Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT.

An improved structure for an Insulated Gate Bipolar Transistor (IGBT) with a separated buffer layer is presented in order to improve the trade-off between the turn-off loss (Eoff) and on-state voltage (Von). However, it is difficult to set efficient parameters due to the increase in the new buffer doping concentration variable. Therefore, a machine learning (ML) algorithm is proposed as a solution. Compared to the conventional Technology Computer-Aided Design (TCAD) simulation tool, it is demonstrated that incorporating the ML algorithm into the device analysis could make it possible to achieve high accuracy and significantly shorten the simulation time. Specifically, utilizing the ML algorithm could achieve coefficients of determination (R2) of Von and Eoff of 0.995 and 0.968, respectively. In addition, it enables the optimized design to fit the target characteristics. In this study, the structure proposed for the trade-off improvement was targeted to obtain the minimum Eoff at the same Von, especially by adjusting the concentration of the separated buffer. We could improve Eoff by 36.2% by optimizing the structure, which was expected to be improved by 24.7% using the ML approach. In another way, it is possible to inversely design four types of structures with characteristics close to the target characteristics (Eoff = 1.64 µJ, Von = 1.38 V). The proposed method of incorporating machine learning into device analysis is expected to be very strategic, especially for power electronics analysis (where the transistor size is comparatively large and requires significant computation). In summary, we improved the trade-off using a separated buffer, and ML enabled optimization and a more precise design, as well as reverse engineering.

Improvement in Turn-Off Loss of the Super Junction IGBT with Separated n-Buffer Layers.

In this study, we propose a super junction insulated-gate bipolar transistor (SJBT) with separated n-buffer layers to solve a relatively long time for carrier annihilation during turn-off. This proposition improves the turn-off characteristic while maintaining similar on-state characteristics and breakdown voltage. The electrical characteristics of the devices were simulated by using the Synopsys Sentaurus technology computer-aided design (TCAD) simulation tool, and we compared the conventional SJBT with SJBT with separated n-buffer layers. The simulation tool result shows that turn-off loss (Eoff) drops by about 7% when on-state voltage (Von) and breakdown voltage (BV) are similar. Von increases by about 0.5% and BV decreases by only about 0.8%.

Efficient and reusable ordered mesoporous WO x /SnO2 catalyst for oxidative desulfurization of dibenzothiophene.

The oxidative desulfurization (ODS) of organic sulfur compounds over tungsten oxide supported on highly ordered mesoporous SnO2 (WO x /meso-SnO2) was investigated. A series of WO x /meso-SnO2 with WO x contents from 10 wt% to 30 wt%, were prepared by conventional wet impregnation. The physico-chemical properties of the WO x /meso-SnO2 catalysts were characterized by X-ray diffraction (XRD), N2 adsorption-desorption isotherms, electron microscopy, Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, and the temperature-programmed reduction of hydrogen (H2-TPR). The characterization results indicated that these catalysts possessed mesoporous structures with uniform pores, high specific surface areas, and well-dispersed polyoxotungstate species on the surface of meso-SnO2 support. The ODS performances were evaluated in a biphasic system (model oil/acetonitrile, S initial = 2000 ppm), using H2O2 as an oxidant, and acetonitrile as an extractant. Dibenzothiophene (DBT) in the model oil was removed completely within 60 min at 50 °C using 20 wt% WO x /meso-SnO2 catalyst. Additionally, the effect of reaction temperature, H2O2/DBT molar ratio, amount of catalyst and different sulfur-containing substrates on the catalytic performances were also investigated in detail. More importantly, the 20 wt% WO x /meso-SnO2 catalyst exhibited 100% surfur-removal efficiency without any regeneration process, even after six times recycling.

Development of an activated carbon filter to remove NO2 and HONO in indoor air.

To obtain the optimum removal efficiency of NO2 and HONO by coated activated carbon (ACs), the influencing factors, including the loading rate, metal and non-metal precursors, and mixture ratios, were investigated. The NOx removal efficiency (RE) for K, with the same loading (1.0 wt.%), was generally higher than for those loaded with Cu or Mn. The RE of NO2 was also higher when KOH was used as the K precursor, compared to other K precursors (KI, KNO3, and KMnO4). In addition, the REs by the ACs loaded with K were approximately 38-55% higher than those by uncoated ACs. Overall, the REs (above 95%) of HONO and NOx with 3% KOH were the highest of the coated AC filters that were tested. Additionally, the REs of NOx and HONO using a mixing ratio of 6 (2.5% PABA (p-aminobenzoic acid)+6% H3PO4):4 (3% KOH) were the highest of all the coatings tested (both metal and non-metal). The results of this study show that AC loaded with various coatings has the potential to effectively reduce NO2 and HONO levels in indoor air.

Electromagnetic interference shielding effectiveness of monolayer graphene.

We report the first experimental results on the electromagnetic interference (EMI) shielding effectiveness (SE) of monolayer graphene. The monolayer CVD graphene has an average SE value of 2.27 dB, corresponding to ~40% shielding of incident waves. CVD graphene shows more than seven times (in terms of dB) greater SE than gold film. The dominant mechanism is absorption rather than reflection, and the portion of absorption decreases with an increase in the number of graphene layers. Our modeling work shows that plane-wave theory for metal shielding is also applicable to graphene. The model predicts that ideal monolayer graphene can shield as much as 97.8% of EMI. This suggests the feasibility of manufacturing an ultrathin, transparent, and flexible EMI shield by single or few-layer graphene.