We investigated wireless power transfer methods best suited for endoscopic energy devices. In order to insert the device into a body the transmitter and receiver need to have small tube-like bendable structures, and to avoid heating, the system needs to have high transmission efficiency. In this investigation, we used an electric field coupling method with stainless-steel monocoil electrodes (~φ3 mm), which was analytically and experimentally predicted to fulfill all the above conditions. The efficiency of a prototype was confirmed to be about 94% in the worst possible situation where the electrodes were severely bent. The proposed system was stable under the following conditions: transmission of electrical power up to 100 W; motion corresponding to insert and remove of the endoscopic energy devices; close approach of a human body to the electrodes.

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Recent electronic devices usually cannot pass radiated emission tests as the operating speed of these devices increases. In particular, when multiple clock drivers operate clock lines synchronously, the radiation noise level increases due to the overlapping of the noise sources. In conventional methods such as the near field measurement, it is impossible to identify a specific wiring connected to a noise source when the harmonic frequencies of each source overlap. In this paper, we verify whether the use of a notch filter, whose resonant frequency is equal to the problematic frequency in the radiated emission tests, and a switching circuit can identify the wiring connected to the noise source. Some experiments show that that location of the aforementioned circuit on the wiring of a PCB can identify the specific wiring connected to the noise source.

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We propose a nondestructive inline inspection and subsequent simple quantitative estimation of profiles of copper-filled through-silicon vias (Cu-TSVs) by viewing one-shot-images of Cu-TSVs with a nano-focus X-ray microscope. A nano-focus X-ray microscope with a resolution of 250 nm makes it possible to observe detailed views of φ5 μm × 50 μm copper pillars and small defects inside them. We use image processing and a supervised machine learning method to classify the void profiles. The voids are usually considered to have an indefinite shape. If rotationally symmetric features of the voids are recognized, the voids are modeled with rotationally symmetric structures and their positions in the Cu-TSVs’ pillar and their volumes are estimated with quantified dimensions. A cross-sectional view after focus ion beam (FIB) processing coincides well with the estimates of geometrical parameters. The classified results can be linked to analysis of mismatches in the conditions during the electro-deposition process.

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