Ultra Low Power 3D-Embedded Convolutional Neural Network Cube Based on α-IGZO Nanosheet and Bi-Layer Resistive Memory-Reference-Cited by-同舟云学术

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Ultra Low Power 3D-Embedded Convolutional Neural Network Cube Based on α-IGZO Nanosheet and Bi-Layer Resistive Memory

Published:2021-09-15 Issue: Volume: Page:
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Container-title:2021 International Conference on IC Design and Technology (ICICDT)
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Author:

Thunder Sunanda,Pal Parthasarathi,Wang Yeong-Her,Huang Po-Tsang

Funder

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9626390/9626391/09626489.pdf?arnumber=9626489

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. The Influence of Strain Path and Heat Treatment Variations on Recrystallization in Cold-Rolled High-Purity Niobium Polycrystals;IEEE Transactions on Applied Superconductivity;2023-08

3. Controllable coexistence of threshold and non-volatile crosspoint memory for highly linear synaptic device applications;Journal of Physics D: Applied Physics;2023-04-28

4. Synergistic Approach of Interfacial Layer Engineering and READ-Voltage Optimization in HfO₂-Based FeFETs for In-Memory-Computing Applications;ACS Applied Electronic Materials;2022-10-27

5. Three Level Charge Pumping On Dielectric Hafnium Oxide Gate;2022 IEEE International Integrated Reliability Workshop (IIRW);2022-10-09

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