Milad Omrani Tamrin, Sébastien 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 |
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| Research Center: | GR2M - Microelectronics and Microsystems Research Group |
| ISBN: | 9781728110318 |
| 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: | 08 Apr 2025 12:23 |
| 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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