Stage-based Neural Network for Reflow Profile Prediction and Reflow Recipe Optimization for Quality and Energy Saving

Abstract During the reflow process, solder joints are formed on the boards with the placed components, so the temperature settings in the reflow oven chamber are vital to the quality of the PCB. Inappropriate profiles cause various defects such as cracks, bridging, delamination, etc. Solder paste manufacturers have generally provided the ideal thermal profile (i.e., target profile), and PCB manufacturers have attempted to meet the given profile by fine-tuning the oven’s recipe. The conventional method tunes the recipe to gather thermal data with a thermal measurement device. It adjusts the profile, which relies on the trial-and-error method which takes much time and effort. This paper proposes (1) a recipe initialization method for determining the initial recipe for collecting training data, (2) a stage-based (ramp, soak, and reflow) input data segmentation method for data preprocessing, (3) a backpropagation neural network, (BPNN) model for predicting the required zone temperature to reduce the gap between the actual processing profile and the target profile, (4) a mixed-integer linear programming (MILP) algorithm for generates the optimal recipe to minimize the temperature settings. This paper aims to enable non-contact prediction of required air temperature from one experiment. The MILP optimization model utilized the constraints of the upper and lower bounds obtained from the prediction result. The model has been cross-validated with different initial recipes and different target profiles. As a result, within 10 minutes of starting the experiment, the generated optimal recipe improved the fitness to the targeted profile by 4.2%, which resulted in 99% and, in the meanwhile, lowered the energy cost by 23%.