We fabricated a set of symmetric gate-recess devices with gate length of 70 nm.We kept the source-to-drain spacing(L_(SD))unchanged,and obtained a group of devices with gate-recess length(L_(recess))from 0.4µm to...We fabricated a set of symmetric gate-recess devices with gate length of 70 nm.We kept the source-to-drain spacing(L_(SD))unchanged,and obtained a group of devices with gate-recess length(L_(recess))from 0.4µm to 0.8µm through process improvement.In order to suppress the influence of the kink effect,we have done SiN_(X) passivation treatment.The maximum saturation current density(ID_(max))and maximum transconductance(g_(m,max))increase as L_(recess) decreases to 0.4µm.At this time,the device shows ID_(max)=749.6 mA/mm at V_(GS)=0.2 V,V_(DS)=1.5 V,and g_(m,max)=1111 mS/mm at V_(GS)=−0.35 V,V_(DS)=1.5 V.Meanwhile,as L_(recess) increases,it causes parasitic capacitance C_(gd) and g_(d) to decrease,making f_(max) drastically increases.When L_(recess)=0.8µm,the device shows f_(T)=188 GHz and f_(max)=1112 GHz.展开更多
It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative freq...It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.展开更多
In this paper, we propose a new OBS scheme, named Fixed Burst Length OBS. The FBL-OBS networks, combined with the extra-offset time scheme, can guarantee the QoS of OBS networks in a more efficient way.
基金the National Natural Science Foundation of China(Grant No.61434006).
文摘We fabricated a set of symmetric gate-recess devices with gate length of 70 nm.We kept the source-to-drain spacing(L_(SD))unchanged,and obtained a group of devices with gate-recess length(L_(recess))from 0.4µm to 0.8µm through process improvement.In order to suppress the influence of the kink effect,we have done SiN_(X) passivation treatment.The maximum saturation current density(ID_(max))and maximum transconductance(g_(m,max))increase as L_(recess) decreases to 0.4µm.At this time,the device shows ID_(max)=749.6 mA/mm at V_(GS)=0.2 V,V_(DS)=1.5 V,and g_(m,max)=1111 mS/mm at V_(GS)=−0.35 V,V_(DS)=1.5 V.Meanwhile,as L_(recess) increases,it causes parasitic capacitance C_(gd) and g_(d) to decrease,making f_(max) drastically increases.When L_(recess)=0.8µm,the device shows f_(T)=188 GHz and f_(max)=1112 GHz.
基金supported by the Research on Key Technologies and Typical Applications of Big Data in Railway Production and Operation(P2023S006)the Fundamental Research Funds for the Central Universities(2022JBZY023).
文摘It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.
文摘In this paper, we propose a new OBS scheme, named Fixed Burst Length OBS. The FBL-OBS networks, combined with the extra-offset time scheme, can guarantee the QoS of OBS networks in a more efficient way.
