Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications.

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade.

Analytics-statistics mixed training and its fitness to semisupervised manufacturing.

While there have been many studies using machine learning (ML) algorithms to predict process outcomes and device performance in semiconductor manufacturing, the extensively developed technology computer-aided design (TCAD) physical models should play a more significant role in conjunction with ML. While TCAD models have been effective in predicting the trends of experiments, a machine learning statistical model is more capable of predicting the anomalous effects that can be dependent on the chambers, machines, fabrication environment, and specific layouts. In this paper, we use an analytics-statistics mixed training (ASMT) approach using TCAD. Under this method, the TCAD models are incorporated into the machine learning training procedure. The mixed dataset with the experimental and TCAD results improved the prediction in terms of accuracy. With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study. In this method, statistical induction and analytical deduction can be combined to increase the prediction accuracy of future intelligent semiconductor manufacturing.

An ultra-compact blackbody using electrophoretic deposited carbon nanotube films.

Carbon nanotubes (CNTs) possesses decent optical properties and thus can be considered as a candidate for perfect absorbers due to their close-to-air refractive index and minimal extinction. However, weak absorption in porous materials, due to the low extinction coefficients, requires an inevitably thick absorption layer (∼100 µm) for the perfect opaque absorbers. Thus, the requirement of large thicknesses of CNTs prohibits them from being used as miniaturized integrated photonic devices. Here, we propose an electrophoretic deposited (EPD) CNT resonant cavity structure on tantalum (Ta) to enhance optical absorption. Efficient random light scattering along with the resonant cavity structure using Ti/SiO2 stacking enhances the absorption in our proposed EPD-CNT film while maintaining the total device thickness to <1 µm. The experiment results reveal that the absorption band covers the entire UV-VIS-NIR spectrum (λ = 0.3-2.6 µm), using resonant-cavity EPD-CNT design. The EPD deposition process is done at relatively low temperature < 120 °C. We believe that this proposal is very promising for sensing, antenna, and thermophotovoltaics (TPV), in terms of bandwidth, compactness and cost.

Polarization-selective ultra-broadband super absorber.

While a broadband metamaterial perfect absorber (MPA) has been implemented and proposed intensively in recent years, an ultra-broadband perfect absorber with polarization selectivity has not been realized in literature. In this work, we propose a configuration of polarization-selective (PS) MPA with ultra-wide absorption bandwidth. The aluminum wire grid is integrated on top of the ultrathin-metal-dielectric stacking. The transverse electric (TE) wave is blocked due to the requirement of zero tangential electric field at the metal surface. The transverse magnetic field can pass the aluminum wire-grids because the normal electric field can be supported by the surface charge density at the metal surface, and full absorption of the TM wave is accomplished by the metal-dielectric stacking beneath. Theoretical calculation using rigorously coupled wave analysis demonstrates the wavelength selectivity from λ = 1.98µm to λ = 11.74µm where the TE absorption is <0.04 while TM absorption is >0.95, using 300 nm thick aluminum (Al) wire grid with 16-pair SiO2/Ti stacking. Additionally, the design is wavelength scalable by adjusting the dielectric thickness (tSiO2) and the wire grid period (P) and height (t). The experimental result is demonstrated using Al grids and Ti/SiO2, and the measured result fully supports the calculated prediction.