = 0.9954), and maximum reproducibility of 0.78% at 2 mg/mL. Additionally, mathematical modelling was performed to estimate the dielectric value of the frequency-dependent glucose sample. The measured and analyzed results indicate that the proposed biosensor is suitable for real-time blood glucose detection measurements.

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO₂ Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing, particularly in challenging environments for monitoring industry and healthcare applications. These systems are equipped with battery-free operation, wireless connectivity, and are designed to be both miniaturized and lightweight. Such features enable the safe, real-time monitoring of industrial environments and support high-precision physiological measurements in confined internal body spaces and on wearable epidermal devices. Despite the exploration into diverse application environments, the development of a systematic and comprehensive research framework for system architecture remains elusive, which hampers further optimization of these systems. This review, therefore, begins with an examination of application scenarios, progresses to evaluate current system architectures, and discusses the function of each component—specifically, the passive sensor module, the wireless communication model, and the readout module—within the context of key implementations in target sensing systems. Furthermore, we present case studies that demonstrate the feasibility of proposed classified components for sensing scenarios, derived from this systematic approach. By outlining a research trajectory for the application of passive wireless systems in sensing technologies, this paper aims to establish a foundation for more advanced, user-friendly applications.