A packaged liquid lens driven by the dielectric force was demonstrated. The liquid lens consisted of a low dielectric constant droplet and a high dielectric constant sealing liquid. The two non-conductive liquids were sealed in a chamber under the condition of iso-density. Focal length of a liquid lens with an aperture of 3mm changed from 34mm to 12mm in the range of 0-200V. Hysteresis was observed in the liquid lens, with a maximum value measured of 12.5° at 120 volts in terms of droplet’s contact angle. The focal spot size measured approximately 80μm. Rise and fall times were 650ms and 300ms, respectively. The lens consumed 1mW of power when applying a 200 volt, 1 kHz signal. The longitudinal and transverse spherical aberrations were estimated to be nearly invariant when the focal length exceeded 20mm.

A liquid crystal droplet lens driven by the dielectrophoresis (DEP) force was demonstrated. The liquid crystal droplet lens was deformed by the DEP forces under non-uniform AC electric fields. Focal length, hysteresis and electrode design were studied. The focal length varied from 1.6mm to 2.6mm in the range of 0-200V at 1 kHz for electrode spacing of 50μm; that is, the tuning ratio of the focal length was about 60% in maximum. The hysteresis of contact angle was found to be less than 3° and it vanished after 1 minute at the rest state. As the electrode spacing over 200μm, the tuning ratios of the focal length dropped below 5%. The liquid crystal droplet lens that had numerical aperture of about 0.5 consumed power of about 0.1mW. Its response time was measured to be about 220ms.

An indium nitride (InN) gas sensor of 10 nm in thickness has achieved detection limit of 0.4 ppm acetone. The sensor has a size of 1 mm by 2.5 mm, while its sensing area is 0.25 mm by 2 mm. Detection of such a low acetone concentration in exhaled breath could enable early diagnosis of diabetes for portable physiological applications. The ultrathin InN epilayer extensively enhances sensing sensitivity due to its strong electron accumulation on roughly 5–10 nm deep layers from the surface. Platinum as catalyst can increase output current signals by 2.5-fold (94 vs. 37.5 μA) as well as reduce response time by 8.4-fold (150 vs. 1,260 s) in comparison with bare InN. More, the effect of 3% oxygen consumption due to breath inhalation and exhalation on 2.4 ppm acetone gas detection was investigated, indicating that such an acetone concentration can be analyzed in air.

[[abstract]]A silicon micromachining method has been developed to fabricate on-chip high-performance suspended spiral inductors, The spiral structure of an inductor was formed with polysilicon and was suspended over a 30-mum-deep cavity in the silicon substrate beneath. Copper (Cu) was electrolessly plated onto the polysilicon spiral to achieve low resistance. The Cu plating process also metallized the inner surfaces of the cavity, forming both a good radio-frequency (RF) ground and an electromagnetic shield, High quality factors (Qs) over 30 and self-resonant frequencies higher than 10 GHz have been achieved. Study of the mechanical properties of the suspended inductors indicates that they can withstand large shock and vibration, Simulation predicts a reduction of an order of magnitude in the mutual inductance of two adjacent inductors with the 30-mum-deep Cu-lined cavity from that with silicon as the substrate, This indicates very small crosstalk between the inductors due to the shielding effect of the cavities.[[fileno]]2020501010021[[department]]動機

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.