Advanced Model Fitting and Parameter Extraction The quickening pace of business is forcing the product development process to make time a critical consideration together with cost and requirements. To optimize the investment in experiments and testing both with respect to cost but more importantly in terms of time, it is critical to extract the maximum information and knowledge from the data that is encompassed within a strategy that retires technical risk most quickly. Beginning with a clear set of success criteria and metrics that are rooted in defined business objectives for the test both with respect to the immediate goals, but also keeping in mind future, potential uses and follow-on experiments. The data and the tests should support and enable clear decisions and actions. To those ends, advanced experimental design and data analysis services are available with the support of domain expertise in materials science in development of new products and processes in a broad range of products and applications. Engagements have included new product development, device qualification and reliability risk assessment, process failure recovery, and manufacturability improvements. These services are delivered with two major goals: Drive your development faster Develop/mentor your staff in advanced data analytical techniques and frameworks Example Analytics Services include: Statistical Design of Experiments (DOE) Exploratory Data Analysis Statistical Process Control Custom Data Extraction Software include: Advanced Model Fitting and Parameter Extraction Image Quantification and Analysis Analysis Automation Experimental Process The experimental process drives to reduce the development time/risk in two ways: First is to get the maximum information from the test and we do this by combining them together and then identifying the underlying governing reactions. Many times, researchers only look at one experiment at a time and this limits their ability to see the big picture sooner. Plan the success criteria and experimental strategy well. Getting a good handle on what constitutes success is critical, but you can also get away with fewer experiments. Also, you can apply Design of Experiment methodologies where you vary multiple parameters together and require fewer experiments. This approach is used to break apart the plan so steps can be multithreaded, run in parallel instead of series to save time.