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| Author | Kaintura, A.; Foss, K.; Couckuyt, I.; Dhaene, T.; Zografos, O.; Vaysset, A.; Sorée, B. | ||||
| Title | Machine Learning for Fast Characterization of Magnetic Logic Devices | Type | P1 Proceeding | ||
Year  |
2018 | Publication | (edaps 2018) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | P1 Proceeding; Condensed Matter Theory (CMT) | ||||
| Abstract | Non-charge-based logic devices are promising candidates for future logic circuits. Interest in studying and developing these devices has grown dramatically in the past decade as they possess key advantages over conventional CMOS technology. Due to their novel designs, a large number of micromagnetic simulations are required to fully characterize the behavior of these devices. The number and complexity of these simulations place large computational requirements on device development. We use state-of-the-art machine learning techniques to expedite identification of their behavior. Several intelligent sampling strategies are combined with machine learning multi-class classification models. These techniques are applied to a recently developed exchange-driven magnetic logic scheme that utilizes direct exchange coupling as the main driver. | ||||
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| Language | Wos | Publication Date | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-5386-6592-3 | ISBN | Additional Links | UA library record; WoS full record | |
| Impact Factor | Times cited | Open Access | |||
| Notes | Approved | Most recent IF: NA | |||
| Call Number | UA @ admin @ c:irua:160484 | Serial | 5219 | ||
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