Simulations of monolayer SiC transistors with metallic 1T-phase MoS_(2) contact for high performance application

We preform a first-principles study of performance of 5 nm double-gated(DG)Schottky-barrier field effect transistors(SBFETs)based on two-dimensional SiC with monolayer or bilayer metallic 1T-phase MoS_(2) contacts.Because of the wide bandgap of SiC,the corresponding DG SBFETs can weaken the short channel effect.The calculated transfer characteristics also meet the standard of the high performance transistor summarized by international technology road-map for semiconductors.Moreover,the bilayer metallic 1T-phase MoS_(2) contacts in three stacking structures all can further raise the ON-state currents of DG SiC SBFETs in varying degrees.The above results are helpful and instructive for design of short channel transistors in the future.

Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling

Recently,steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features.However,most existing methods based on deep learning are specially designed for one image domain(i.e.,spatial or JPEG),and they often take long time to train.To make a balance between the detection performance and the training time,in this paper,we propose an effective and relatively fast steganalytic network called US-CovNet(Universal Steganalytic Covariance Network)for both{the}spatial and JPEG domains.To this end,we carefully design several important components of{US-CovNet}that will significantly affect the detection performance,including the high-pass filter set,the shortcut connection and the pooling{layer}.Extensive experimental results show that compared with the current best steganalytic networks(i.e.,SRNet and J-YeNet),{US-CovNet}can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography.For example,the detection accuracy of US-CovNet is at least 0.56%higher than that of SRNet in the spatial domain.In the JPEG domain,US-CovNet performs slightly worse than J-YeNet in some cases with the degradation of less than 0.78%.However,the training time of US-CovNet is significantly reduced,which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.