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Simulations of monolayer SiC transistors with metallic 1T-phase MoS_(2) contact for high performance application
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作者 Hai-Qing Xie Dan Wu +4 位作者 xiao-qing deng Zhi-Qiang Fan Wu-Xing Zhou Chang-Qing Xiang Yue-Yang Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第11期495-500,共6页
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.Bec... 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. 展开更多
关键词 Schottky-barrier field effect transistor SIC band structure short channel effect
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Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling
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作者 xiao-qing deng Bo-Lin Chen +1 位作者 Wei-Qi Luo Da Luo 《Journal of Computer Science & Technology》 SCIE EI CSCD 2022年第5期1134-1145,共12页
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 desi... 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. 展开更多
关键词 STEGANALYSIS STEGANOGRAPHY convolutional neural networks global covariance pooling
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