In 1970, Kapron et. al. reported an optical fiber with a transmission loss of 20 dB/km, and a semiconductor laser continuously oscillating at room temperature was demonstrated as well. After then, technology development for optical fiber communication started in full swing. Optical fibers have been evolutionally advanced and, now, optical networks are indispensable life lines for human society. In this report, the progress of optical fibers in half a century is reviewed, and a recent trend is also introduced.
Perovskite solar cells have recently attracted great interest for next generation solar cells such as low-cost, light-weight, and/or flexible solar cells. While power conversion efficiency has reached as high as conventional widely used crystal Si solar cells, long-term stability and large area module production need to be improved for practical use. We found stability and crystal quality of perovskite films are improved by addition of small amount of alkaline metals such as Cs and Rb, which enables to achieve highly stable and large area perovskite solar cells. Here we review the effect of alkaline metal additives for lead-halide perovskite materials.
A state of the art Back Illuminated (BI) Single Photon Avalanche Diode (SPAD) array sensor realized via a 90 nm CMOS compatible process on a 300 mm silicon platform is reported in the following. The array consists of 10 µm pixels, each using a 7 µm thick silicon active layer, which allows to extend the device’s optical sensitivity up to the Near-Infrared (NIR) spectrum. Furthermore, buried metal Full Trench Isolation (FTI) was employed to suppress crosstalk (Xtalk),a critical feature in a device sensitive enough to be triggered by a single electro-luminescence photon emitted by a neighboring pixel. Finally, in order to maximize the Fill Factor (F.F) and allow a BI structure, a Cu-Cu bonding process was carried out.
Machine learning is attracting increased attention in the research field of novel materials development. Among the various material preparation methods, thin-film technology is actively used in various fundamental researches, industries, and applications for preparing thin films of various types of materials and nanostructured materials. In this article, our recent researches concerning novel materials development utilizing the combination of thin-film technologies and machine learning approaches are provided. Our methods using machine learning approaches were achieved to more efficient materials development, and would be able to be used for the exploration of novel thermoelectric materials.
Electric characterization of electronic devices is demonstrated from the viewpoints of noise measurement and system installation. Several examples, such as measurement automation, noise reduction from the environment, and measurement at cryogenic temperatures, are introduced using transistors and non-volatile memory devices.