Statistical Software
For Hi-Tech and Semiconductor Industry
Visualization and analysis of semiconductor data pose unique challenges due to its size and volume. When dealing with hundreds or thousands of variables, and possibly millions of observations, you need an analysis package that is fast, flexible, extensive, and extendable. JMP data analysis software from SAS allows scientists and engineers to function more effectively by providing them with a tool that is easy to use, contains hundreds of statistical routines from simple to complex, and is customizable using a built-in scripting language.
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NXP
"Data is worthless if we don’t have the right tools to work with it, so we need to be using the newest and most innovative means to manage this data. JMP uses the most innovative and effective methods, and that’s very important… And because JMP is so intuitive, anyone can achieve useful analytics."
Corinne Bergès | Manager, Risk Assessment, Statistical and Safety Analysis and Six Sigma
JMP Capabilities for Hi-Tech and Semiconductor Industry
Design of Experiments
Generate both modern and classical designs. Design split and strip plot experiments for hard-to-change factors and multi-step processes. Use Definitive Screening designs to minimize experimental runs while optimizing experimental information. Create a design to fit your experiment instead of forcing your experiment to fit the design.
Model-Driven Multivariate Control Charts
Employ a modern approach to process control that considers more than one input at a time.
Process Automization and Standardization
Use the JMP Scripting Language to automate repetitive or error-prone tasks involving data access, cleaning, and analysis. Extend JMP’s functionality by creating custom routines. Use the Application Builder and Dashboard Builder to deploy analytics to a wider audience by creating easy-to-use reports and applications. Run SAS, MATLAB, R, or Python scripts from inside of JMP.
Visualization
Use drag-and-drop graphing to quickly explore data. Show custom graphs such as wafer maps, test pad layouts, and track systems. Use interactive filtering and variable swapping to quickly find important relationships.
Advanced Predictive Modeling
Build, tune, and test models using a variety of methods, including neural networks, tree-based algorithms, partial least squares, or support vector machines, with one click. Export the results to SAS, Python, C, or JavaScript to use in production environments.
Reliability Analysis
Choose from dozens of distributions, from simple to complex, for modeling lifetime data. Design and analyze accelerated testing experiments. Develop block diagrams to access system reliability and perform simulations on repairable systems.