A large amount of ultra-low-power consumption electronic devices are urgently needed in the new era of the internet of things,which demand relatively low frequency response.Here,atomic layer deposition has been utiliz...A large amount of ultra-low-power consumption electronic devices are urgently needed in the new era of the internet of things,which demand relatively low frequency response.Here,atomic layer deposition has been utilized to fabricate the ion polarization dielectric of the Li PON-Al_(2)O_(3) hybrid structure.The Li PON thin film is periodically stacked in the Al_(2)O_(3) matrix.This hybrid structure presents a frequency-dependent dielectric constant,of which k is significantly higher than the aluminum oxide matrix from 1 k Hz to 200 k Hz in frequency.The increased dielectric constant is attributed to the lithium ions shifting locally upon the applied electrical field,which shows an additional polarization to the Al_(2)O_(3) matrix.This work provides a new strategy with promising potential to engineers for the dielectric constant of the gate oxide and sheds light on the application of electrolyte/dielectric hybrid structure in a variety of devices from capacitors to transistors.展开更多
This paper proposes an intelligent process fault diagnosis system through integrating the techniques of Andrews plot and convolutional neural network.The proposed fault diagnosis method extracts features from the on-l...This paper proposes an intelligent process fault diagnosis system through integrating the techniques of Andrews plot and convolutional neural network.The proposed fault diagnosis method extracts features from the on-line process measurements using Andrews function.To address the uncertainty of setting the proper dimension of extracted features in Andrews function,a convolutional neural network is used to further extract diagnostic information from the Andrews function outputs.The outputs of the convolutional neural network are then fed to a single hidden layer neural network to obtain the final fault diagnosis result.The proposed fault diagnosis system is compared with a conventional neural network based fault diagnosis system and integrating Andrews function with neural network and manual selection of features in Andrews function outputs.Applications to a simulated CSTR process show that the proposed fault diagnosis system gives much better performance than the conventional neural network based fault diagnosis system and manual selection of features in Andrews function outputs.It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2018YFB2200500and 2018YFB2200504)the National Natural Science Foundation of China(Grant Nos.22090010,22090011,and61504070)。
文摘A large amount of ultra-low-power consumption electronic devices are urgently needed in the new era of the internet of things,which demand relatively low frequency response.Here,atomic layer deposition has been utilized to fabricate the ion polarization dielectric of the Li PON-Al_(2)O_(3) hybrid structure.The Li PON thin film is periodically stacked in the Al_(2)O_(3) matrix.This hybrid structure presents a frequency-dependent dielectric constant,of which k is significantly higher than the aluminum oxide matrix from 1 k Hz to 200 k Hz in frequency.The increased dielectric constant is attributed to the lithium ions shifting locally upon the applied electrical field,which shows an additional polarization to the Al_(2)O_(3) matrix.This work provides a new strategy with promising potential to engineers for the dielectric constant of the gate oxide and sheds light on the application of electrolyte/dielectric hybrid structure in a variety of devices from capacitors to transistors.
基金supports from the European Commission (Project No.:PIRSES-GA-2013-612230)National Natural Science Foundation of China (project No.:61673236)are gratefully acknowledged.
文摘This paper proposes an intelligent process fault diagnosis system through integrating the techniques of Andrews plot and convolutional neural network.The proposed fault diagnosis method extracts features from the on-line process measurements using Andrews function.To address the uncertainty of setting the proper dimension of extracted features in Andrews function,a convolutional neural network is used to further extract diagnostic information from the Andrews function outputs.The outputs of the convolutional neural network are then fed to a single hidden layer neural network to obtain the final fault diagnosis result.The proposed fault diagnosis system is compared with a conventional neural network based fault diagnosis system and integrating Andrews function with neural network and manual selection of features in Andrews function outputs.Applications to a simulated CSTR process show that the proposed fault diagnosis system gives much better performance than the conventional neural network based fault diagnosis system and manual selection of features in Andrews function outputs.It reveals that the use of Andrews function and convolutional neural network can improve the diagnosis performance.
