Monte Carlo Ion Implantation Simulator Research Articles

GPGPU MCII for high-energy implantation

In this paper, we develop a GPGPU acceleration methodology for the Binary-Collision-Approximation based Monte Carlo ion implantation simulation (MCII). Our proposed method avoids the branch-divergence issue which comes from the difference of material crystallinity for the structure with multiple materials. We also introduce an efficient scheme to mitigate the side effect for damage accumulation due to massive parallelization of simulation. Our demonstration of high energy implantation into CIS structure shows almost 40x speed-up compared to CPU implementation of MCII. We conclude that GPU-MCII is effective for acceleration of Monte Carlo simulations with high energy implantation e.g. deep photodiode or well isolation formation.

Comparison of Y–Ba–Cu–O Films Irradiated With Helium and Neon Ions for the Fabricationof Josephson Devices

We have irradiated high-quality YBa2Cu3O7-δ thin films with 90-keV helium and 175-keV neon ions to compare how ions of different mass affect electrical transport and reduce superconducting transition temperature. We measure the temperature dependence of the resistivity for films irradiated with different fluence and compare the results with Monte Carlo ion implantation simulations. We observe a smaller increase in resistivity for films irradiated with neon than those irradiated with helium for an equivalent reduction in transition temperature. We attribute this observation to nonuniform damage throughout the thickness of the film when irradiated with neon, as opposed to a more uniform damage profile in the case of helium irradiation.

A study of ion implantation into crystalline germanium

We report an experimental and simulation study for introducing dopant ions into (1 0 0) germanium in the energy range from 5 to 40 keV. The successful calibration of our Monte Carlo ion implantation simulator for amorphous and crystalline Ge targets is demonstrated by comparison of predicted boron profiles with SIMS data. The implantation profile in Ge is shallower than in Si for a given energy due to the larger nuclear and electronic stopping power of Ge atoms. The generated point defects and amorphized regions in the crystal are calculated by a modified Kinchin–Pease model. We found that the higher displacement energy in Ge, the stronger backscattering effect, and the smaller energy transfer from the ion to the primary recoil of a cascade are mainly responsible for the significantly reduced implantation damage in Ge. The point responses for boron implantations in Si and Ge are compared with each other. Finally the simulation results for the formation of interdigitated Ge-on-Si PIN-photodiodes are presented.

An algorithm for smoothing three-dimensional Monte Carlo ion implantation simulation results

We present an algorithm for smoothing results of three-dimensional Monte Carlo ion implantation simulations and translating them from the grid used for the Monte Carlo simulation to an arbitrary unstructured three-dimensional grid. This algorithm is important for joining various simulations of semiconductor manufacturing process steps, where data have to be smoothed or transferred from one grid to another. Furthermore different grids must be used since using ortho-grids is mandatory because of performance reasons for certain Monte Carlo simulation methods. The algorithm is based on approximations by generalized Bernstein polynomials. This approach was put on a mathematically sound basis by proving several properties of these polynomials. It does not suffer from the ill effects of least squares fits of polynomials of fixed degree as known from the popular response surface method. The smoothing algorithm which works very fast is described and in order to show its applicability, the results of smoothing a three-dimensional real world implantation example are given and compared with those of a least squares fit of a multivariate polynomial of degree 2, which yielded unusable results.

On smoothing three-dimensional monte carlo ion implantation simulation results

An algorithm for smoothing results of three-dimensional (3-D) Monte Carlo ion implantation simulations and translating them from the grid used for the Monte Carlo simulation to an arbitrary unstructured 3-D grid is presented. This algorithm is important for joining various process simulation steps, where data have to be smoothed or transferred from one grid to another. Furthermore, it is important for integrating the ion implantation simulator into a process flow. One reason for using different grids is that for certain Monte Carlo simulation methods, using orthogrids is mandatory because of performance reasons. The algorithm presented sweeps a small rectangular grid over the points of the new tetrahedral grid and uses approximation by generalized Bernstein polynomials. This approach was put on a mathematically sound basis by proving several properties of these polynomials. It does not suffer from the adverse effects of least squares fits of polynomials of fixed degree as known from the response surface method. The most important properties of Bernstein polynomials generalized to cuboid domains are presented, including uniform convergence, an asymptotic formula, and the variation diminishing property. The smoothing algorithm which works very fast is described and, in order to show its applicability, the resulting values of a 3-D real world implantation example are given and compared with those of a least squares fit of a multivariate polynomial of degree two, which yielded unusable results.

3D Atomistic Simulations of Submicron Device Fabrication

ABSTRACTWe use the Lattice Monte Carlo method to simulate the coupled motion of vacancies, interstitials and dopants during the annealing of a 50nm channel length NMOS structure. The initial defect locations are taken from Monte Carlo ion implantation simulations. The resulting defects diffuse, recombine, pair and cluster, with rates of these atomic processes calculated based on the local environment. Dopant redistribution occurs via both displacement by vacancies as well as the formation and diffusion of mobile boron and arsenic interstitials. We use these simulations to demonstrate the potential of atomistic simulations for deep submicron devices and to explore the influence of atomistic processes on macroscopic behavior.

A two-dimensional B implantation model for semiconductor process simulation environments

A computationally efficient semi-empirical model has been developed for modeling two-dimensional distributions of boron implanted into single-crystal silicon. This model accurately and efficiently models the depth profiles and lateral doping profiles under a masking edge for implantations as a function of dose, tilt angle, rotation angle, orientation of the masking edge, and masking layer thickness, in addition to energy. This new two-dimensional model is based on the dual-Pearson model [A.F. Tasch et al., J. Electrochem. Soc. 136 (1989) 810] for one-dimensional dopant depth distributions, which provides an accurate method of modeling the depth profile based on approximately 1000 SIMS profiles, and the UT-MARLOWE Monte Carlo ion implantation simulation code [K.M. Klein et al., IEEE Trans. Electron Devices ED-39 (1992) 1614], which provides well-modeled lateral dopant profiles. Combining depth profile and lateral profile information from these two models allows this new model to be both accurate and computationally efficient, making it suitable for use in semiconductor process modeling codes.