**Hints & Tips**

**Q:** How is implant damage
enhanced diffusion modeled by ATHENA?

Which tuning parameters should be used for matching experimental
results?

**A:** The effect of implant
damage enhanced diffusion is important in many technologies. Typical
cases are the source and drain diffusion in MOSFETs and the emitter
diffusion in bipolar devices. Damage generated by implantation leads
to an enhancement to the diffusion of these dopants during subsequent
heat cycles.

Simulation of the enhanced diffusion
effects are divided between two processes. Firstly ATHENA must simulate
the implant damage generated by a given implant and secondly it
must model the effect that these defects have on subsequent impurity
diffusion.

ATHENA considers implant damage as
point defect generation. Point defects are silicon interstitials
and lattice vacancies that are created as energetic implanted ions
collide with silicon lattice atoms.

The most practical model for coupling
implant damage to subsequent diffusion calculations is the +1 model.
In its simplest form, the +1 model adds exactly one interstitial
for each implanted ion. This is a reasonable approximation if one
assumes that the vacancies and interstitials created by the implant
recombine quickly relative to the timescale needed to produce significant
diffusion. This leaves one extra interstitial for each ion (assuming
the implanted ion has replaced it on the lattice).

This model is applicable to
both Monte Carlo and the default analytic implants and can be invoked
by including the `UNIT.DAM`

parameter
on the IMPLANT statement. A commonly applied variation to this model
is to scale the number of generated interstitials. In ATHENA, this
can be done using the parameter `DAM.FACT`

on
the IMPLANT statement. A corresponding profile of lattice vacancies
is introduced in this model with the maximum of zero and
(`1-DAM.FACT`

) times the implanted
ion profile.

The diffusion models that will
include the effect of the point defects are either the `TWO.DIM`

or `FULL.CPL`

models.
Both models include the local point defect concentration in the
diffusion rate of the dopants. Both interstitials and vacancies
diffuse quickly compared with dopant ions. The point defects also
recombine as the implant damage is annealed out.

When it comes to tuning to match
measured doping profiles, two approaches are possible. Either the
damage during implant or the diffusion effect of the point defects
could be used. The amount of point defects generated during an implant
is extremely difficult to measure. Similarly the model parameters
for both diffusion and recombination rates for point defects are
uncertain. All are areas of current academic research.

Typically the most effective tuning parameter
in this type of simulation is the `DAM.FACT`

value
itself. Figure 1 shows how fairly small changes in this parameter affect the
doping profile. A value of 0.01 is typical. An ATHENA implant statement for
an MOS source/drain might be:

`IMPLANT ARSENIC DOSE=3.0E15 ENERGY=60 \`

`UNIT.DAMAGE DAM.FACT=0.01`

Figure 1. Variations in diffusion due to tuning of DAM.FACT parameter.

Figure 2 illustrates how the damage produced
by source drain implants affects the center of a MOS transistor with varying
gate length. For shorter gate length devices the damage at the source drain
area produces additional diffusion in the center that is not seen for longer
channel devices. This phenomenon explains some of the reverse short channel
effects seen in certain processes.

Figure 2. Enhanced diffusion of MOS channel profile.