TY - GEN
T1 - Self-assembly quantum dots growth prediction by quantum-inspired linear genetic programming
AU - Dias, Douglas Mota
AU - Singulani, Anderson Pires
AU - Pacheco, Marco Aurélio C.
AU - De Souza, Patrcia Lustoza
AU - Pires, Mauricio Pamplona
AU - Neto, Omar Paranaiba Vilela
PY - 2011
Y1 - 2011
N2 - In this work we present the application of quantum inspired linear genetic programming (QILGP) to the growth of self-assembled quantum dots. Quantum inspired linear genetic programming is a novel model to evolve machine code programs exploiting quantum mechanics principles. Quantum dots are nanostructures that have been widely applied to optoelectronics devices. The method proposed here relies on an existing database of growth parameters with a resulting quantum dot characteristic to be able to later obtain the growth parameters needed to reach a specific value for such a quantum dot characteristic. The computational techniques were used to associate the growth input parameters with the mean height of the deposited quantum dots. Trends of the quantum dot mean height behavior as a function of growth parameters were correctly predicted, improving on the results obtained by artificial neural network and classical genetic programming.
AB - In this work we present the application of quantum inspired linear genetic programming (QILGP) to the growth of self-assembled quantum dots. Quantum inspired linear genetic programming is a novel model to evolve machine code programs exploiting quantum mechanics principles. Quantum dots are nanostructures that have been widely applied to optoelectronics devices. The method proposed here relies on an existing database of growth parameters with a resulting quantum dot characteristic to be able to later obtain the growth parameters needed to reach a specific value for such a quantum dot characteristic. The computational techniques were used to associate the growth input parameters with the mean height of the deposited quantum dots. Trends of the quantum dot mean height behavior as a function of growth parameters were correctly predicted, improving on the results obtained by artificial neural network and classical genetic programming.
KW - Computational Nanotechnology
KW - Growth Prediction
KW - Quantum Dots
KW - Quantum Inspired Linear Genetic Programming
UR - https://www.scopus.com/pages/publications/80051992343
U2 - 10.1109/CEC.2011.5949871
DO - 10.1109/CEC.2011.5949871
M3 - Conference contribution
AN - SCOPUS:80051992343
SN - 9781424478347
T3 - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
SP - 2075
EP - 2082
BT - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
PB - IEEE Computer Society
ER -