Case Studies and White Papers
Trimming the FAT out of Experimental Methods
This introductory article provides compelling reasons to abandon traditional scientific methods that deploy only one factor at a time (OFAT) in favor of multifactor testing techniques known as design of experiments (DOE). Only via DOE can experimenters detect interactions, which often prove to be the key to success.
Reducing 300mm Wafer Coating Defects without Compromising Uniformity
Via a sequential strategy of experimentation using factorial DOE and response surface methods (RSM), engineers at Brewer Science, a microelectronic materials manufacturer, made breakthrough quality improvements in the production of their 300 mm wafers.
PCR Process Optimized via Split-Plot DOE
This paper illustrates the use of design of experiments (DOE) and split-plot design to quickly and effectively determine the factor settings that maximize amplification in a polymerase chain reaction (PCR) experiment.
Design of Experiments Training Banishes Purple-Paper Blues
A paper mill used DOE to pre-define the appearance and shade of paper before full production. They discovered an unexpected interaction takes of a new blue-dye with coating formulation containing a starch binder. It turned the paper purple, which did not happen with a latex binder. This episode formed the basis for a DOE-training exercise.
An innovative blend of hardware, software and the right training in statistical know-how supercharges research automation.
Engineers use mixture design to create thinner, whiter keypad backlighting. A slightly different version was published in Product Design & Development.
DOE is used in this study demonstrating the versatile compatibility of a unique polyester polyol, PD-90 LV, with both polyester and polyether polyols.
Proper application of solder paste onto a PCb is essential in surface mount assemblies. In this case study DOE was used to understand this process, and ultimately to improve electrical yields.
Engineers solved two perplexing wafer production mysteries using design of experiments (DOE), saving the company $180,000 a year.
How Experimental Design Optimizes Assay Optimization
Optimizing biological assay conditions is a demanding process that scientists face daily. The requirement is to develop high-quality, robust assays that work across a range of biological conditions. The demand is to do this within a short time frame. To overcome these obstacles, automated assay optimization (AAO) systems often are used to accommodate large numbers of samples. Applying DOE to AAO is essential to make the best use of this high-tech equipment.