An extensible TCAD optimization framework combining gradient based and genetic optimizers

Abstract

The siesta framework is an extensible tool for optimization and inverse modeling of semiconductor devices including dynamic load balancing, for taking advantage of several, loosely connected workstations. Two gradient-based and two evolutionary computation optimizers are currently available through a uniform interface and can be combined at will. At a real world inverse modeling example, we demonstrate that evolutionary computation optimizers provide several advantages over gradient-based optimizers, due to the specific properties of the objective functions in TCAD applications. Furthermore, we shortly discuss some issues arising in inverse modeling and conclude with a comparison of gradient-based and evolutionary computation optimizers from a TCAD point of view.