Self-assembly quantum dots growth prediction by quantum-inspired linear genetic programming

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.