For the drive current optimizations following the two-dimensional optimization
approach 62 optimization parameters are used to describe the doping profile as
shown earlier in Fig. 3.4. Each of these parameters represents
the logarithm of the doping value at one of the optimization grid points. For
the constant background substrate doping
a value of
10
cm
is used.
In Listing 4.5 the parts of the device generation sub-model
mkdev.mod are shown which define the substrate doping parameter
Nsub as well as the initial values and bounds of the optimization
parameters for Device Generation A and Device Generation B. The doping
parameter K06_01, for example, belongs to the grid point in row 6,
column 1 of the grid shown in Fig. 3.4. The optimization starts
with a uniformly doped inverted-T region with an acceptor doping of
10 cm
for Device Generation A and 10
cm
for
Device Generation B. The allowed range for each parameter lies within
10
cm
and 10
cm
.
The Knots section in the Makedevice input deck is given in
Listing 4.6. The doping parameters are given in a matrix
K which defines the doping in a rectangular box at the semiconductor
surface. The inverted-T region is embedded in this rectangle. Matrix entries
outside the inverted-T region are set equal to the substrate doping
.
Fig. 4.3 shows the improvement in drive current
during the
optimization procedure for both device generations. The values are taken at
each temporary minimum which is the last evaluation step before a gradient
calculation as shown earlier in Fig. 3.8. The constraint was
fulfilled at any time keeping the leakage current at 1 pA. It should be noted
that the superior drive current of Device Generation A results from the higher
supply voltage of 1.5 V compared to 0.9 V of Device Generation B.
During the whole optimization procedure more than 5000 device simulations had to be performed, most of them were gradient steps and took only a few seconds due to the very good initial solution provided. Anyway, an optimization like this with such a very large number of optimization parameters and simulation jobs claims considerable computational resources which can only be provided by a workstation cluster of high-speed machines. Assuming that, one optimization procedure like this can be finished within a few hours up to one day.
Fig. 4.4 and Fig. 4.5 show the acceptor doping profiles as results of the two-dimensional optimization approach for Device Generation A and Device Generation B, respectively. These doping profiles look quite complex due to the numerical nature of the optimization procedure. The optimizer just handles a black box with input parameters delivering the target and the constraint (see Fig. 3.1). As a matter of fact, the influence of each parameter on the performance of the device varies, some have more influence, the others less. Therefore, parameters with very little influence can end up with almost arbitrary values.