Neural network modeling with confidence bounds: a case study on the solder paste deposition process

The formation of reliable solder joints in electronic assemblies is a critical issue in surface mount manufacturing. Stringent control is placed on the solder paste deposition process to minimize soldering defects and achieve high assembly yield. Time series process modeling of the solder paste quality characteristics using neural networks (NN) is a promising approach that complements traditional control charting schemes deployed on-line. We present the study of building a multilayer feedforward neural network for monitoring the solder paste deposition process performance. Modeling via neural networks provides not only useful insights in the process dynamics, it also allows forecasts of future process behavior to be made. Data measurements collected on ball grid array (BGA) and quad flat pack (QFP) packages are used to illustrate the NN technique and the forecast accuracies of the models are summarized. Furthermore, in order to quantify the errors associated with the forecasted point estimates, asymptotically valid prediction intervals are computed using nonlinear regression. Simulation results showed that the prediction intervals constructed give reasonably satisfactory coverage percentages as compared to the nominal confidence levels. Process control using NN with confidence bounds provides more quality information on the performance of the deposition process for better decision making and continuous improvement