We have explicated the Goos-Hänchen (GH) shift in a mum-order Kretchmann-Raether configuration embedded in an optical waveguide structure by using the finite-difference time-domain method. For optical waveguide-type surface plasmon resonance (SPR) devices, the precise derivation of the GH shift has become critical. Artmann's equation, which is accurate enough for bulk optics, is difficult to apply to waveguide-type SPR devices. This is because Artmann's equation, based on the differentiation of the phase shift, is inaccurate at the critical and resonance angles where drastic phase changes occur. In this study, we accurately identified both the positive and the negative GH shifts around the incidence angle of resonance. In a waveguide-type Kretchmann-Raether configuration with an Au thin film of 50 nm, positive and negative lateral shifts of -0.75 and + 1.0 microm are obtained on the SPR with the incident angles of 44.4 degrees and 47.5 degrees, respectively, at a wavelength of 632.8 nm.

Diamond is a highly attractive ultrawide bandgap semiconductor for next-generation high-power switching devices and RF devices for its superior physical and electrical properties. However, the lack of effective n-type dopants in diamond has limited the material to only unipolar p-type device applications. Heterostructure bipolar devices that use better n-type semiconductors together with p-type diamond is an approach to get high performance devices. In this work, p–n–p AlGaAs/GaAs/diamond heterojunction bipolar transistors (HBTs) are proposed and fabricated using a grafting technique. The double-heterojunction is formed by transferring an AlGaAs(p-type)/GaAs(n-type) membrane onto single-crystalline p-type doped diamond with an electron affinity of 0.32 eV. The epitaxial AlGaAs/GaAs emitter-base p–n junction shows an ideality factor of 1.09 with an Ion/Ioff of 1.53 × 107 at ± 1.5 V. The grafted GaAs/diamond n–p junction shows an ideality factor of 3.67 with an Ion/Ioff of 3.74 × 1010 at ± 5.2 V. Due to the valence-band energy barrier of 0.3 eV between the GaAs base and the diamond collector, the measured current gain for the HBT is slightly below unity. Simulations show that by reducing the electron affinity value of the p-type diamond, the base-collector energy barrier height can be correspondingly reduced, and high current gain can be expected.

Flexible capacitive pressure sensors with a simple structure and low power consumption are attracting attention, owing to their wide range of applications in wearable electronic devices. However, it is difficult to manufacture pressure sensors with high sensitivity, wide detection range, and low detection limits. We developed a highly sensitive and flexible capacitive pressure sensor based on the porous Ecoflex, which has an aligned airgap structure and can be manufactured by simply using a mold and a micro-needle. The existence of precisely aligned airgap structures significantly improved the sensor sensitivity compared to other dielectric structures without airgaps. The proposed capacitive pressure sensor with an alignment airgap structure supports a wide range of working pressures (20–100 kPa), quick response time (≈100 ms), high operational stability, and low-pressure detection limit (20 Pa). Moreover, we also studied the application of pulse wave monitoring in wearable sensors, exhibiting excellent performance in wearable devices that detect pulse waves before and after exercise. The proposed pressure sensor is applicable in electronic skin and wearable medical assistive devices owing to its excellent functional features.

BackgroundTotal kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming.MethodsWe developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts. We used abdominal T2-weighted magnetic resonance images from 210 individuals with ADPKD who were divided into two groups: one group of 157 to train the network and a second group of 53 to test it. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation, in that 80% of 157 cases were for training and the remaining 20% were for validation. We used Dice similarity coefficient, intraclass correlation coefficient, and Bland–Altman analysis to assess the performance of the automated segmentation method compared with the manual method.ResultsThe automated and manual reference methods exhibited excellent geometric concordance (Dice similarity coefficient: mean±SD, 0.962±0.018) on the test datasets, with kidney volumes ranging from 178.9 to 2776.0 ml (mean±SD, 1058.5±706.8 ml) and exophytic cysts ranging from 113.4 to 2497.6 ml (mean±SD, 549.0±559.1 ml). The intraclass correlation coefficient was 0.9994 (95% confidence interval, 0.9991 to 0.9996; P<0.001) with a minimum bias of −2.424 ml (95% limits of agreement, −49.80 to 44.95).ConclusionsWe developed a fully automated segmentation method to measure TKV that excludes exophytic cysts and has an accuracy similar to that of a human expert. This technique may be useful in clinical studies that require automated computation of TKV to evaluate progression of ADPKD and response to treatment.