(Physicist, MBA)

- Evolutionary Optimization

- Design Parameter Optimization (DesParO)

- Investigation of hot electrons by Evolutionary
Algorithms

## Evolutionary Optimization

The idea of Evolutionary Optimization Algorithms (EA) is inspired by the evolution processes that take place in nature, where sucessfull species must adapt continuously to the changing enviroment by creating new individuals and by selectio of the fittest. This natural optimization technique can easily be transfered to mathematical optimization problems. Theobjective function F on a search space S can be interpreted as the environment and each vector v is a possible individuum in this environment. The fitness of these mathematical individuals is given by the objective function F(v). The EA searches for the optimal solution, by starting with an initial set of vectors called "population". In each following iteration (generation) a new set of vectors is created from the old population by modifying the fittest of the old vectors. For the new population of the next generation the fittest among the newly created and old vectors are selected.

I started working with Evolutionary Algorithms during the diploma thesis for my stdy of buissnes and administration (Betriebswirtschaftslehre) at the comprehensive Unniversity in Hagen. Here I developed an object oriented C++-libary for EAs.

## Design Parameter Optimization (DesParO)

Computer simulation is an important tool in modern product development. New prototypes of products are designed, improved and tested using computers before a first real prototype is build. In this process different geometries, materials and process parameters are changed by hand until a sufficient solution was found. DesParO provides the possibility to optimise these design parameters using the computer. DesParO is an optimisation toolbox especially design for industrial simulation. The toolbox contains a collection of efficient algorthims for the computer based optimisation and can easily be adopted to any industrial simulation code. Consulting by the Fraunhofer institute SCAI ensures an efficient use of the toolbox. DesParO was especially designed for computational expensive simulation codes and supports parallel computation.

### DesParO-Homepage

## Investigation of hot electrons with the aid of Evolutionary Algorithms

By means of Evolutionary Algorithms, a class of robust optimization techniques used in Operations Research, it is possible to backward calculate electron distributions from measurement results.**HgCdTe**The observation of EEW in HgCdTe makes it possible to investigate the electric field distribution of electrons in this material for T<30 K. EAs revealed that in order to achieve energy balance in moderate electric fields, the electron distribution differs slightly from a Fermi distribution.

-*Genetic algorithms: A new approach to energy balance equations*, J. Jakumeit, Appl. Phys. Lett. 66, 1812, 1995**Silizium (Si-MOSFETs)**In collaboration with the group of Prof. K. Hess and Prof. U. Ravaioli at the Beckman Institut of the University of Illinois ( National Center for Computational Electronics) the EAs were used to investigate the distribution of hot electrons in silicon. The object was to calculate sustrate and gate currents in Si-MOSFETs. With help of a physical mutation operator, which is based on the Monte-Carlo technique, results comparable to full band calculations could be obtained.

-*Calculation of hot electron distributions in silicon by means of an Evolutionary Algorithm*, J. Jakumeit, U.~Ravaioli, K.~Hess, J. Appl. Phys., Okt. 96, (postscript, gezipt, 153kb)

-*Evolutionary algorithms for the calculation of electron distributions in Si-MOSFETs*, J, Jakumeit, Proceedings of the IV. International Conference on Parallel Problem Solving from Nature (Berlin 1996), Lecture Notes in Computer Science 1141, Springer Verlag, 1996, S. 819 (postscript, gezipt, 186kb)