design of experiments (doe) for semiconductor processing (3 days)

​​Experiments are performed today in many research organizations to increase our understanding and knowledge of various scientific principles and processes. Experiments are often conducted in a series of trials or tests which produce quantifiable outcomes. One of the common approaches employed by many scientists and engineers today in experimentation is One-Variable-At-a-Time (OVAT), where we vary one variable at a time keeping all other variables in the experiment fixed (or constant).

This approach depends upon guesswork, luck, experience, and intuition for its success. Moreover, this type of experimentation requires large resources to obtain a limited amount of information about the scientific principle or process. OVAT experiment often is unreliable, inefficient, and time-consuming, and may yield false transfer functions for the scientific process. 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 then to design an experiment so that valid, reliable, and sound conclusions can be drawn effectively, efficiently, and economically. DOE-DLS is a full 3-days classroom training.

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.

Features: What specific aspects do you cover in this training that deliver the key benefits? • 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: How will it help me in my job? How will it help the organization or company?

• Helps the experimenters execute experiments for maximum gain and lowest cost across all functional areas in new product, process and service development

• The Organization benefits with lowest cost successful experimental projects.

• New products and processes are developed with the optimal performance and sweet spots of performance are discovered for the existing products, processes and services.

Digital Lean Sigma® Curriculum 19 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

. 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


• 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 Pre-Requisites

Next Schedule Date and Location:

Only offer at clients site

Price:$29000USD for up to 20 students/ Each Additional attendee will be charged a fee $850 USD




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