Using direct kernel methods to accelerate process improvement with new and emerging technologies within and across supply chains

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This research is critical for maintaining global competitiveness with existing and emerging manufacturing technologies and processes by developing a software infrastructure to assist in increasing desired outputs while reducing time and cost necessary to obtain a deeper level of understanding. An increased rate of learning offers: process yields improvements, performance enhancement, and facility and supply chain optimization. To obtain these goals, one must extract the greatest amount of process knowledge from available data. In many cases, huge amounts of data are collected, but suitable methods for extracting the potential value out of this information is lacking. For example, substanital process data are available for every circuit or MEMS device made in a semiconductor foundry. Alternatively with emerging process technologies such as nanotechnology or biotechnology, little is understood about the different critical factors and ideal settings to optimize product quality and output. Consequently, an analytical platform that identifies and characterizes linear relations, non-linear relations, and the interactions between two or more variables could accelerate: understanding, improvement, application, and commercialization of these processes.