TAILIEUCHUNG - Giáo trình:Process Improvement

An ideal way to run this experiment would be to run all the 4x3=12 wafers in the same furnace run. That would eliminate the nuisance furnace factor completely. However, regular production wafers have furnace priority, and only a few experimental wafers are allowed into any furnace run at the same time. Non-Blocked method A non-blocked way to run this experiment would be to run each of the twelve experimental wafers, in random order, one per furnace run. That would increase the experimental error of each resistivity measurement by the run-to-run furnace variability and make it more difficult to study the effects of the different dosages. The blocked way to run this experiment,. | y V A Ạ A Process Giáo trình y V Ạ Improvement 5. Process Improvement ENGINEERING STATISTICS HANDBOOK hW tools raids lỉEÂtCH BACK Nixf 5. Process Improvement 1. Introduction 2. Assumptions 1. Definition of experimental design 2. Uses 3. Steps 1. Measurement system capable 2. Process stable 3. Simple model 4. Residuals well-behaved 3. Choosing an Experimental Design 1. Set objectives 2. Select process variables and levels 3. Select experimental design 1. Completely randomized designs 2. Randomized block designs 3. Full factorial designs 4. Fractional factorial designs 5. Plackett-Burman designs 6. Response surface designs 7. Adding center point runs 8. Improving fractional design resolution 9. Three-level full factorial designs 10. Three-level mixed-level and fractional factorial designs 4. Analysis of DOE Data 1. DOE analysis steps 2. Plotting DOE data 3. Modeling DOE data 4. Testing and revising DOE models 5. Interpreting DOE results 6. Confirming DOE results 7. DOE examples 1. Full factorial example 2. Fractional factorial example 3. Response surface example http div898 handbook pri 1 of 2 5 1 2006 10 30 17 AM 5. Process Improvement 5. Advanced Topics 1. When classical designs don t work 2. Computer-aided designs 1. D-Optimal designs 2. Repairing a design 3. Optimizing a process 1. Single response case 2. Multiple response case 4. Mixture designs 1. Mixture screening designs 2. Simplex-lattice designs 3. Simplex-centroid designs 4. Constrained mixture designs 5. Treating mixture and process variables together 5. Nested variation 6. Taguchi designs 7. John s 3 4 fractional factorial designs 8. Small composite designs 9. An EDA approach to experiment design 6. Case Studies 1. Eddy current probe sensitivity study 2. Sonoluminescent light intensity study 7. A Glossary of DOE Terminology 8. References Click here for a detailed table of contents sematTech rT00Li 4 AIK S-EAftCH BACK NEXT http div898 handbook pri 2 of 2 5

• Điện - Điện tử
• Select process variables and levels
• Completely randomized designs
• Randomized block designs
• Full factorial designs
• Fractional factorial designs
• Plackett Burman designs