AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators
Abstract Emerging devices, such as magnetic tunnel junctions, are key for energy-efficient, performant future computing systems. However, designing devices with the desirable specification and performance for these applications is often found to be time-consuming and non-trivial. Here, we investigat...
Saved in:
| Main Authors: | Karan P. Patel, Andrew Maicke, Jared Arzate, Jaesuk Kwon, J. Darby Smith, James B. Aimone, Jean Anne C. Incorvia, Suma G. Cardwell, Catherine D. Schuman |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00376-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Inelastic Electron Tunneling Spectroscopy of Aryl Alkane Molecular Junction Devices with Graphene Electrodes
by: Hyunwook Song
Published: (2025-05-01) -
True-Randomness and Pseudo-Randomness in Ring Oscillator-Based True Random Number Generators
by: Nathalie Bochard, et al.
Published: (2010-01-01) -
Effect of Dzyaloshinskii Moriya interaction on magnetic tunnel junction based molecular spintronics devices (MTJMSD)
by: Babu Ram Sankhi, et al.
Published: (2025-02-01) -
Engineering of Germanium Tunnel Junctions for Optical Applications
by: Roman Koerner, et al.
Published: (2018-01-01) -
Interface Feedback Effect in Molecular Tunnel Junctions
by: Yunxia Feng, et al.
Published: (2025-02-01)