Electro-Thermal Characterization of Dynamical VO2 Memristors via Local Activity Modeling

Brown, Timothy D.; Bohaichuk, Stephanie M.; Islam, Mahnaz; Kumar, Suhas; Pop, Eric; Williams, R. Stanley

doi:10.1002/adma.202205451

Electro-Thermal Characterization of Dynamical VO₂ Memristors via Local Activity Modeling

Journal Article · Tue Sep 27 00:00:00 EDT 2022 · Advanced Materials

DOI:https://doi.org/10.1002/adma.202205451· OSTI ID:1894601

Brown, Timothy D. ^[1]; Bohaichuk, Stephanie M. ^[2]; Islam, Mahnaz ^[2]; ^[3]; Pop, Eric ^[2]; ^[4]

Texas A & M University, College Station, TX (United States); Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Stanford University, CA (United States)
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Texas A & M University, College Station, TX (United States)

We report translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO₂/SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); ON Semiconductor; Stanford SystemX Alliance; Texas A&M University; USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1894601

Alternate ID(s):: OSTI ID: 1995858

Report Number(s):: SAND2022-12567J; 709900

Journal Information:: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 37 Vol. 35; ISSN 0935-9648

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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