GaN-based p-channel heterostructure field-effect transistors(p-HFETs)face significant constraints on on-state currents compared with n-channel high electron mobility transistors.In this work,we propose a novel double ...GaN-based p-channel heterostructure field-effect transistors(p-HFETs)face significant constraints on on-state currents compared with n-channel high electron mobility transistors.In this work,we propose a novel double heterostructure which introduces an additional p-GaN insertion layer into traditional p-HFETs.The impact of the device structure on the hole densities and valence band energies of both the upper and lower channels is analyzed by using Silvaco TACD simulations,including the thickness of the upper AlGaN layer and the doping impurities and concentration in the GaN buffer layer,as well as the thickness and Mg-doping concentration in the p-GaN insertion layer.With the help of the p-GaN insertion layer,the C-doping concentration in the GaN buffer layer can be reduced,while the density of the two-dimensional hole gas in the lower channel is enhanced at the same time.This work suggests that a double heterostructure with a p-GaN insertion layer is a better approach to improve p-HFETs compared with those devices with C-doped buffer layer alone.展开更多
Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to impro...Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.展开更多
Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational res...Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe.展开更多
A novel normally-off double channel reverse conducting(DCRC)HEMT with an integrated MOS-channel diode(MCD)is proposed and investigated by TCAD simulation.The proposed structure has two features:one is double heterojun...A novel normally-off double channel reverse conducting(DCRC)HEMT with an integrated MOS-channel diode(MCD)is proposed and investigated by TCAD simulation.The proposed structure has two features:one is double heterojunctions to form dual 2DEG channels named the 1^(st)path and the 2^(nd)path for reverse conduction,and the other is the MCD forming by the trench source metal,source dielectric,and Ga N.At the initial reverse conduction stage,the MCD acts as a switch to control the 1^(st)path which would be turned on prior to the 2^(nd)path.Because of the introduction of the 1^(st)path,the DCRC-HEMT has an additional reverse conducting channel to help enhance the reverse conduction performance.Compared with the conventional HEMT(Conv.HEMT),the DCRC-HEMT can obtain a low reverse turn-on voltage(VRT)and its VRTis independent of the gate-source bias(VGS)at the same time.The DCRC-HEMT achieves the VRTof 0.62 V,which is 59.7%and 75.9%lower than that of the Conv.HEMT at VGS=0 V and-1 V,respectively.In addition,the forward conduction capability and blocking characteristics almost remain unchanged.In the end,the key fabrication flows of DCRC-HEMT are presented.展开更多
受到外业采集条件限制和施工环境的影响,通常很难采集到理想且完整的规则采样地质雷达数据。地质雷达数据的缺失和不规则采样容易对数据处理过程产生严重干扰,影响后续解释工作。本文给出了一种基于无展开随机QR分解(Uncoiled Randomize...受到外业采集条件限制和施工环境的影响,通常很难采集到理想且完整的规则采样地质雷达数据。地质雷达数据的缺失和不规则采样容易对数据处理过程产生严重干扰,影响后续解释工作。本文给出了一种基于无展开随机QR分解(Uncoiled Randomized QR decomposition,URQR)的地质雷达数据重建方法。首先引入随机QR分解算法实现对地质雷达数据矩阵的降秩计算,并通过利用无展开求平均快速算法,来解决降秩后的Toeplitz矩阵对角线求平均效率低,内存占用量大的问题。然后基于凸集投影理论,实现了无展开随机QR分解算法的数据重建流程。最后,利用本文方法与随机奇异值分解(Randomized Singular Value Decomposition,RSVD)算法,对理论与实际地质雷达缺失道数据进行重建,通过对比质量因子Q值,说明了本文方法重建效果优于RSVD方法,对于大型地质雷达数据的重建,本文方法计算效率明显高于RSVD方法,验证了本文方法的有效性、可行性、效率高的特点。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62104184,62234009,62090014,62188102,62104178,and 62104179)the Fundamental Research Funds for the Central Universities of China(Grant Nos.YJSJ23019,XJSJ23047,and ZDRC2002)+1 种基金the China National Postdoctoral Program for Innovative Talents(Grant No.BX20200262)the China Postdoctoral Science Foundation(Grant No.2021M692499)。
文摘GaN-based p-channel heterostructure field-effect transistors(p-HFETs)face significant constraints on on-state currents compared with n-channel high electron mobility transistors.In this work,we propose a novel double heterostructure which introduces an additional p-GaN insertion layer into traditional p-HFETs.The impact of the device structure on the hole densities and valence band energies of both the upper and lower channels is analyzed by using Silvaco TACD simulations,including the thickness of the upper AlGaN layer and the doping impurities and concentration in the GaN buffer layer,as well as the thickness and Mg-doping concentration in the p-GaN insertion layer.With the help of the p-GaN insertion layer,the C-doping concentration in the GaN buffer layer can be reduced,while the density of the two-dimensional hole gas in the lower channel is enhanced at the same time.This work suggests that a double heterostructure with a p-GaN insertion layer is a better approach to improve p-HFETs compared with those devices with C-doped buffer layer alone.
基金Supported by National Natural Science Foundation of China(Grant No.51109094)Priority Academic Program Development of Jiangsu Higher Education Institutions of China
文摘Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.
基金National Natural Science Foundations of China(41671452)China Postdoctoral Science Foundation Funded Project(2017M612510)。
文摘Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe.
基金the National Natural Science Foundations of China(Grant Nos.61874149 and U20A20208)the Outstanding Youth Science and Technology Foundation of China(Grant No.2018-JCJQ-ZQ-060)。
文摘A novel normally-off double channel reverse conducting(DCRC)HEMT with an integrated MOS-channel diode(MCD)is proposed and investigated by TCAD simulation.The proposed structure has two features:one is double heterojunctions to form dual 2DEG channels named the 1^(st)path and the 2^(nd)path for reverse conduction,and the other is the MCD forming by the trench source metal,source dielectric,and Ga N.At the initial reverse conduction stage,the MCD acts as a switch to control the 1^(st)path which would be turned on prior to the 2^(nd)path.Because of the introduction of the 1^(st)path,the DCRC-HEMT has an additional reverse conducting channel to help enhance the reverse conduction performance.Compared with the conventional HEMT(Conv.HEMT),the DCRC-HEMT can obtain a low reverse turn-on voltage(VRT)and its VRTis independent of the gate-source bias(VGS)at the same time.The DCRC-HEMT achieves the VRTof 0.62 V,which is 59.7%and 75.9%lower than that of the Conv.HEMT at VGS=0 V and-1 V,respectively.In addition,the forward conduction capability and blocking characteristics almost remain unchanged.In the end,the key fabrication flows of DCRC-HEMT are presented.
文摘受到外业采集条件限制和施工环境的影响,通常很难采集到理想且完整的规则采样地质雷达数据。地质雷达数据的缺失和不规则采样容易对数据处理过程产生严重干扰,影响后续解释工作。本文给出了一种基于无展开随机QR分解(Uncoiled Randomized QR decomposition,URQR)的地质雷达数据重建方法。首先引入随机QR分解算法实现对地质雷达数据矩阵的降秩计算,并通过利用无展开求平均快速算法,来解决降秩后的Toeplitz矩阵对角线求平均效率低,内存占用量大的问题。然后基于凸集投影理论,实现了无展开随机QR分解算法的数据重建流程。最后,利用本文方法与随机奇异值分解(Randomized Singular Value Decomposition,RSVD)算法,对理论与实际地质雷达缺失道数据进行重建,通过对比质量因子Q值,说明了本文方法重建效果优于RSVD方法,对于大型地质雷达数据的重建,本文方法计算效率明显高于RSVD方法,验证了本文方法的有效性、可行性、效率高的特点。
