Nanoimprint Lithography Database System Design And Its Application In Neural Networks Parameters Optimization

Nanoimprint lithography(NIL)is one of the most promising nanofabrication techniques.As the next generation semiconductor lithography technoloy,NIL has the characteristics of low cost,high throughout,and high resolution.It is necessary to realize its application in nano-electronics,nano-photonics and biomedical devices.However,there is no standard parameters guideline to transfer patterns with low defect.To obtain optimal NIL process parameters,it is often required to develop a reliable tool for predicting the critical dimension and the defect rate of the transferred patterns.In recent decades,the rapid development of computer technologgy has made the “data + artificial intelligence” become the mainstream of materials research.Using data science methods to process and analyze data is accelerating the speed of material research and development.Based on the above research background,aiming to build a new research mode of “nanoimprint + big data”,this research built a nanoimprint database management system,and combined scanning electron microscope and Pro-SEM software to detect printing defects.In the same time,the research built a multi-layer neural network with nanoimprint material parameters and process parameters as the input layer,defect rate and pattern fidelity as the output layer.By digitizing the nanoimprint process,the research built a quantitative relationship between the defect rate,process parameters,and material properties to achieve nanoimprint fidelity and defect rate detection.The study laid the foundation for improving the fidelity and mitigating the dfect rate of the nanoimprint.Specifically,this research provided three methods for fabricating UV imprint mold,and evaluated their advantages and disadvantages.The first method combines photolithography,etching,evaporation,and other techniques to fabricate silicon mold with different aspect ratio,including three patterns(pillar,hole and grating).As for the pillar mold,the diameter is 1400.7 nm,and the height is 1220 nm,2287 nm,and 3490 nm respectively.As for the hole mold,the diameter is 2104.3 nm,and the depth is 1099 nm,2098 nm,and 2800 nm respectively.As for the grating mold,the critical dimension is 835.9 nm,and the height is 1219 nm,2087 nm,and 3011 nm.The second method used a replica technique to fabricate a transparent UV-PDMS soft mold.The third method used a UV curing technique to fabricate a transparent Ormo Stamp hard mold.At the same time,the research used automatic nanoimprint machine to carry out UV imprint experiment,combining with scanning electron microscope and Pro-SEM to develop a low cost nanoimprint metrology and defect detection method.Through the classification of graphic types,a defect rate calculation method was provided.Additionally,based on the experimental design of NIL,a NIL database system based based on Client/Server architecture was established using Dephi 7.0 and Microsoft Access database.The database system includes four modules: mold parameter management,imprint resist management,imprint process parameter management and system authority management,which realized the addition,deletion,modification and difference of mold parameters,imprint resist parameters,and imprint process parameters,including the query and entry of scanning electron microscope images.The system can help scientific research institutions and industrial people to choose NIL parameters,reducing the time and resource waste caused by the trial and error method.Finally,this research concluded with an overview of artificial neural networks,introducing three neural networks,including BP neural network(Back propagation neural network,BPNN),generalized regression nerual network,and probabilistic neural network,introducing the application of neural networks in parameter optimization.In accordance with SI standard units,standardized unit specifications for mold parameters,material parameters,and process parameters were established.At the same time,each parameter was converted into computer language,and a BP-ANN mold was trained using the imprint parameters from literature.The minimum mean square error was about 0.00566,and the correlation coefficient reached 0.983,proving the parameters fitting effect satisfies the need of parameter optimization.