A Qualitative Study on Global and Local Optimization Techniques for TCAD Analysis Tasks
نویسندگان
چکیده
We compare the two well-known global optimization methods, simulated annealing and genetic optimization, to a local gradient-based optimization technique. We rate the applicability of each method in terms of the minimal achievable target value for a given number of simulation runs in an inverse modeling application. The gradient-based optimizer used in the experiment is based on the Levenberg-Marquardt algorithm. The actual implementation (lmmin) was taken from MINPACK [1]. The genetic optimizer (genopt) is based on GALIB [2]. For the simulated annealing [3] optimizer (siman) an implementation by L. Ingber was taken. All optimizers are capable of evaluating several targets in parallel.
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