Course Overview
This comprehensive 3-day course covers Design of Experiments (DOE) for semiconductor processing. Statistical thinking and statistical methods play an important role in planning, conducting, analyzing, and interpreting data from scientific and engineering experiments.
When several variables influence a certain characteristic of an item or principle, the best strategy is to design an experiment so that valid, reliable, and sound conclusions can be drawn effectively, efficiently, and economically. This course moves beyond the inefficient One-Variable-At-a-Time (OVAT) approach to provide systematic experimental design methods.
Course Objectives and Skills Gained
- Develop the ability to identify and quantify sources of variations, perform measurement systems analysis
- Develop the ability to develop a hypothesis for testing and test hypotheses
- Develop capability for designing appropriate design of experiments (DOE)
- Develop skills for executing the designed experiments, collecting experimental data, and analyzing data
- Develop capabilities for integrating data and systems for seamless functionality
Key Features & Benefits
Features:
- Provides skills to understand, segment, and quantify various sources of variation
- Provides tools to develop appropriate Designs for the experiments to be performed
- Provides tools to prioritize variables that will optimize the performance of the product or process
- Provides tools to conduct designed experiments and collect data
- Provides tools to analyze the result of the experiments and translate those into practical applications
Benefits:
- Helps experimenters execute experiments for maximum gain and lowest cost across all functional areas
- Organization benefits with lowest cost successful experimental projects
- New products and processes are developed with optimal performance
- Sweet spots of performance are discovered for existing products, processes and services
Course Outline
Introduction, Background, and Overview
- Variation and Sources of Variation
- Measurement System Analysis
- Hypothesis testing
- An Introduction to Design of Experiments (DOE)
- Basic principles of DOE
- Overview of basic statistical concepts
- Types and purposes of DOE methods
Full Factorial Design
- ANOVA
- The basics of "full factorials"
- Factorial effects and plots
- Model evaluation
Two-Level Fractional Factorial Design
- Objective
- The one-half fraction and one-quarter of the 2k design
- The general 2k-p fractional factorial design
- Resolution III, IV and V designs
The Robust Design
- The basics of robust designs
- Taguchi designs
- A short robust design example
The Response Surface Methodology
- From first-order experiments to second-order experiments
- Analysis of second-order response surfaces
- Central composite designs
- Box-Behnken design
- Process optimization
Multiple Case Studies and Applications
Practical examples and real-world applications of DOE methods in semiconductor manufacturing.
Who Should Attend
Typically offered to Scientific, R&D, and new product/process development personnel. Staff scientists, R&D Engineers, Process Engineers, Design Engineers, IT Engineers, R&D Managers, Senior R&D Managers, and others related to New Product and Process Development or performance optimization tasks.
Prerequisites
Basic understanding of statistical concepts and semiconductor manufacturing processes is recommended.