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Process Control in Semiconductors March 28, 2006.

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Presentation on theme: "Process Control in Semiconductors March 28, 2006."— Presentation transcript:

1 Process Control in Semiconductors March 28, 2006

2 Process Control in Semiconductors 2 Agenda What is process control Why is control needed Types of control used Importance of control from a business standpoint How a controller is developed Questions

3 March 28, 2006Process Control in Semiconductors 3 Why do we need Process Control Moore’s Law states that transistors on a chip will double roughly every two years In order to keep up, features on a chip need to decrease in size (e.i. 120 nm, 90 nm, 60nm etc.) The smaller the features the more critical control is towards yields

4 March 28, 2006Process Control in Semiconductors 4 Types of Control Used Run-to-Run Control (RtR): automatic change in the process recipe for a given run based on feed-back data from post-process metrology and feed-forward data from previous operations.

5 March 28, 2006Process Control in Semiconductors 5 Run-to-Run Control (RtR) Variability is shifted from the controlled (output) variables to the manipulated (input) variables Not all variability can be controlled within a given process Output Variables Variability

6 March 28, 2006Process Control in Semiconductors 6 Run-to-Run Control (RtR) Transferring Variability InputOutput Polish Time (s) Film Thickness (A) StDev0440 InputOutput Polish Time (s) Film Thickness (A) StDev7.649 Without RtRWith RtR

7 March 28, 2006Process Control in Semiconductors 7 Run-to-Run Control (RtR) Transferring Variability InputOutput Polish Time (s) Film Thickness (A) StDev0440 InputOutput Polish Time (s) Film Thickness (A) StDev7.649 Without RtRWith RtR Note how the variability is transferred from the film thickness to the input polish time. The deviation in the output (which relates to product quality) is reduced ~10x, while the deviation in the input (which does not relate to product quality) is increased.

8 March 28, 2006Process Control in Semiconductors 8 Importance of Control to the Company Economic Importance –Increase yield –Decrease cycle time Reducing re-work Decreasing bottlenecks Competitive Value (Business Strategies) –Use as trading chips

9 March 28, 2006Process Control in Semiconductors 9 Significance of Increased Yields Theoretical Example Assume 1000 wafers/week are processed Assume 250 good die/wafer Total of 250,000 die per week

10 March 28, 2006Process Control in Semiconductors 10 Significance of Increased Yields (cont.) Theoretical Example If you achieve a 5% yield increase, now you have 262 good die/wafer Total of 262,000 good die/week Increase of 12,000 good die/week Assume avg. selling price is $100/die Revenue increases by $1.2M/week or $60M/year

11 March 28, 2006Process Control in Semiconductors 11 Significance of Cycle Time Theoretical Example Assume 1000 wafers/week are processed Assume 250 good die/wafer Total of 250,000 good die per week

12 March 28, 2006Process Control in Semiconductors 12 Significance of Cycle Time (Cont.) Theoretical Example If you achieve a 5% greater throughput due to less cycle time, now you process 1050 wafer/week. Total of 262,500 good die/week Increase of 12,500 good die/week Assume avg. selling price is $100 Revenue increases by $1.25M/week or $62.5 M/year

13 March 28, 2006Process Control in Semiconductors 13 Business Strategies Trade control IP for transistor knowledge Trade control IP for reduced wafer price (from foundry)

14 March 28, 2006Process Control in Semiconductors 14 Deciding when a new control is needed Proactive approach (R&D) –Anticipated future needs Develop solutions necessary for future products Reactive approach –Quality issues (i.e. bad yield) –Productivity issues Bottleneck tools Poor processing (i.e. re-works)

15 March 28, 2006Process Control in Semiconductors 15 Developing a new controller Identify the process to be controlled –Identify the target outputs –Identify the inputs necessary to control Develop the algorithm –The control model –The control law –EWMA for state estimation Test the controller in a development environment Roll out the controller in the FAB in passive mode Roll out the controller in the FAB in production mode

16 March 28, 2006Process Control in Semiconductors 16 The Control Model The model explains the relationship between the inputs and the output In APC, this is almost always a linear model of the form –y  the output (measurement) –u  the input (control knob) –c  the intercept (output when the input is zero) –b  gain (affect of changing the input on the output)

17 March 28, 2006Process Control in Semiconductors 17 The Control Law Once we have the process model, we can calculate the inputs required to achieve the output target –Let us designate the output target as “T” –Starting with the model –Replace y with T and solve for u

18 March 28, 2006Process Control in Semiconductors 18 Determining the “State” Again, starting with the model Historical data is used to estimate the parameters b or c –Option 1: set c as a constant and estimate b –Option 2: set b as a constant and estimate c Estimating the parameters from historical data is the main focus of APC

19 March 28, 2006Process Control in Semiconductors 19 The EWMA Filter Since things can change over time, newer data is more relevant when estimating the unknown model parameter Some examples are: –CMP pad wear –Tools get dirty (etch deposition, stepper lenses, etc.) –Upstream process changes (layer thickness, material properties, etc.) Measurements are usually discounted exponentially

20 March 28, 2006Process Control in Semiconductors 20 The EWMA Filter Measurements are weighted by an exponentially decreasing factor, ω i = (1-λ) i

21 March 28, 2006Process Control in Semiconductors 21 Choosing the EWMA filter constant, λ When λ is close to 1.0 –Only the most recent measurement is considered –Results in fast responses to process changes –Makes you more sensitive to noise Whenλ is close to 0.0 –All measurements are weighted equally (measurements are averaged) –Less sensitivity to noise –Results in slow responses to process changes Typically, λ is chosen to be between 0.2 and 0.5 The most common choice is λ = 0.3

22 March 28, 2006Process Control in Semiconductors 22 Choosing the EWMA Tuning Parameter, λ =0.1=0.9 Less sensitive to noise Slower response to step disturbances More sensitive to noise Quicker response to step disturbances

23 March 28, 2006Process Control in Semiconductors 23 EWMA Updating Equation If we have enough data, the weight on the oldest measurements is negligible In this case, the EWMA estimate is a weighted average of the new observation and the old estimate

24 March 28, 2006Process Control in Semiconductors 24 Deposition Example Oxide Oxide Thickness

25 March 28, 2006Process Control in Semiconductors 25 Deposition (cont.) Input –Deposition Time Output –Oxide Thickness

26 March 28, 2006Process Control in Semiconductors 26 Deposition (cont.) Control model: –y is the Film Thickness –b is the Dep rate –u is the Dep time –c is the intercept Assume that the intercept is constant Estimate the dep rate, b, from historical data

27 March 28, 2006Process Control in Semiconductors 27 Deposition (cont.) Control Law –Starting with the model: –Solve for Dep Time: Dep thickness = Dep rate * Dep time + intercept

28 March 28, 2006Process Control in Semiconductors 28 Deposition (cont.) Use an EWMA filter with λ =.4 to find EWMA(Dep rate) If the target is T=100, what should the input be for the next run? Historical Data RunThicknessDep TimeInterceptDep Rate (nm)(s)(nm/s)

29 March 28, 2006Process Control in Semiconductors 29 Deposition (cont.) Recall: Therefore: EWMA(dep rate) = 6.64 nm/s EWMA(dep rate) =

30 March 28, 2006Process Control in Semiconductors 30 Deposition (cont.) Recall: Using EWMA(Dep rate): Dep Time = 15.1 s

31 March 28, 2006Process Control in Semiconductors 31 Conclusion Controlling certain processes within nanometers is important to the yield of the product Having tight control effects the economic impact to the company EWMA is a way to estimate a parameter from historical data

32 March 28, 2006Process Control in Semiconductors 32 Questions??

33 March 28, 2006Process Control in Semiconductors 33 Trademark Attribution AMD, the AMD Arrow logo and combinations thereof are trademarks of Advanced Micro Devices, Inc. in the United States and/or other jurisdictions. Other names used in this presentation are for identification purposes only and may be trademarks of their respective owners. © 2005 Advanced Micro Devices, Inc. All rights reserved.


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