Nam‐Young Kim

This work proposes a microwave resonator built from gallium arsenide using integrated passive device (IPD) technology. It consists of a three-layered interlaced spiral structure with airbridges and inner interdigital structures. For integrated systems, IPD technology demonstrated outstanding performance, robustness, and a tiny size at a low cost. The airbridges were made more compact, with overall dimensions of 1590 × 800 µm2 (0.038 × 0.019 λg2). The designed microwave resonator operated at 1.99 GHz with a return loss of 39 dB, an insertion loss of 0.07 dB, and a quality factor of 1.15. Additionally, an experiment was conducted on the properties of the airbridge and how they affected resistance, inductance, and S-parameters in the construction of the resonator. To investigate the impact of airbridges on the structure, E- and H-field distributions of the resonator were simulated. Furthermore, its use in sensing applications was explored. Various concentrations of glucose solutions were used in the experiment. The proposed device featured a minimum detectable concentration of 0.2 mg/mL; high sensitivity, namely, 14.58 MHz/mg·mL−1, with a linear response; and a short response time. Thus, this work proposes a structure that exhibits potential in integrated systems and real-time sensing systems with high sensitivity.

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.