Novel machine learning-based prediction approach for nanoindentation load-deformation in a thin film: Applications to electronic industries

被引：0
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作者：
Laxmikant Vajire, Sujal [1 ]
Prashant Singh, Abhishek [1 ,2 ]
Kumar Saini, Dinesh [3 ]
Kumar Mukhopadhyay, Anoop [4 ]
Singh, Kulwant [5 ,6 ]
Mishra, Dhaneshwar [1 ,7 ]
机构：
[1] Department of Mechanical Engineering, School of Automobile, Mechatronics, Mechanical Engineering, Manipal University Jaipur, Dahmikalan 303007, Rajasthan, Jaipur, India
[2] ARK Infosolutions Pvt. Ltd, Uttar Pradesh, Noida, India
[3] Department of Computer and Communication Engineering, School of Computers, Information Technology, Manipal University Jaipur, Dahmikalan 303007, Rajasthan, Jaipur, India
[4] Department of Physics, School of Basic Sciences and Research, Sharda University, Uttar Pradesh, Greater Noida,201310, India
[5] Department of Electronics and Communication Engineering, School of Electrical, Electronics, Communication Engineering, Manipal University Jaipur, Dahmikalan 303007, Rajasthan, Jaipur, India
[6] FlexMEMS Research Centre (FMRC), Manipal University Jaipur, Dahmikalan 303007, Rajasthan, Jaipur, India
[7] Multiscale Simulation Research Center (MSRC), Manipal University Jaipur, Dahmikalan 303007, Rajasthan, Jaipur, India
来源：
Computers and Industrial Engineering | 2022年 / 174卷
关键词：
Aluminum nitride - Deformation - E-learning - Gallium nitride - III-V semiconductors - Lattice mismatch - Light emitting diodes - Nanoindentation - Sapphire - Semiconductor quantum wells - Substrates;
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