1 / 24

Testing for Faults, Looking for Defects

Testing for Faults, Looking for Defects. Vishwani D. Agrawal James J. Danaher Professor Department of Electrical and Computer Engineering Auburn University, Auburn, AL 36849 http://www.eng.auburn.edu/~vagrawal vagrawal@eng.auburn.edu IEEE Latin American Test Workshop, March 2011 Keynote

schuyler
Download Presentation

Testing for Faults, Looking for Defects

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Testing for Faults, Looking for Defects Vishwani D. Agrawal James J. Danaher Professor Department of Electrical and Computer Engineering Auburn University, Auburn, AL 36849 http://www.eng.auburn.edu/~vagrawal vagrawal@eng.auburn.edu IEEE Latin American Test Workshop, March 2011 Keynote NYUAD Seminar, April 2011, Invited Talk Testing for Faults, Looking for Defects

  2. VLSI Chip Yield • A manufacturing defect is a finite chip area with electrically malfunctioning circuitry caused by errors in the fabrication process. • A chip with no manufacturing defect is called a good chip. • Fraction (or percentage) of good chips produced in a manufacturing process is called the yield. Yield is denoted by symbol Y. • Cost of a chip: Cost of fabricating and testing a wafer  Yield × Number of chip sites on the wafer Testing for Faults, Looking for Defects

  3. Clustered VLSI Defects Good chips Faulty chips Defects Wafer Clustered defects (VLSI) Wafer yield = 17/22 = 0.77 Unclustered defects Wafer yield = 12/22 = 0.55 Testing for Faults, Looking for Defects

  4. Yield Parameters • Defect density (d ) = Average number of defects per unit of chip area • Chip area (A ) • Clustering parameter (a) • Negative binomial distribution of defects, p (x ) = Prob(number of defects on a chip = x ) Γ(α+x ) (Ad / α) x =  .  x ! Γ (α) (1+Ad / α) α+x where Γis the gamma function α = 0, p (x ) is a delta function (max. clustering) α =  , p (x ) is Poisson distr. (no clustering, William/Brown) Testing for Faults, Looking for Defects

  5. Yield Equation Y = Prob( zero defect on a chip ) = p (0) Y = ( 1 + Ad / α ) – α Example: Ad = 1.0, α = 0.5, Y = 0.58 Unclustered defects: α =  ,Y = e – Ad Example: Ad = 1.0, α = , Y = 0.37 too pessimistic ! Testing for Faults, Looking for Defects

  6. Defect Level or Reject Ratio • Defect level (DL) is the ratio of faulty chips among the chips that pass tests. • DL is measured as defective parts per million (dpm, or simply ppm). • DL is a measure of the effectiveness of tests. • DL is a quantitative measure of the manufactured product quality: • For commercial VLSI chips a DL higher than 500 dpm is considered unacceptable. • Chip manufacturers strive for much lower defect levels. Below 100 dpm means high quality. • Zero-defect refers to 3.4 dpm or below. Testing for Faults, Looking for Defects

  7. Determination of DL • From field return data: Chips failing in the field are returned to the manufacturer. The number of returned chips normalized to one million chips shipped is the DL. • From test data: Fault coverage of tests and chip fallout rate are analyzed. A modified yield model is fitted to the fallout data to estimate the DL. Testing for Faults, Looking for Defects

  8. Modified Yield Equation • Three parameters: • Fault density, f = average number of stuck-at faults per unit chip area • Fault clustering parameter, b • Stuck-at fault coverage, T • The modified yield equation: Y (T ) = (1 + TAf / β) –β Assuming that tests with 100% fault coverage (T =1.0) remove all faulty chips, Y = Y (1) = (1 + Af / β) – β Testing for Faults, Looking for Defects

  9. Defect Level Y (T ) - Y (1) DL (T ) =  Y (T ) ( β + TAf ) β = 1 –  ( β + Af ) β Where T is the fault coverage of tests, Af is the average number of faults on the chip of area A, β is the fault clustering parameter. Af and β are determined by test data analysis. , Y (T ) = e –TAf and DL(T ) = 1 – Y (1)1 –T b =  Testing for Faults, Looking for Defects

  10. Example: SEMATECH Chip • Bus interface controller ASIC fabricated and tested at IBM, Burlington, Vermont • 116,000 equivalent (2-input NAND) gates • 304-pin package, 249 I/O • Clock: 40MHz, some parts 50MHz • 0.8m CMOS, 3.3V, 9.4mm x 8.8mm area • Full scan, 99.79% fault coverage • Advantest 3381 ATE, 18,466 chips tested at 2.5MHz test clock • Data obtained courtesy of Phil Nigh (IBM) Testing for Faults, Looking for Defects

  11. Test Coverage from Fault Simulator Stuck-at fault coverage Vector number, V Testing for Faults, Looking for Defects

  12. Measured Chip Fallout Measured chip fallout, 1 – Y(d ) Vector number, V Testing for Faults, Looking for Defects

  13. Model Fitting Unclustered faults: 1 – e– TAf Af = 0.31, β =  Y (1) = 0.7348 Clustered faults: 1 – (1+TAf/β)– β Af = 2.1, β = 0.083 Chip fallout and computed 1-Y (T ) Y (1) = 0.7623 Measured chip fallout Stuck-at fault coverage, T Testing for Faults, Looking for Defects

  14. Computed Defect Level (1 – 0.7348)×106 (1 – 0.7623)×106 Unclustered faults, β =  Clustered faults, β = 0.083 Defect level (dpm) Stuck-at fault coverage (%) Testing for Faults, Looking for Defects

  15. Reexamine Assumption • Assumption: 100% fault coverage leads to zero defect level. • Reality: 100% defect coverage leads to zero defect level. • Must examine the two coverages. Testing for Faults, Looking for Defects

  16. Fault vs. Defect Coverage Fault coverage, T(V ) Defect coverage, D(V ) • Coverage = % of stuck-at faults detected by vectors. • Faults are countable. • Alternative definition: T (V )= Prob (detection by V vectors | a fault is present) • All faults assumed equally probable on a faulty chip. • Determined theoretically. • Coverage = % of real defects detected by vectors. • Many types, large numbers. • Alternative definition: D (V ) = Prob (detection by V vectors | a defect is present) • Each defect may have a different probability of occurrence. • Determined experimentally. Testing for Faults, Looking for Defects

  17. Defect Coverage D (V ) = Prob (detection by V vectors | defect present) Prob(detection by V vectors and defect present) =  Prob(defect present) or 1 – Y (d = 1) 1 – Y (d ) =  Y(d = 1) is true yield 1 – Y (d = 1) Measured yield, Y (d )and estimated true yield can provide a statistical estimate for defect coverage. Source of inaccuracy: true yield, Y(d = 1), is not known. Testing for Faults, Looking for Defects

  18. Defect and Fault Coverages Defect coverage D(V ) from test data Y(d =1) = 0.7623 Fault coverage T(V ) from fault simulator Coverage Vector number (V ) Testing for Faults, Looking for Defects

  19. Defect vs. Fault Coverage D > T Defect coverage, D D < T Fault coverage, T Testing for Faults, Looking for Defects

  20. Conclusion • Defect coverage can be determined from the measured test data. • Assumption: • Either, tests are capable of activating the defect (Q: Can a delay defect be detected by slow-speed stuck-at fault tests?) • Or, the real defect is clustered with faults detectable by the tests. • The above assumption, “DL = 0 at f = 100%,” may be justified since fault coverage appears to be more pessimistic than defect coverage. • Defect coverage D (V ) is a transformation of test data: • Vector 0 → coverage 0% • Vector → coverage 100% • Unclustered fault assumption adds pessimism. Testing for Faults, Looking for Defects

  21. Future Directions • Defect density, d, should not be confused with defect coverage, D (V ): • d = number of defects per unit area • D (V ) = percentage of all possible defects detected by V vectors • Analyze test data for yield, defect coverage and defect level without involving modeled faults. Experiment Y Chip fallout fraction Fraction of chips Vectors, V 0 1.0 Prob(defect occurrence) Testing for Faults, Looking for Defects

  22. Directions . . . • Diagnosis: Defects do not conform to any single fault model. • Question: Which is better? • 100% coverage for one fault model, or • some coverage for multiple fault models Testing for Faults, Looking for Defects

  23. Directions . . . • Generate tests for defect coverage and diagnosis. • Question: which is better? • 100% stuck-at fault coverage, or • 100% diagnostic coverage of stuck-at faults, or • N-detect tests (longer tests), or • Any of the above + random vectors. Testing for Faults, Looking for Defects

  24. References • The clustered fault model used for Sematech data is described in the book: M. L. Bushnell and V. D. Agrawal, Essentials of Electronic Testing for Digital, Memory and Mixed-Signal VLSI Circuits, Springer, 2000, Chapter 3. • The unclustered defect model is from the paper: T. W. Williams and N. C. Brown, “Defect Level as a Function of Fault Coverage,” IEEE Trans. Computers, vol. C-30, no. 12, pp. 987-988, Dec. 1981. • The discussion on defect coverage is from a presentation: J. T. de Sousa and V. D. Agrawal, “An Experimental Study of Tester Yield and Defect Coverage,” IEEE International Test Synthesis Workshop, Santa Barbara, California, March 2001. • A direct analysis of defect level without involving the stuck-at fault coverage is given in the paper: S. C. Seth and V. D. Agrawal, “On the Probability of Fault Occurrence,” Defect and Fault Tolerance in VLSI Systems, I. Koren, editor, Plenum Publishing Corp., 1989, pp. 47-52. Testing for Faults, Looking for Defects

More Related