Modeling and optimization of process parameters for GaAs/AlGaAs multiple quantum well avalanche photodiodes using genetic algorithms

Kim, Eui Seung; Oh, Changhoon; Lee, Seogoo; Lee, Bongyong; Yun, Ilgu

doi:10.1016/s0026-2692(01)00037-4

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Evolutionary methodology is ubiquitous from traditional mineral processing [67][68][69] to state of the art VLSI circuits, 70,71 involving perhaps materials of every possible kind. Some typical examples would include processing of primary metals, 9 ceramics, 72 rubbers 73 and polymers in general, 74 composites, 75 electronic materials, 76,77 and paper, 78 as well as devices such as electrochemical cells. 79 Even the processing aspects of futuristic carbon nanotubes have been the subject of a recent investigation; 80 although the design aspect of this material, as indicated before, still lacks a rigorous GAs based exploitation.…”

Section: Genetic Algorithms In Materials Processingmentioning

confidence: 99%

Genetic algorithms in materials design and processing

Chakraborti¹

2004

International Materials Reviews

195

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Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection. They are highly robust and efficient for most engineering optimising studies. Although a late entrant in the materials arena, GAs based studies are increasingly making their presence felt in many different aspects of this discipline. In recent times, GAs have been successfully used in numerous problems in the areas of atomistic material design, alloy design, polymer processing, powder compaction and sintering, ferrous production metallurgy, continuous casting, metal rolling, metal cutting, welding, and so on. The present review attempts to present the state of the art in this area. It includes three broad sections given as: fundamentals of genetic algorithms, genetic algorithms in materials design, and genetic algorithms in materials processing. The first section provides the reader with the basic concepts and the intricacies associated with this novel technique. The following section presents a detailed account of the usage of GAs to design various materials, predominantly at the atomic level. The third section aims to capture the process of applicability of GAs in numerous materials processing operations. A thorough literature search and critical analysis of the research conducted so far is provided, and attempts have been made to demonstrate how one single methodology can be utilised to study virtually every area of the vast materials discipline.

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Section: Genetic Algorithms In Materials Processingmentioning

confidence: 99%

Genetic algorithms in materials design and processing

Chakraborti¹

2004

International Materials Reviews

195

View full text Add to dashboard Cite

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A review of data mining applications for quality improvement in manufacturing industry

Köksal

Batmaz

Testik

2011

Expert Systems with Applications

370

188

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Quantum Genetic Algorithm Method in Self-Consistent Electronic Structure Calculations of a Quantum Dot With Many Electrons

Şahin

Atav

Tomak

2005

Int. J. Mod. Phys. C

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In this study, we have calculated energy levels of an N-electron quantum dot. For this purpose, we have used two different techniques, matrix diagonalization and quantum genetic algorithm, to obtain simultaneous solutions of the coupled Schrödinger and Poisson equation in the Hartree approximation. We have determined single particle energy levels, total energy, chemical potential and capacitive energy. We have also compared the results, demonstrated the applicability of QGA to many-electron quantum systems and evaluated its effectiveness.

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Modeling and optimization of process parameters for GaAs/AlGaAs multiple quantum well avalanche photodiodes using genetic algorithms

Cited by 4 publications

References 4 publications

Genetic algorithms in materials design and processing

Genetic algorithms in materials design and processing

A review of data mining applications for quality improvement in manufacturing industry

Quantum Genetic Algorithm Method in Self-Consistent Electronic Structure Calculations of a Quantum Dot With Many Electrons

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