Your browser doesn't support javascript.
loading
Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing.
Qiu, Erbin; Zhang, Yuan-Hang; Ventra, Massimiliano Di; Schuller, Ivan K.
Afiliação
  • Qiu E; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
  • Zhang YH; Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA.
  • Ventra MD; Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA.
  • Schuller IK; Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA.
Adv Mater ; 36(6): e2306818, 2024 Feb.
Article em En | MEDLINE | ID: mdl-37770043
While 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.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Adv Mater Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Adv Mater Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos