Synergistic effect of total ionizing dose on single-event gate rupture in SiC power MOSFETs

The synergistic effect of total ionizing dose(TID) and single event gate rupture(SEGR) in SiC power metal–oxide–semiconductor field effect transistors(MOSFETs) is investigated via simulation. The device is found to be more sensitive to SEGR with TID increasing, especially at higher temperature. The microscopic mechanism is revealed to be the increased trapped charges induced by TID and subsequent enhancement of electric field intensity inside the oxide layer.

Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy

The hot deformation behaviors of FGH98 nickel-based powder superalloy were experimentally investigated and theoretically analyzed by Arrhenius models and machine learning(ML).Hot compression tests were conducted with a Gleeble-3800 thermo-mechanical simulation machine on the FGH98 superalloy at strain rates of 0.001–1 s–1 and temperatures of 1025–1175℃.The peak stresses under different deformation conditions were analyzed via the Sellars model and an ML-inspired Gaussian process regression(GPR)model.The prediction of the GPR model outperformed that from the Sellars model.In addition,the stress-strain responses were predicted by the GPR model and tested by experimentally measured stress-strain curves.The results indicate that the developed GPR model has great power with wide generalization capability in the prediction of hot deformation behaviors of FGH98 superalloy,as evidenced by the R2 value higher than 0.99 on the test dataset.

Real-World Superresolution by Using Deep Degradation Learning

Most current deep convolutional neural networks can achieve excellent results on a single image superresolution and are trained using corresponding high-resolution(HR)images and low-resolution(LR)images.Conversely,their superresolution performance in real-world superresolution tests is reduced because thesemethods create paired LR images by simply interpolating and downsampling HR images,which is very different from natural degradation.In this article,we design a new unsupervised framework conditioned by degradation representations of real-world hyperresolution problems.The approach presented in this paper consists of three stages:we first learn the implicit degradation representation from real-world LR images and then acquire LR images by shrinking the network,which will share similar degradation with real-world images.Finally,we make paired data of the generated real LR images and HR images for training the SR network.Our approach can obtain better results than the recent SR approach on the NTIRE2020 real-world SR challenge Track1 dataset.