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High-resolution fabrication of nanopatterns by multistep iterative miniaturization of hot-embossed prestressed polymer films and constrained shrinking

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Microsystems & Nanoengineering 2022年第1期8卷 281-289页

作者： shady sayed P.Ravi Selvaganapathy Department of Mechanical Engineering McMaster UniversityHamiltonON L8S 4L8Canada

The fabrication of nanostructures and nanopatterns is of crucial importance in microelectronics,nanofluidics,and the manufacture of biomedical devices and ***,the creation of nanopatterns by means of conventional nanofabrication techniques such as electron beam lithography is expensive and ***,we develop a multistep miniaturization approach using prestressed polymer films to generate nanopatterns from microscale patterns without the need of complex nanolithography *** polymer films have been used as a miniaturization technique to fabricate features with a smaller size than the initial imprinted ***,the height of the imprinted features is significantly reduced after the thermal shrinking of the prestressed films due to the shape memory effect of the polymer,and as a result,the topographical features tend to disappear after *** have developed a miniaturization approach that controls the material flow and maintains the shrunken patterns by applying mechanical constraints during the shrinking *** combination of hot embossing and constrained shrinking makes it possible to reduce the size of the initial imprinted features even to the *** developed multistep miniaturization approach allows using the shrunken pattern as a master for a subsequent miniaturization ***-defined patterns as small as 100 nm are fabricated,showing a 10-fold reduction in size from the original *** developed approach also allows the transfer of the shrunken polymeric patterns to a silicon substrate,which can be used as a functional substrate for many applications or directly as a master for nanoimprint lithography.

关键词： constrained polymer size

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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Computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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