Design of Experiments with Cornerstone and Its Impact on Machine Learning
Download the Cornerstone e-book to learn how Design of Experiments supports process modeling, optimization, and machine learning with structured statistical methods.
All white papers
Optimizing D-Optimal Designs in Cornerstone
Handle categorical factors, constraints, forced runs, and more. Discover how Cornerstone’s enhanced algorithm boosts your design’s efficiency.
Transforming Factors & Responses for Improved Regression
Learn how Cornerstone allows factor and response transformations, enabling more accurate polynomial regression with optional Box-Cox offset.
Optimizing Wafermap Designs with Zernike Polynomials
Discover how Zernike polynomials and optimized sampling points boost wafer homogeneity, efficiency, and yield in semiconductor processes.
Visualizing Principal Components in Cornerstone
See how Cornerstone’s PCA reduces big datasets and uses tile maps to simplify loadings interpretation, helping you analyze up to 114 predictors.
Big Data Access & Analysis with Cornerstone
Overcome big data project pitfalls as you harness Cornerstone’s direct access to large datasets, advanced modeling, and AI-driven classification methods.
Achieving Leading-Edge Yield with Cornerstone Analytics
Explore how a top semiconductor IDM leverages Cornerstone for faster yield learning, unified data analysis, and superior quality standards.
The Multi-Category Chart for Exploratory Categorical Data
Discover a new graphical tool that handles up to 100 categorical variables, offering deeper root cause insights than classic histograms or parallel plots.
Cornerstone Big Data Analysis with Apache Spark
Discover how Cornerstone uses Apache Spark to handle large datasets, overcoming performance hurdles and unlocking valuable insights for smart ecosystems.
Design of Experiments Workflow for Wafer Uniformity in Semiconductor Manufacturing
Learn how design of experiments (DoE) reduces runs and streamlines contact hole etching, boosting wafer uniformity in semiconductor manufacturing.