Combining Statistics and AI in the Optimization of Semiconductors for Solar Cells

Jörg Risius, Günter Seidelmann
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:469-475, 1995.

Abstract

In the framework of a research project in photovoltaics, a flexible method of parameter optimization is developed. Target of the optimization is the quality of semiconductor materials for solar cells. The quality depends on the parameter values chosen for the semiconductor production process. The optimization method is based on the combined application of statistics and artificial intelligence. Experiment design is used to collect and analyze experimental data from the process in order to acquire knowledge about the relationship between parameter values and semiconductor quality. Classifiers built by machine learning algorithms help to determine semiconductor quality by the inspection of special signals obtainable from the running process. A final on-line hillclimbing search for optimal parameter values is guided by both the classifier and the knowledge about process behaviour derived from previous experiments.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-risius95a, title = {Combining Statistics and AI in the Optimization of Semiconductors for Solar Cells}, author = {Risius, J\"org and Seidelmann, G\"unter}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {469--475}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/risius95a/risius95a.pdf}, url = {https://proceedings.mlr.press/r0/risius95a.html}, abstract = {In the framework of a research project in photovoltaics, a flexible method of parameter optimization is developed. Target of the optimization is the quality of semiconductor materials for solar cells. The quality depends on the parameter values chosen for the semiconductor production process. The optimization method is based on the combined application of statistics and artificial intelligence. Experiment design is used to collect and analyze experimental data from the process in order to acquire knowledge about the relationship between parameter values and semiconductor quality. Classifiers built by machine learning algorithms help to determine semiconductor quality by the inspection of special signals obtainable from the running process. A final on-line hillclimbing search for optimal parameter values is guided by both the classifier and the knowledge about process behaviour derived from previous experiments.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Combining Statistics and AI in the Optimization of Semiconductors for Solar Cells %A Jörg Risius %A Günter Seidelmann %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-risius95a %I PMLR %P 469--475 %U https://proceedings.mlr.press/r0/risius95a.html %V R0 %X In the framework of a research project in photovoltaics, a flexible method of parameter optimization is developed. Target of the optimization is the quality of semiconductor materials for solar cells. The quality depends on the parameter values chosen for the semiconductor production process. The optimization method is based on the combined application of statistics and artificial intelligence. Experiment design is used to collect and analyze experimental data from the process in order to acquire knowledge about the relationship between parameter values and semiconductor quality. Classifiers built by machine learning algorithms help to determine semiconductor quality by the inspection of special signals obtainable from the running process. A final on-line hillclimbing search for optimal parameter values is guided by both the classifier and the knowledge about process behaviour derived from previous experiments. %Z Reissued by PMLR on 01 May 2022.
APA
Risius, J. & Seidelmann, G.. (1995). Combining Statistics and AI in the Optimization of Semiconductors for Solar Cells. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:469-475 Available from https://proceedings.mlr.press/r0/risius95a.html. Reissued by PMLR on 01 May 2022.

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