Process modeling of polymeric optical waveguide using artificial neural network

Polymeric optical waveguides have recently drawn a great interest in the fabrication of many functional optical devices. Photolithography is the widely used technique for the fabrication of such waveguides. To facilitate low-cost manufacturing and to achieve high reproducibility, determination and control of the process parameter in the different fabrication steps is very important. However, the mathematical modeling of waveguide fabrication process is quite difficult due to complex physical/chemical sub-processes involve in the lithography process. In this work, waveguide fabrication using photolithography has been modeled using artificial neural network which is capable to extract a simple and accurate empirical model of a complex nonlinear process. This modeling scheme predicts the value of the parameters required in the lithography process including spin speed and time, expose dose, develop time, power and pressure, while the geometry of the waveguide that includes film thickness and wafer size is predefined. The modeling results are compared with the experimental results and found a good experiment between them. Such agreement indicates that the modeling approach will be very useful for the process optimization and supervisory process control scheme.