Premium
Continuum simulation of solid phase epitaxial regrowth of amorphized silicon including most advanced physical interactions
Author(s) -
Delalleau Julien,
Pakfar Ardechir,
Bazizi ElMedhi,
Simola Roberto,
Tavernier Clement
Publication year - 2011
Publication title -
physica status solidi (a)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.532
H-Index - 104
eISSN - 1862-6319
pISSN - 1862-6300
DOI - 10.1002/pssa.201000228
Subject(s) - materials science , epitaxy , dopant , silicon , recrystallization (geology) , ion implantation , optoelectronics , dopant activation , fabrication , amorphous solid , doping , electronic engineering , nanotechnology , crystallography , ion , chemistry , engineering , medicine , paleontology , alternative medicine , organic chemistry , layer (electronics) , pathology , biology
Solid‐phase‐epitaxial regrowth (SPER) of Si amorphized by ion implantation is considered as a potential solution for the fabrication of highly‐activated ultra‐shallow junctions for future technology nodes of Si CMOS devices. In the frame of 32 and 22 nm technologies node development, SPER occurs after amorphizing implantations used in source/drain regions. To get an accurate simulation of dopant activation and junction depth position, a suitable continuum SPER model, implemented into a commercial simulator, is now mandatory. This TCAD model must consider the different physical effects associated with SPER: silicon regrowth rate, dopants redistribution snow plough effect, and interaction with silicon point defects. In this work, using a previously established model, we have implemented an improved physically based model for SPER and, several formulations have been developed to enable a robust/accurate modeling of the recrystallization velocity. It takes into account the direct interaction between amorphous/crystalline interface kinetics and point defects, and a regrowth rate dependent on temperature. Simulation results of dopant concentration profiles are in good agreement with experimental data and can provide important insight for optimizing the bulk silicon process as well in one dimension as two dimensions.