HIGHER-ORDER SAMPLING STRATEGIES IN MONTE-CARLO SIMULATIONS OF ELECTRON-ENERGY DISTRIBUTION-FUNCTIONS IN PLASMAS

A new method of gathering statistics for Monte Carlo methods, Legendre polynomial weighted sampling (LPWS), is presented. LPWS requires only a minimum of particles to extract higher-order derivative information about a particle's distribution function. In this technique, when cal culating a particle's distribution function, higher-order derivative i nformation about the Monte Carlo particles is recorded along with just counting the number of particles in a bin. The distribution function is then constructed from this information. Specifically, in this paper , second-order Legendre polynomial weighted sampling is employed. Lege ndre polynomial weighted sampling is demonstrated by calculating the e lectron energy distribution functions in an inductively coupled plasma reactor.