Neural network approach to modeling hot intrusion process for micromold fabrication

The rapid fabrication of polymeric mold masters by laser micromachining and hot-intrusion permits the low cost manufacture of microfluidic devices with near optical quality surface finishes. A metallic hot intrusion mask with the desired microfeatures is first machined by laser and then used to produce the mold master by pressing the mask onto a polymethylmethacrylate (PMMA) substrate under applied heat and pressure. A thorough understanding of the physical phenomenon is required to produce features with high dimensional accuracy. A neural network approach to modeling the relationship among microchannel height (H), width (W), the intrusion process parameters of pressure and temperature is described in this paper. Experimentally acquired data are used to both train and test the neural network for parameterselection. Analysis of the preliminary results shows that the modeling methodology can predict suitable parameters within 6% error.Peer reviewed: YesNRC publication: Ye