Perpendicular-spin-transfer-torque magnetic-tunnel-junction neuron for spiking neural networks depending on the nanoscale grain size of the MgO tunnelling barrieropen access
- Authors
- Baek, Jong-Ung; Choi, Jin-Young; Kim, Dong-Won; Kim, Ji-Chan; Jun, Han-Sol; Woo, Dae-Seong; Yi, Woo-Seok; Choi, Yo-Han; Seo, Hyung-Tak; Kim, Jae-Joon; Park, Jea-Gun
- Issue Date
- Feb-2022
- Publisher
- ROYAL SOC CHEMISTRY
- Citation
- MATERIALS ADVANCES, v.3, no.3, pp.1587 - 1593
- Indexed
- SCOPUS
- Journal Title
- MATERIALS ADVANCES
- Volume
- 3
- Number
- 3
- Start Page
- 1587
- End Page
- 1593
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139621
- DOI
- 10.1039/d1ma00862e
- Abstract
- Unlike conventional neuromorphic chips fabricated with C-MOSFETs and capacitors, those utilizing p-STT MTJ neuron devices can achieve fast switching (on the order of several tens of nanoseconds) and extremely low power consumption (<0.2 pJ per spike). A p-STT MTJ neuron with a sensing circuit, which is composed of one p-STT MTJ neuron device, seven n-MOSFETs, three p-MOSFETs, and one reference resistor, was constructed in this study and presented integrate-and-fire characteristics for use in spiking neural networks. In particular, the difference in resistance between the no-spiking input and after the implementation of integration-and-fire was found to be principally determined by the average nanoscale grain size (i.e., 0.418 to 1.141 nm) and face-centered-cubic crystallinity of the MgO tunnelling barrier of the p-STT MTJ neuron devices. Therefore, a larger grain size and better crystallinity led to a larger resistance difference in these devices. MNIST pattern recognition tests (achieving a testing accuracy of 90.34%) using the p-STT MTJ neurons were conducted for demonstrating a spiking neural network.
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