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Improving robustness of GGNMOS with P-base layer for electrostatic discharge protection in 0.5-μm BCD process
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作者 Fei Hou ruibo chen +3 位作者 Feibo Du Jizhi Liu Zhiwei Liu Juin J Liou 《Chinese Physics B》 SCIE EI CAS CSCD 2019年第8期393-396,共4页
Gate-grounded N-channel MOSFET(GGNMOS)has been extensively used for on-chip electrostatic discharge(ESD)protection.However,the ESD performance of the conventional GGNMOS is significantly degraded by the current crowdi... Gate-grounded N-channel MOSFET(GGNMOS)has been extensively used for on-chip electrostatic discharge(ESD)protection.However,the ESD performance of the conventional GGNMOS is significantly degraded by the current crowding effect.In this paper,an enhanced GGNMOS with P-base layer(PB-NMOS)are proposed to improve the ESD robustness in BCD process without the increase in layout area or additional layer.TCAD simulations are carried out to explain the underlying mechanisms of that utilizing the P-base layer can effectively restrain the current crowing effect in proposed devices.All devices are fabricated in a 0.5-μm BCD process and measured using the transmission line pulsing(TLP)tester.Compared with the conventional GGNMOS,the proposed PB-NMOS devices offer a higher failure current than its conventional counterpart,which can be increased by 15.38%.Furthermore,the PB-NMOS type 3 possesses a considerably lower trigger voltage than the conventional GGNMOS to protect core circuit effectively. 展开更多
关键词 ESD GGNMOS failure current TRIGGER VOLTAGE
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MDC-Net:a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning 被引量:1
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作者 Wenxia Dai Yiheng Jiang +6 位作者 Wen Zeng ruibo chen Yongyang Xu Ningning Zhu Wen Xiao Zhen Dong Qingfeng Guan 《International Journal of Digital Earth》 SCIE EI 2023年第1期1224-1245,共22页
Wood-leaf separation from terrestrial laser scanning(TLS)is a crucial prerequisite for quantifying many biophysical properties and understanding ecological functions.In this study,we propose a novel multi-directional ... Wood-leaf separation from terrestrial laser scanning(TLS)is a crucial prerequisite for quantifying many biophysical properties and understanding ecological functions.In this study,we propose a novel multi-directional collaborative convolutional neural network(MDC-Net)that takes the original 3D coordinates and useful features from prior knowledge(prior features)as input,and outputs the semantic labels of TLS point clouds.The MDC-Net contains two key units:(1)a multi-directional neighborhood construction(MDNC)unit to obtain more representative neighbors and enable directionally aware feature encoding in the subsequent local feature extraction,to mitigate occlusion effects;(2)a collaborative feature encoding(CFE)unit is introduced to incorporate useful features from prior knowledge into the network through a collaborative cross coding to enhance the discrimination for thin structures(e.g.small branches and leaf).The MDC-Net is evaluated onfive plots from forests in Guangxi,China,with different branch architectures and leaf distributions.Experimental results showed that the MDC-Net achieved an OA of 0.973 and a mIoU of 0.821 and outperformed other related methods.We believe the MDC-Net would facilitate the usage of TLS in ecology studies for quantifying tree size and morphology and thus promote the development of relevant ecological applications. 展开更多
关键词 Terrestrial laser scanning wood and leaf separation deep learning prior features
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