AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators

2024/11/01 by Karan Patel, Andrew Maicke, Patel, Karan P. +15 · 1 citation
Engineering · #3D IC and TSV technologies #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Manufacturing Process and Optimization #Modular Robots and Swarm Intelligence #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.2411.01008

openalex publication_date 2024/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. 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 AI 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.

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