A computationally efficient target search algorithm for a Monte Carlo ion implantation simulator

Abstract

Ion implantation is a critical technology in semiconductor Ultra Large Scale Integration (ULSI). Binary collision approximation (BCA)-based Monte Carlo (MC) ion implantation simulators are commonly used to predict the impurity and damage profiles. A deterministic propagator is needed in these simulators to simulate the propagation of ions in crystalline materials. A search-for-target algorithm is frequently called to determine the collision partners and collision parameters in a deterministic propagator, and this is usually the computational bottleneck of MC ion implantation simulators. The standard search-for-target algorithm has been redesigned for computational efficiency and for economic usage of memory. Instead of searching for collision partners in a standard 29-atom crystal neighborhood identical to all ions, narrowed-down potential target lists are pre-computed based on the ion's relative position to a reference point as well as its direction of motion. The American National Standards Institution (ANSI) C++ standard container class bitset [1] is used to store such potential target lists, and the memory usage is very efficient. Combined with a quasi-simultaneous collision algorithm, the CPU times for MeV P and B implantation simulations are found to be reduced by more than a factor of two, rendering very reasonable computation times for MeV ion implantation simulations on standard workstations.