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.
Original language | English (US) |
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Pages (from-to) | 61-68 |
Number of pages | 8 |
Journal | Microelectronics Journal |
Volume | 33 |
Issue number | 1-2 |
DOIs | |
State | Published - Jan 2 2002 |
Externally published | Yes |
Keywords
- Evolutionary computation
- Genetic optimization
- Gradient based optimization
- Inverse modeling
- Optimization
- TCAD
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Condensed Matter Physics
- Surfaces, Coatings and Films
- Electrical and Electronic Engineering