Towards a Decision Support System for a Wafer & Chip plant

1. Towards a Decision Support System for a Wafer & Chip plant Wim Smit & Wim HendriksenEmbedded Systems Group HAN University, Arnhem, The Netherlands

2. Contents of the presentation • The advent of 300 mm diameter wafers • The need for better decision support tools • A typical decision process in the SC industry • The mathematical model formation process • Construction of a simulation model • Playing with the simulation model • Some conclusions and possible follow-up

3. The advent of 300 mm wafers • Some identified consequences: • a further ‘massification’ of the industry • a very high initial investment: approx. $ 3 billion • a very high annual sales needed: > $ 6 billion • a shake-out of the industry is expected • All kinds of partnering and cooperation's • Increasing need for better decision support tools

4. Volatility of the SC-market

5. A typical decision processin the SC industry (1) • Characterized by among others: • many people and profs are involved • many steps and some take a long time • many new technologies are involved • very high financial risks • Many uncertainties: • in type of products, • in the markets and • in the technology

6. A typical decision processin the SC industry (2) • A multi-dimensional and multi–disciplinary decision space • In such environments, decisions can not be seen anymore as the sum of small decisions of all the different parts of the process • Needed: a decision support system which is ‘holistic’ and which ‘integrates’ all decisions

7. Requirements for a DSS in the SC industry • Able • to “catch” all the mental models of the persons involved to synchronize the various insights • perform “what-if” analyses • develop a “feeling” for the dynamic dimension to define - in a straightforward way - the “opportunity windows” • to create a vision of “coherence”

8. A possible road to meet the challenge • Discern the following five steps in making an adequate DSS: 1) the issue raising phase 2) the mental model formation process 3) the mathematical model formation 4) the creation of a simulation model 5) finally the creation of the DSS

9. Step 1: the issue raising phase • Is characterized by collecting all the issues considered to be of importance • The output is often a long list of • statements, • wishes, • requirements, etc. made by the many stakeholders • The list contains the “building stones” for all further steps; it is important to collect the list in a very profound and detailed way

10. Step 2: the mental model formation process • The issues from step 1 are mapped and clustered in different groups • The clustering is very important, because the clusters will play a dominant role in the subsequent steps • A “cause-effect” diagram is constructed from these clusters, so the structure of the dependencies is identified and fixed

11. Step 3: the mathematical model formation • The “cause-effect” diagram of step 2 is now ‘translated’ in a set of mathematical equations • Some equations are algebraic, some are so-called differential equations, linear and non-linear, etc. • There are many feedbacks in the “cause-effect” diagram, and hence we will find these in the mathematical model

12. Step 4: creation of the simulation model • This model is a ‘translation’ of the mathematical model formulated in the previous step • The choice of the simulation language is of great importance • In the selection criteria we should include e.g. simplicity, flexibility, graphics power, allow modular programming, etc.

13. Step 5: creation of the final Decision Support System • The simulation model, formulated in step 4, is the base of the DSS • The DSS should be • user-friendly, and • capable to be used by many different users, e.g. by general managers, strategists, marketing & sales persons, financial experts, engineers, R&D persons, operations managers, etc. • In short the DSS should be “versatile”

14. The MatLab simulation model

15. Simulation results at low market demand D

16. The model in module form

17. The IBM case reconstructed • From literature we could derive that in the start up stage of their new 300 mm wafer fab the following data are valid: • Tbset = 10 months; • INV = $ 4 billion, so FC0 = $ 33 million/ month; • b = 450 chips/wafer • The relevant simulation results are given in the next four sheets; the calculated payback time [PBT] was about 26.1 months.

18. Wp, Ws and D as f(t)

19. Yield and chip price as f(t)

20. c.R, wafer cost and FC0 as f(t)

21. AR, ATC and INV as f(t)

22. Sensitivity analysis on the IBM case (1) • If we vary b, T1 and T2 around the found base situation, we find, for the dependency of the payback time PBT, the following equation: dPBT = -6.10-2.db + 1.33.dT1 – 0.8.dT2 • Increasing b is very effective in making PBT smaller! As expected, a decrease of the yield time constant [T1] and an increase of the time constant of the price erosion [T2] both cause a smaller PBT

23. Sensitivity analysis on the IBM case (2) • The chip price at t = 0, P0, has a strong influence on the PBT. The relation is: dPBT = -7.2.dP0so $ 1 per chip more reduces the PBT with 7.2 months! • The above relationships show that the payback time is not only determined by the technical performance, but also largely by the performance of the sales people

24. Sensitivity analysis on the IMB case (3)

25. Some conclusions (1) • The simulation model as derived shows a reasonable fit with reality • With the model “what-if” questions can indeed be answered • Via a sensitivity analysis the important parameters could be determined • As it is now the model can most likely become a part of an advanced DSS

26. Some conclusions (2) • The model should be validated and verified in a better way with most likely proprietary data of some companies • Also the model should be expanded with an inventory module to have a stocking means for times when D < Ws • Besides, the simulation of the number of chips from one wafer should be improved by introducing in the model new types of wafer steppers

27. Follow-up • We envision • to expand the model further, and • to make the model more adequate for the production stage, i.e. normal annual data will be introduced in the model • Finally, by using the model in the industry, we hope to discover which type of questions are most asked to the model, and this will be the base for a spec of the DSS

28. Steps towards a DSS • The following models should be created: 1) the base model 2) the inventory model 3) the equipment supplier model 4) the model for the behaviour of the CEO 5) the financial risk model 6) the model for the volatility of the market 7) the DSS model with specialization modules