IEE 591 Spring 2000
FOR SEMICONDUCTOR PROCESSING
Class: T,Th 5:15-6:30, SCOB 301
Instructors: George C. Runger, Goldwater Center 530, telephone (480) 965-3193, firstname.lastname@example.org
David Drain, Intel Corporation, email@example.com
Office hours: George Runger: TTh 1:30-3:00, other times by appointment.
Web site: http://www.eas.asu.edu/~masmlab/iee591/
Engineering Statistics, by D. C. Montgomery, G. C. Runger, and N. F. Hubele, John Wiley and Sons, 1997 with Minitab software enclosed
Statistical Methods for Industrial Process Control
Handbook of Experimental Methods for Process Improvement both by David Drain, Chapman and Hall, 1997
Introduction to Statistical Quality Control, by D.C. Montgomery, John Wiley & Sons, 1997
About the course:
00000000A course in statistical process control and improvements through designed experiments that focuses on semiconductor processing
Intended for engineers, and physical/chemical scientists, and deals with the types of control charts and experiments that are frequently run in industrial settings
A basic working knowledge of introductory statistical methods would be useful background, but introductory material will be covered at the start of the course. A formal course in engineering statistics at the level of ASE 485 would be strong preparation for this course.
The introductory material that will be covered at the start of the course includes the following:
Compute and interpret the sample mean and standard deviation,
Use the normal distribution,
Test a hypothesis (the t-test, for example),
Construct and interpret a confidence interval
Fit a model using the method of least squares
Compute basic probabilities for risk assessment
Reason statistically from a sample to a process
. Plan, design, conduct, and analyze experiments efficiently and effectively.
Opportunities to use the principles taught in the course arise in all phases of engineering work, including new product design and development, process development, and manufacturing process improvement. Methods will be customized to semiconductor manufacturing and examples will be drawn from this field. Some important modifications to standard methods are needed for semiconductor processes.
All experiments conducted by engineers and scientists are designed experiments; some of them are poorly designed, and others are well designed. The well-designed ones allow you to obtain the desired results faster, easier, and with fewer resources. That’s what you will learn how to do in this course. A well-designed experiment can lead to reduced development lead-time for new processes and products, improved manufacturing process performance, and products that have superior function and reliability.
Computer software: Computer software to implement the methods presented will be illustrated, and you will have opportunities to use it for homework assignments and project. The textbook includes a version of Minitab and the campus labs provide Minitab. Any one of several commercial packages can be used and the relationships between these and Minitab should be easy to follow. Design of experiments work will require a version of Minitab that runs on the campus labs.
Grading: Your grade in the course will be determined by two mid-term exams (40%), a final exam (30%) and projects (30%).
Homework and Projects: Important!! You should work as many exercises from the book as you feel are necessary to become familiar with the material. These will not be turned in, but selected solutions will be provided. Small projects will be assigned approximately every two weeks that will be more comprehensive applications of the material. These will be turned in and they will sometimes require you to collect and analyze data present to the class. No proprietary information should be used. Additional details will be provided.