1. Process Variation Mohammad Sharifkhani

2. Reading Textbook, Chapter 6 A paper in the reference

3. Introduction What is process variation? –Deviation from intended or designed values Types: –Environmental Arise during circuit operation –Power supply, temprature, etc. –Physical Parametric variation of the process –Processing, masking, etc.

4. Introduction The variation do not scale as much! –Check ITRS Parameters under variation for interconnects: –W, T, H, p (resistivity) Why is it important?

5. Introduction Why is it important? –Interpath correlation  Yield The max delay of K path in a lot of chips

6. Introduction Intergate correlation –  Yield

7. Introduction Impact on performance

8. Introduction Impact on Price!

9. Sources of Variation (Lithography) Lithography –Sub-wavelength lithography

10. Sources of Variation: Lithography Optical Proximity Correction (OPC) –modifies layout to compensate for process distortions –Add non-electrical structures to layout to control diffraction of light Rule-based or odelbased

11. Sources of Variation: Lithography

12. Gate Length Variation Horizontal variation

13. Chemical Mechanical Planarization(CMP)

14. Etch/polishing variation CMP) Vertically affects the wire caps, resistance, etc.

15. Erosion and Dishing

16. Stress Induced Variability

17. Random Doping Variation –RDF: Random location and distribution of the dopant atoms  Vth variation of 10/sqrt(W) mV/um½

18. Environmental Sources: Temperature

19. Environmental Sources: IR Drop

20. Categorization

21. Intra-die –Within die variations –Due to Layout patterns (e.g, two interconnects) –Systematic, lithography, lens, etc. Wafer level trends (e.g, slanted plane) –Induces mismatch (between different paths) Inter-die –Variation between nominally identical dies (on the same wafer) –Shift in the mean of a parameter (Vth, wire width) –Simplified distributions are possible to capture variance –Easier to model based on systematic trends on the wafer (bowl shape  Speed)

22. Inter die vs. Intra die

23. Temporal variations

24. Temporal variation (short scale)

25. Lumped statistics Regardless of the reason behind variation –Combination of reasons Find the mean, variance for individual parameters Assuming uncorrelated Results in worst case scenarios

26. Survey of process variation Device geometry variation –Film thickness variation Gate oxide; critical, yet well controlled. Causes Inter-die variation –Lateral dimension variation Lithography limitation, lens, etching, etc.; affects effective length and width, both inter and intra die variation Channel length variation dominates output current characteristics, vth, etc.

27. Survey of process variation Device material parameter variation –Doping variation; affects junction depth, threshold voltage Drain eng. (Halo) gives rise to variation Intra-die variation –Deposition variation; affects the resistivity (silicide and metal) Variation in Contact and Line differences

28. Effect on device electrical parameter variation –V th variation; geometric variations, charge trapped in oxide and RDF; 10% of the Vth of the smallest device –Leakage current  exponential relationship with Vth Survey of process variation

29. Vth variation

30. Leakage vs. Freq.

31. Survey of process variation Effect on interconnect electrical parameter variation –Line width and line space variation; the smaller the worse  resistance variation and capacitance variation  coupling variation –Metal thickness; resistance variation (up to 20% of line thickness can be etched) –Dielectric thickness; coupling capacitance variation; deposition can vary up to 5%, polishing –Contact and via size; resistance variation due to etching, layer thickness

32. Modeling variation Statistical modeling –Model parameter extraction; We can not measure L or W, but Ids for a given Vgs. Model fitting using a number of devices. It is difficult and very inaccurate The process itself changes in time Worst case analysis and design is prefered

33. Worst-case analysis If we assume all varying parameters are uncorrolated, we end up with an overly pessimistic situation

34. Worst case analysis Finding worst case situation that actually happens –Worst case models (corners) –Provided by manufacturing companies –If a design passes the design spec, it provides an acceptable Yield

35. Spatial variation modeling (mismatch) Have long been studied in Analog circuits –The variance in the mismatch is twice as much as the variance of individual variables (if they are uncorrelated) –If they are close  correlation Layout information needed ahead of time –If averaging happens  less variance (e.g., larger devices mismatch  area)

36. Example An example will be shown in Timing section