Harnessing the Metal-Insulator Transition of VO 2 in Neuromorphic Computing.

Autor: Schofield P; Department of Chemistry, Texas A&M University, College Station, TX, 77843, USA.; Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA., Bradicich A; Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA., Gurrola RM; Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA., Zhang Y; Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA., Brown TD; Sandia National Laboratories, Livermore, CA, 94551, USA., Pharr M; Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA., Shamberger PJ; Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA., Banerjee S; Department of Chemistry, Texas A&M University, College Station, TX, 77843, USA.; Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA.
Jazyk: angličtina
Zdroj: Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2023 Sep; Vol. 35 (37), pp. e2205294. Date of Electronic Publication: 2022 Nov 29.
DOI: 10.1002/adma.202205294
Abstrakt: Future-generation neuromorphic computing seeks to overcome the limitations of von Neumann architectures by colocating logic and memory functions, thereby emulating the function of neurons and synapses in the human brain. Despite remarkable demonstrations of high-fidelity neuronal emulation, the predictive design of neuromorphic circuits starting from knowledge of material transformations remains challenging. VO 2 is an attractive candidate since it manifests a near-room-temperature, discontinuous, and hysteretic metal-insulator transition. The transition provides a nonlinear dynamical response to input signals, as needed to construct neuronal circuit elements. Strategies for tuning the transformation characteristics of VO 2 based on modification of material properties, interfacial structure, and field couplings, are discussed. Dynamical modulation of transformation characteristics through in situ processing is discussed as a means of imbuing synaptic function. Mechanistic understanding of site-selective modification; external, epitaxial, and chemical strain; defect dynamics; and interfacial field coupling in modifying local atomistic structure, the implications therein for electronic structure, and ultimately, the tuning of transformation characteristics, is emphasized. Opportunities are highlighted for inverse design and for using design principles related to thermodynamics and kinetics of electronic transitions learned from VO 2 to inform the design of new Mott materials, as well as to go beyond energy-efficient computation to manifest intelligence.
(© 2022 Wiley-VCH GmbH.)
Databáze: MEDLINE
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