Materials in small device structures underly certain parameter fluctuations as
much as bulk materials, but due to the small dimensions of the material regions,
the impact of the fluctuations is much higher as compared to bulk material. Hence,
fluctuations have to be considered from the beginning of the design. Even if an
optimal device has been designed, the characteristic after its fabrication
might be completely different.
In order to minimize these discrepancies, certain technology-specific
constraints have to be introduced which have to be considered within the
optimization frameworks to improve the characteristics.
To perform the optimization tasks the state-of-the-art simulation and
optimization framework Simulation Environment for Semiconductor Technology
Analysis (SIESTA) [44] is used and refined in this thesis
which provides an open interface that allows to easily add new software tools.
SIESTA can be used with several optimization strategies for specific
optimization tasks. The optimizer varies and proposes values for the unknown or
uncertain parameters.
The framework sends the parameters from the optimizer in an appropriate format
to the simulators. The simulator may be arranged in a
simulation tool flow where the output of one simulator is submitted as the input
to another simulation tool.
At the end of the simulation flow, the quality of the final simulation result is
determined by an objective function which returns a score value which is a
quantified representation of the quality of the simulation result. The
following presents typical applications in which optimization is used.
- Parameter extraction can be used to identify model parameters which
are not accurately known [47]. The required input data for
this task includes the simulation software with the appropriate models, as
well as measurements or reference data to which the simulation result can be
compared, and a score function (or objective function) that determines the quality of
the simulation result. This extraction mechanism uses the inverse modeling
technique [48,49,50], which is often performed to
characterize novel device structures and new materials as well as material
compositions in order to develop compact models at a specified scope.
- Calibration is a special case of the parameter
extraction [49]. The range of the uncertain parameters can be
further constrained which enables in general faster convergence to complete
the calibration task. The main difference between parameter extraction
and calibration is that calibration needs a much higher accuracy
because the initial guess is normally very close to the optimum, but should be
further improved, if for instance a sample has to be calibrated to a certain
set of measurements to minimize the model error.
Due to the higher quality demands, the determination of the quality of the
simulation result is a very critical issue for calibration.
These quality criteria (objective or score functions) have to be specified by
the user for each particular problem class and tuned for each individual
problem. This function can include comparisons of absolute and relative values
to calculate a significant metric to determine the quality of the simulation
result with respect to reference data.
- General optimization is the most general approach and can be used
for arbitrary purposes. The optimization is performed until a certain quality
criterion has been reached.
There exists a wide range of applications for the optimization related
to TCAD or electronic devices [51,52].
More general electronic design purposes have been discussed
in [53,54], and specific optimizers and application for other
regimes for instance in economics have been discussed in [55,56].
With a rigorous implementation of the major aspects occurring in a particular
setup problem the optimization framework is able to minimize or maximize certain
figures of merit within user-defined specifications. Hence, many trade-offs can
be optimized together to obtain a reasonable solutions for the specified problem.
Stefan Holzer
2007-11-19