Pitchfork and Hopf bifurcations in quantum dot light emitting diode: analysis and prediction by using artificial neural network

Abstract

The analytical and numerical analyses as well as prediction with artificial neural network (ANN) for chaos-based artificial intelligence applications of quantum dot light emitting diode (QDLED) are investigated in this paper. The system of equations describing QDLED has three, or one equilibrium points depending on the capture rate from wetting layer into the dot and the injection current. The stability analysis of the equilibrium points reveals the existence of Pitchfork and Hopf bifurcations. The different dynamical behaviors (including steady state, periodic and chaotic behaviors) found in QDLED are illustrated in two parameters bifurcation diagrams, phase portraits and time series. Finaly, the QDLED system is predicted using ANN for chaos-based artificial intelligence applications.