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E u r o S c i C o n C o n f e r e n c e o n
Nanotechnology &
Smart Materials
Nano Research & Applications
ISSN 2471-9838
O c t o b e r 0 4 - 0 6 , 2 0 1 8
Am s t e r d a m , N e t h e r l a n d s
Nanotechnology & Smart Materials 2018
Page 63
S
ince a decade, deep learning (DL) has been exploited in various fields such
as healthcare, automobile, electronics, weather prediction, telecom and many
more. DL has the ability to learn the dependence between two sets of data and
to generalize on unseen data, whereas major characteristic of DL is to discover
intricate structure in large datasets. It has huge potential to be used in materials
process and micro-electro-mechanical systems (MEMS). MEMS devices’
experimental and commercial simulator results may not be matching due to
unavoidable environmental conditions while experimenting, difference in design
and fabricated device, etc. DL model is made using MEMS devices experimental
study which may give accurate predictive result compared to simulators. These
analytical models prepared using DLmay bemore accurate, fast and cost effective
solution as compared to commercial available MEMS softwares.
Biography
Ankit Agarwal has completed his B Tech from BIET, Jhansi,
India and M Tech from IIT, Delhi. He worked as Research
Assistant at Trinity College Dublin, Dublin City, Ireland. Currently,
he is working as a Senior Data Scientist in Mobileum. His
interests are to explore machine learning and deep learning
for experimental applications. He has already demonstrated
deep learning for telecom and computer vision. He is highly
motivated to apply deep learning for MEMS systems.
ankit.agarwal@mobileum.comDeep learning model for MEMS
Ankit Agarwal
Mobileum, India
Ankit Agarwal, Nano Res Appl Volume:4
DOI: 10.21767/2471-9838-C6-024