Design of Experiments
Design of Experiments (DOE) Tutorial
Design of Experiments (DOE) techniques enables designers to determine simultaneously the individual and interactive effects of many factors that could affect the output results in any design. DOE also provides a full insight of interaction between design elements; therefore, it helps turn any standard design into a robust one. Simply put, DOE helps to pin point the sensitive parts and sensitive areas in designs that cause problems in Yield. Designers are then able to fix these problems and produce robust and higher yield designs prior going into production. Here is a simple and practical example that walks you through the basic ideas behind DOE. This example is meant to illustrate the concept in a very easy way to understand DOE and enables you to go on and expand your knowledge on DOE further. DOE Example: Let us assume that we have designed an amplifier and we need to design an experiment to investigate the sensitivity of this amplifier to process variation. In other words, we would like to find out if there are any elements in the design that largely affect the output response due to their high sensitivities to the output measure. In ADS, the DOE tool comes with full supporting plots that enable designers to determine simultaneously the individual and interactive effects of many factors that could affect the output results in any design. Pareto plots, main effects and Interactions plots can be automatically displayed from the Data Display tool for study and investigation. However, in this example DOE is illustrated using a manual calculations approach in order to allow you to observe how the analysis and results are calculated, and what these results mean.
Let us start with our amplifier example: In this example, let us chose three elements where we want to see their effects on the Gain of the amplifier. These elements are: W (the width of the microstrip lines), a resistor (R), and a Capacitor (C). Since we chose three
Design of Experiments (DOE) techniques enables designers to determine simultaneously the individual and interactive effects of many factors that could affect the output results in any design. DOE also provides a full insight of interaction between design elements; therefore, it helps turn any standard design into a robust one. Simply put, DOE helps to pin point the sensitive parts and sensitive areas in designs that cause problems in Yield. Designers are then able to fix these problems and produce robust and higher yield designs prior going into production. Here is a simple and practical example that walks you through the basic ideas behind DOE. This example is meant to illustrate the concept in a very easy way to understand DOE and enables you to go on and expand your knowledge on DOE further. DOE Example: Let us assume that we have designed an amplifier and we need to design an experiment to investigate the sensitivity of this amplifier to process variation. In other words, we would like to find out if there are any elements in the design that largely affect the output response due to their high sensitivities to the output measure. In ADS, the DOE tool comes with full supporting plots that enable designers to determine simultaneously the individual and interactive effects of many factors that could affect the output results in any design. Pareto plots, main effects and Interactions plots can be automatically displayed from the Data Display tool for study and investigation. However, in this example DOE is illustrated using a manual calculations approach in order to allow you to observe how the analysis and results are calculated, and what these results mean.
Let us start with our amplifier example: In this example, let us chose three elements where we want to see their effects on the Gain of the amplifier. These elements are: W (the width of the microstrip lines), a resistor (R), and a Capacitor (C). Since we chose three