Characterization and Optimization of a Wave-Soldering Process

Abstract

A case study for improving the quality of a wave-soldering process that produced printed circuit boards (PCB's) is presented. A mixed-level fractional factorial design was implemented in a high-volume production system during normal operational hours. The observed ordered-categorical data from the bottom-side soldered leads were weighted to formulate the average, spatial uniformity, and dispersion process performance measurements. For lead classes like the integrated circuit and printed grid array, a polynomial model was established using the least squares method with weights provided by a dispersion function. The main-effect and interaction model terms were selected by forward and all-subsets regressions. Production quotas, topside defects, presoldering board temperature, and variance models were used to set the constraints for simultaneously optimizing predictions from the average and the uniformity models of all leads. A nonlinear optimization routine was used to determine the best and most robust settings for the continuous and discrete process variables. Results from a confirmatory experiment showed an improvement of mean soldering quality by 33% and of uniformity by 39%.

Achieving Uniformity in a Semiconductor Fabrication Process Using Spatial Modeling

“…The usefulness of the mean model will be in assessing the achievement of target, as well as uniformity. The variance model is useful for measuring repeatability of the process (Mesenbrink, Lu, McKenzie and Taheri (1994); Davidian and Carroll (1987)). The correlation model affects target, uniformity, and repeatability.…”

“…Moreover, based on Taguchi's (1987) robust parameter design philosophy, one would like the quality of work on a wafer to be repeatable from wafer to wafer. To handle this multiobjective optimization problem, Mesenbrink, Lu, McKenzie and Taheri (1994), in a soldering process optimization case study, formulate summary statistics (e.g., mean response, uniformity, and their associated variances), fit models to these summary statistics, and then compare predictions to find the best process condition to achieve quality goals. The use of summary statistics (especially • f the SN ratio), however, can result in loss of information and is not flexible when the quality goal is changed, say, to look for maximum yield in a certain region.…”

Combined Generalized Linear Modelling-Non-Linear Programming Approach to Robust Process Design-A Case-Study in Circuit Board Quality Improvement

“…Linking these process subresponses to quality characteristics can be difficult and time consuming (Brinkley and Digiacomo, 1994). Mesenbrink et al (1994) used an ordered categorical quality rating of individual solder joints as a quasi-normal response for soldering process optimization. The use of automated vision systems for circuit board inspection allows the easy collection of attribute data and provided the source of the data used for this study.…”

Fractional Design Wizard: A computer program for cost-effective experimental research design

“…When a new product is made, there are many unknown possible shortcomings in the production process. Mesenbrink and Lu (1994), who studied the quality of a wavesoldering process, gave an example of such a study. In the The computer program Fractional Design Wizard creates fractional factorial designs that are costeffective and especially useful for discarding irrelevant factors from a large number of possible candidates.…”