Affiliation:
1. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA 92093 USA
2. Department of Physics University of California San Diego La Jolla CA 92093 USA
Abstract
AbstractWhile the complementary metal‐oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator‐to‐metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy‐efficient thermal neural networks, fostering progress in brain‐inspired computing.
Funder
Air Force Office of Scientific Research
U.S. Department of Energy
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Cited by
2 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