Sensitivity analysis and optimization of excimer laser ablation for microvia formation using neural networks and genetic algorithms

Higher levels of integration on the chip and package with increasing wiring density require highly repeatable and reliable microvias. To meet future needs of local via densities >5000 vias per cm/sup 2/ and via diameters of <50/spl mu/m, a scanning projection excimer laser ablation process is being developed for build-up and flex substrates. Vias with diameters of 30, 40, and 50 /spl mu/m are ablated in 257mu;m thick DuPont Kapton /spl reg/ E polyimide using an ANVIK HexScan/spl trade/ 2150 SXE excimer laser. A 25-1 fractional factorial experiment is conducted to determine the significance of laser fluence, shot frequency, number of pulses, and vertical and horizontal positions of the debris removal system in the laser tool in affecting ablated dielectric thickness, top via diameter, via wall angle, and via resistance. The complex nonlinear interactions between process set points and responses are empirically modelled using the feed-forward neural networks (NNs) employing the error back-propagation training algorithm. Neural networks encode the functional relationship between the process conditions and responses, and are then used to perform sensitivity analysis to quantify the variation in responses for incremental changes in particular process conditions. In addition, genetic algorithms (GAs) are used to identify optimized recipes for neural network response models. Experimental verification of the optimized recipes is performed to achieve completely open microvias, specific microvia diameters and wall angles, and low via resistance.