The single-event effect（SEE） is the most serious problem in space environment.The modern semiconductor technology is concerned with the feasibility of the linear energy transfer（LET） as metric in characterizing SE...The single-event effect（SEE） is the most serious problem in space environment.The modern semiconductor technology is concerned with the feasibility of the linear energy transfer（LET） as metric in characterizing SEE induced by heavy ions.In this paper,we calibrate the detailed static random access memory（SRAM） cell structure model of an advanced field programmable gate array（FPGA） device using the computer-aided design tool,and calculate the heavy ion energy loss in multi-layer metal utilizing Geant4.Based on the heavy ion accelerator experiment and numerical simulation,it is proved that the metric of LET at the device surface,ignoring the top metal material in the advanced semiconductor device,would underestimate the SEE.In the SEE evaluation in space radiation environment the top-layers on the semiconductor device must be taken into consideration.展开更多
The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and ...The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects,and current industrial judgment methods rely excessively on human decision making.A novel stacked denoising autoencoders(SDAE)model optimized with support vector machine(SVM)theory was proposed for the recognition of cross-section defects.Firstly,interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve.Secondly,the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning,and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features,and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation.Finally,the curve mirroring and combination stitching methods were used as data augmentation for the training set,which dealt with the problem of sample imbalance in the original data set,and the accuracy of cross-section defect prediction was further improved.The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip,which helps to reduce flatness quality concerns in downstream processes.展开更多
文摘The single-event effect（SEE） is the most serious problem in space environment.The modern semiconductor technology is concerned with the feasibility of the linear energy transfer（LET） as metric in characterizing SEE induced by heavy ions.In this paper,we calibrate the detailed static random access memory（SRAM） cell structure model of an advanced field programmable gate array（FPGA） device using the computer-aided design tool,and calculate the heavy ion energy loss in multi-layer metal utilizing Geant4.Based on the heavy ion accelerator experiment and numerical simulation,it is proved that the metric of LET at the device surface,ignoring the top metal material in the advanced semiconductor device,would underestimate the SEE.In the SEE evaluation in space radiation environment the top-layers on the semiconductor device must be taken into consideration.
基金supported by the National Natural Science Foundation of China(No.52004029)the Joint Doctoral Program of China Scholarship Council(CSC)(202006460073)Liuzhou Science and Technology Plan Project,China(2021AAD0102).
文摘The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final failure.The characteristics of complex curve,significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects,and current industrial judgment methods rely excessively on human decision making.A novel stacked denoising autoencoders(SDAE)model optimized with support vector machine(SVM)theory was proposed for the recognition of cross-section defects.Firstly,interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile curve.Secondly,the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning,and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features,and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error back-propagation.Finally,the curve mirroring and combination stitching methods were used as data augmentation for the training set,which dealt with the problem of sample imbalance in the original data set,and the accuracy of cross-section defect prediction was further improved.The approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip,which helps to reduce flatness quality concerns in downstream processes.
