Breakdown Lifetime Analysis of HfO2-based Ferroelectric Tunnel Junction (FTJ) Memory for In-Memory Reinforcement Learning
We clarified breakdown mechanisms of HfO 2 -based ferroelectric tunnel junction (FTJ) memory via systematic time-dependent dielectric breakdown (TDDB) measurement for realization of reliable in-memory reinforcement learning (RL) system. The defect generation in the interfacial layer SiO 2 determines...
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Published in | 2020 IEEE International Reliability Physics Symposium (IRPS) pp. 1 - 6 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.04.2020
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Subjects | |
Online Access | Get full text |
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Summary: | We clarified breakdown mechanisms of HfO 2 -based ferroelectric tunnel junction (FTJ) memory via systematic time-dependent dielectric breakdown (TDDB) measurement for realization of reliable in-memory reinforcement learning (RL) system. The defect generation in the interfacial layer SiO 2 determines the device breakdown. By detailed TDDB analysis, we found that the lifetime of FTJ with ~ 30nm in diameter satisfies endurance requirement for practical RL array operation. As a result, HfO 2 FTJ has high potential to achieve reliable cross-point RL system. |
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ISSN: | 1938-1891 |
DOI: | 10.1109/IRPS45951.2020.9129314 |