In this paper, we present a statistical-neural network modeling approach to process optimization of fine pitch stencil printing for solder paste deposition on pads of printed circuit boards (PCB). The overall objective was to determine the optimum settings of the design parameters that would result in minimum solder paste height variation for the new board designs with 20-mil, 25-mil, and 50-mil pitch pad patterns. As a first step, a Taguchi orthogonal array, L27, was designed to capture the main effects of the six important printing machinery parameters and the PCBs pad conditions. Some of their interactions were also included. Fifty-four experimental runs (two per setting) were conducted. These data were then used to construct neural network models relating the desired quality characteristics to the input design parameters. Our modular approach was used to select the appropriate architecture for these models. These models in conjunction with the gradient descent algorithm enabled us to determine the optimum settings for minimum solder paste height variation. Confirming experiments on the production line validated the optimum settings predicted by the model. In addition to the combination of all the three pad patterns, i.e., 20, 25, and 50 mil pitch pads, we also built neural network models for individual and dual combinations of the three pad patterns. The simulations indicate different optimum settings for different pad pattern combinations.

Issue Section:
Technical Papers
Topics:
Modeling, Optimization, Printing, Solders, Design, Neural network models, Algorithms, Assembly lines, Engineering simulation, Machinery, Printed circuit boards, Simulation
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