Semi-empirical neural network modeling of metal-organic chemical vapor deposition

Metal-organic chemical vapor deposition (MOCVD) is an important technique for growing thin films with various applications in electronics and optics. The development of accurate and efficient MOCVD process models is therefore desirable, since such models ran be instrumental in improving process control in a manufacturing environment. This paper presents a semi-empirical MOCVD model based on "hybrid" neural networks. The model is constructed by characterizing the MOCVD of titanium dioxide (TiO/sub 2/) films through the measurement of deposition rate over a range of deposition conditions by a statistically designed experiment in which susceptor and source temperature, flow rate of the carrier gas for the precursor and chamber pressure are varied. A modified backpropagation neural network is then trained on the experimental data to determine the value of the adjustable parameters in an analytical expression for the TiO/sub 2/ deposition rate. In so doing, a general purpose methodology for deriving semi-empirical neural process models which take into account prior knowledge of the underlying process physics is developed.