Using deep learning approaches to overcome limited dataset issues within semiconductor domain

Milad Omrani Tamrin, Sebastien Henwood, Jean-François Dubois, Jean-Jules Brault, Saad Chidami and Samuel Bassetto

Paper (2019)

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Department:	Department of Mathematics and Industrial Engineering Department of Electrical Engineering
Research Center:	GR2M - Microelectronics and Microsystems Research Group
PolyPublie URL:	https://publications.polymtl.ca/44375/
Conference Title:	17th IEEE International New Circuits and Systems Conference (NEWCAS 2019)
Conference Location:	Munich, Germany
Conference Date(s):	2019-06-23 - 2019-06-26
Publisher:	IEEE
DOI:	10.1109/newcas44328.2019.8961246
Official URL:	https://doi.org/10.1109/newcas44328.2019.8961246
Date Deposited:	18 Apr 2023 15:02
Last Modified:	05 Apr 2024 11:43

Cite in APA 7:	Tamrin, M. O., Henwood, S., Dubois, J.-F., Brault, J.-J., Chidami, S., & Bassetto, S. (2019, June). Using deep learning approaches to overcome limited dataset issues within semiconductor domain [Paper]. 17th IEEE International New Circuits and Systems Conference (NEWCAS 2019), Munich, Germany (4 pages). https://doi.org/10.1109/newcas44328.2019.8961246
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