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A vertical chemical vapor deposition process (CVD)
optimization method using modified geneticalgorithms (MGA) has
been proposed. Genetic algorithms (GA) are a computational
optimization paradigm modeled after biological evolution
concept. Strategies such as: elitist with ranking selection
reproduction scheme and multiple points crossover are used to
raise the search efficiency of the traditional GA. Self-adjusted
operator probability not only helps to avoid premature but also
define parameters automatically. Moreover, we integrate hybrid
genetic operator, immigration operator, and heuristic fitness
function to enhance its local fine tuning ability. In order to
prove the improvement results, we initially optimize several
highly nonlinear functions with MGA, then, with a well-defined
fitness function, the optimization procedure has been
successfully applied to the CVD process with various noise
level. Through the optimal solution, we obtained the thickness
in deposition layers which is more uniformly distributed over
the wafers. These results demonstrate the superiority of the
proposed optimization solution in comparison with other existing
optimization algorithms.
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