Skip to main navigation Skip to search Skip to main content

Progress in micron-scale field emission models based on nanoscale surface characterization for use in PIC-DSMC vacuum arc simulations

  • Chris H. Moore
  • , Ashish Jindal
  • , Ezra Bussmann
  • , Taisuke Ohta
  • , Morgann Berg
  • , Cherrelle Thomas
  • , David Scrymgeour
  • , Paul Clem
  • , Matthew M. Hopkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

3D Particle-In-Cell Direct Simulation Monte Carlo (PIC-DSMC) simulations of cm-sized devices cannot resolve atomic-scale (nm) surface features and thus one must generate micron-scale models for an effective “local” work function, field enhancement factor, and emission area. Here we report on development of a stochastic effective model based on atomic-scale characterization of as-built electrode surfaces. Representative probability density distributions of the work function and geometric field enhancement factor (beta) for a sputter-deposited Pt surface are generated from atomic-scale surface characterization using Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM), and Photoemission Electron Microscopy (PEEM). In the micron-scale model every simulated PIC-DSMC surface element draws work functions and betas for many independent “atomic emitters”. During the simulation the field emitted current from an element is computed by summing each “atomic emitter's” current. This model has reasonable agreement with measured micron-scale emitted currents across a range of electric field values.
Original languageEnglish
Title of host publicationProceedings - International Symposium on Discharges and Electrical Insulation in Vacuum, ISDEIV
Pages272-273
Number of pages2
Volume2021-September
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

Fingerprint

Dive into the research topics of 'Progress in micron-scale field emission models based on nanoscale surface characterization for use in PIC-DSMC vacuum arc simulations'. Together they form a unique fingerprint.

Cite this