Effect of thickness of antimony selenide film on its photoelectric properties and microstructure

Antimony selenide(Sb2Se3) films are widely used in phase change memory and solar cells due to their stable switching effect and excellent photovoltaic properties. These properties of the films are affected by the film thickness. A method combining the advantages of Levenberg–Marquardt method and spectral fitting method(LM–SFM) is presented to study the dependence of refractive index(RI), absorption coefficient, optical band gap, Wemple–Di Domenico parameters, dielectric constant and optical electronegativity of the Sb2Se3films on their thickness. The results show that the RI and absorption coefficient of the Sb2Se3films increase with the increase of film thickness, while the optical band gap decreases with the increase of film thickness. Finally, the reasons why the optical and electrical properties of the film change with its thickness are explained by x-ray diffractometer(XRD), energy dispersive x-ray spectrometer(EDS), Mott–Davis state density model and Raman microstructure analysis.

Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

To effectively estimate the unknown aerodynamic parameters from the aircraft’s flight data,this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory(LSTM)network model and the Levenberg-Marquardt(LM)method.The stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input-output data of the aircraft system without requiring explicit postulation of the dynamics.The LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic parameters.The proposed method is applied by using the real flight data,generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle(UAV).The investigation reveals that for the two different flight data,the designed stacked LSTM network structure can maintain the efficacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters(i.e.,the initial weights,initial biases,number of hidden cells,time-steps,learning rate,and number of training iterations).Besides,the proposed method’s effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.