Deep Learning-based Multiscale Modelling of Polysilicon MEMS
Author:
Affiliation:
1. Politecnico di Milano,Department of Civil and Environmental Engineering,Milano,Italy,20133
Funder
Universidad de Costa Rica
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9758836/9758837/09758899.pdf?arnumber=9758899
Reference11 articles.
1. Effect of Imperfections Due to Material Heterogeneity on the Offset of Polysilicon MEMS Structures
2. On-Chip Testing: A Miniaturized Lab to Assess Sub-Micron Uncertainties in Polysilicon MEMS
3. Mechanical Characterization of Polysilicon MEMS: A Hybrid TMCMC/POD-Kriging Approach
4. Mechanical characterization of polysilicon MEMS devices: a stochastic, deep learning-based approach;quesada-molina;21st International Conference on Thermal Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE),2020
5. Two-Scale Deep Learning Model for Polysilicon MEMS Sensors;quesada-molina;Computer Sciences & Mathematics Forum,2021
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1. Uncertainty Quantification at the Microscale: A Data-Driven Multi-Scale Approach;The 9th International Electronic Conference on Sensors and Applications;2022-11-01
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