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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00376-8 |
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| _version_ | 1849774362906853376 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Karan P. Patel |
| collection | DOAJ |
| description | 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 investigate the design and optimization of spin–orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our artificial-intelligence-guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices. This framework can also be applied to other devices and applications. |
| format | Article |
| id | doaj-art-c5b25db16aaf46f89fe7472cddc0b696 |
| institution | DOAJ |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-c5b25db16aaf46f89fe7472cddc0b6962025-08-20T03:01:43ZengNature PortfolioCommunications Engineering2731-33952025-03-014111110.1038/s44172-025-00376-8AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generatorsKaran P. Patel0Andrew Maicke1Jared Arzate2Jaesuk Kwon3J. Darby Smith4James B. Aimone5Jean Anne C. Incorvia6Suma G. Cardwell7Catherine D. Schuman8Department of Electrical Engineering and Computer Science, University of TennesseeChandra Family Dept. of Electrical and Computer Engineering, The University of Texas at AustinChandra Family Dept. of Electrical and Computer Engineering, The University of Texas at AustinChandra Family Dept. of Electrical and Computer Engineering, The University of Texas at AustinSandia National LaboratoriesSandia National LaboratoriesChandra Family Dept. of Electrical and Computer Engineering, The University of Texas at AustinSandia National LaboratoriesDepartment of Electrical Engineering and Computer Science, University of TennesseeAbstract 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 investigate the design and optimization of spin–orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our artificial-intelligence-guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices. This framework can also be applied to other devices and applications.https://doi.org/10.1038/s44172-025-00376-8 |
| spellingShingle | 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 AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators Communications Engineering |
| title | AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators |
| title_full | AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators |
| title_fullStr | AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators |
| title_full_unstemmed | AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators |
| title_short | AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators |
| title_sort | ai guided framework for the design of materials and devices for magnetic tunnel junction based true random number generators |
| url | https://doi.org/10.1038/s44172-025-00376-8 |
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