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Ion implantation is a standard technology to dope substrates in Si VLSI processes. The ion implantation profiles in Si substrates are generated based on a vast secondary ion mass spectrometry (SIMS) database of ion implantation profiles in commercial simulators such as Sentaurus Process. However, we cannot predict profiles accurately when there are no experimental data or only poor data. Recently, various ions such as C, N, and F have been used to suppress transient enhanced diffusion in the subsequent annealing processes, as shown by Hu (2000) and Mirabera (2005). Furthermore, various substrates have been investigated, such as SiGe for state of the art Si LSI [Kim (2006), Weber (2007)], Ge for post Si devices [Chui (2002), Shang (2003)], and SiC for high-temperature, high-voltage, and highpower applicants [Schoerner (1999)]. Ion implantation is also a standard technology to dope these substrates. However, accumulation of the corresponding experimental database is time and cost consuming. Therefore, theoretical evaluation of these profiles is invoked. Monte Carlo (MC) simulation is widely used for predicting ion implantation profiles and has long been developed to be available for any combination of incident ion and substrate atoms [Ziegler (2008) SRIM, Tian (2003), Suzuki (2009)]. We can evaluate the accuracy of the MC by comparing an ion implantation database such as FabMeister-IM [Suzuki (2010a)]. We show that the MC results sometimes deviate from the experimental data with its original form. We modified the electron stopping power model, calibrated its parameters, and reproduced most of the database with the energy range between 0.5 and 2000 keV. MC simulation takes long time to calculate the profiles, and the profiles are scattered in the low concentration region. Therefore, MC results are sometimes converted to an analytical Pearson function presented by Hofker (1975) and Ashworth (1990) utilizing its moment parameters, with which we can expect smooth profiles over the entire region. Furthermore, we can use the moment parameters of MC as a database and can instantaneously obtain profiles using the Pearson function for various ion implantation conditions by interpolating the moment parameter values. However, we show that direct use of the moment parameters evaluated from MC data sometimes induces inaccurate Pearson function, as shown in

Monte Carlo Simulation for Ion Implantation Profiles, Amorphous Layer Thickness Formed by the Ion Implantation, and Database Based on Pearson Function