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Stringing together the individual unit processes in a semiconductor workflow has its own set of analytical challenges. Compound that with fine-tuning those processes to meet the needs of the world’s most demanding customers and industries, and you get a situation where you need flexibility and easy workflows from your analytical tools.
From troubleshooting existing processes and equipment to developing the new methods needed to support the next bleeding-edge technology, process and equipment engineers must have the flexibility to shift between goals and tasks quickly. Moreover, they must be able to work with a broad range of data types and sources and design experiments to tune processes quickly and economically.
Developing insight into how and why a device may fail years in the future is a challenging problem that requires unique understanding of device physics, statistics, probability and modeling.
Quality engineers need to work across domains and with diverse data sets – everything from customer acceptance to statistical process control data and everything in between. Data analysis is also necessary to locate the root cause of problems, outline corrective actions and identify improvement opportunities resulting in improved efficiency and waste reduction.
boost in job satisfaction among engineers
improved process capability index
Create custom experimental series to maximize the information you get from each wafer.
Optimize your process faster by getting more information out of each experimental lot than you have before.
Leverage machine learning, AI and classic data mining techniques to find hidden insights and quickly identify potential root causes of problems.
JMP links dynamic graphics with powerful statistics to interactively share findings. Explore your data without leaving the analysis flow or having to rerun commands as new questions arise.
Leverage the Process Screening platform in JMP – our quality dashboard – to quickly determine which variables require attention and identify out-of-control events before they become excursions.
Investigate data integrity by more efficiently identifying unexpected patterns in data, even with large data sets.
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