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Automated Volume Diagnostics

Automated Volume Diagnostics. A ccelerated yield learning in 40nm and below technologies. John Kim Yield Explorer Applications Consultant Synopsys, Inc. June 19, 2013. Agenda. Current Challenges Diagnostics vs Volume Diagnostics Analysis Flows with Volume Diagnostics

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Automated Volume Diagnostics

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  1. Automated Volume Diagnostics Accelerated yield learning in 40nm and below technologies John Kim Yield Explorer Applications Consultant Synopsys, Inc June 19, 2013

  2. Agenda Current Challenges Diagnostics vs Volume Diagnostics Analysis Flows with Volume Diagnostics Collaboration between Fab/Fabless Conclusions Korea Test Conference 2013

  3. Systematic Issues Rising Dramatically Trend of Initial Yield Loss by Technode Systematic • Systematic contribution to initial yield losses are worsening at newer technodes • Random Defect issues are also increasing but can be managed with existing methods and infrastructure • Some different methods are needed to address these new mechanisms Design-based yield issues Litho-based yield issues Random Yield Loss (%) Defect-based yield issues Technology Node (nm) **Chart data source - IBS Korea Test Conference 2013

  4. How to address those systematics? • Traditional yield learning methods can address random defectivity sources… • Inline inspections • Technology structural and IP testchips • Single production volume yield learning vehicle • Memory array based detection and FA localization • Various EFA visualization techniques • Litho/DFM simulation • Legacy learning • But what about product/technology specific design and layout systematics? Korea Test Conference 2013

  5. ATPG Diagnostics Based Yield Learning • ATPG Diagnostics based Yield Learning gives us an enhanced level of analysis and characterization capability • Most logic products already use ATPG for automated high test coverage pattern generation • Diagnostics provides very high localization of likely defective region, often down to a few square microns with physical diagnostics usage • Volume diagnostics adds statistical confidence to identify root cause Korea Test Conference 2013

  6. Agenda Current Challenges Diagnostics vs Volume Diagnostics Analysis Flows with Volume Diagnostics Collaboration between Fab/Fabless Conclusions Korea Test Conference 2013

  7. How Logic Diagnostics Work P1:11001010 P2:00011101 P3:10100011 • Assumptions: • Many ATPG patterns • ATE failures recorded from all those patterns • Most faults produce a unique test response signature • Find the fault which most closely matches the defect signature from the ATE P1:PPPPFPP P2:PPPPPPP P3:PFFPPPP P1:PPPPPPP P2:PPFFFPF P3:PFPPPPP Signature for fault A Signature for fault B Following pages provide basics of how scan diagnostics works Korea Test Conference 2013

  8. Good Scan Operation ATE Expect Data Load Data 0 0 1 1 1 0 0 0 0 0 0 0 Scan Chain1 D D Q Q D Q D D Q Q D Q SI SI SI SI SI SI Combinational Logic 0 0 0 0 0 0 0 0 Scan Chain2 Scan Chain Loading System Clock Scan Chain Unloading Korea Test Conference 2013

  9. Scan Operation with Defect ATE Expect Data Load Data 0 1 1 0 0 1 0 0 0 0 0 0 Miscompare Scan Chain1 D D Q Q D Q D D Q Q D Q SI SI SI SI SI SI Combinational Logic 0 0 0 1 1 0 1 Defect 0 Miscompare Scan Chain2 Unloading System Clock Setup Loading Korea Test Conference 2013

  10. Scan Diagnosis Scan Chain1 D D Q Q D Q D D Q Q D Q Miscompare SI SI SI SI SI SI Combinational Logic 0 0 0 1 1 0 1 0 Defect Miscompare Scan Chain2 Korea Test Conference 2013

  11. Diagnostics • Subnet diagnosis enables even further localization of open defects Fail Fail Region Fail Driver Pass Korea Test Conference 2013

  12. What is Volume Diagnostics? • Performs statistical analysis of diagnostics results from multiple failing chips • Identifies systematic, yield-limiting issues by using design data • Provides actionable information on high value candidates for Physical Failure Analysis (PFA) • Can apply to both chain or logic diagnostics So why volume diagnostics vs single diagnostics? Relative Yield Fallout Defect Type Korea Test Conference 2013

  13. Why Volume Diagnostics • To explain why volume diagnostics are important, let’s first consider BINSORT data • What can be concluded from one die of BINSORT data? • Can anything be concluded from this failing die BIN88 • How important is Bin 88 failures on this wafer? • Is it a systematic failure? Korea Test Conference 2013

  14. Why Volume Diagnostics Analysis of a statistically significant volume of data provides a better level of understanding about the failing population • To understand it’s importance and characteristic, need more data to make conclusion • With the inclusion of other dies on this wafermap, it becomes more clear • Bin 88 is unlikely a systematic nor is it important failing BIN on this wafer • Bin 68 is the most important issue here and shows a strong systematic signature Korea Test Conference 2013

  15. Why Volume Diagnostics • Similar to BINSORT example, volume diagnostic analysis of multiple dies/wafers/lots provides clearer picture of the most important systematics on a sample 1 die diagnostics Increase analysis sample to 10 die diagnostics Systematic becomes observable with increased volume No systematics observable Failing Net1 Failing Net1 Failing Net2 Failing Net3 Korea Test Conference 2013

  16. What is Volume Diagnostics? • Volume diagnostics can describe any statistical treatment of diagnostic data (both chain and logic) • It can range from the simple to the extremely sophisticated • Full Automated Volume Diagnostics • Automatic/semiautomatic prefiltering of bad diagnostic data • Analyzes data from multiple directions, with single or multiple variable combinations • Applies statistical tests and intelligent heuristics to interpret and quantify results • Aligns non-diagnostic data sources to enrich understanding • Generates tool files to drive FA equipment to likely source of defects • Basic Volume Diagnostics • Manual parsing of diagnostic datalogs and data manipulation • Simple summing, sorting and filtering to identify strong systematic signals • Manual inspection of results • Manual generation of coordinates for FA team to localize defect Korea Test Conference 2013

  17. Considerations during analysis • Some important details should be considered in volume diagnostics • Should any data be removed prior to analysis? • Are normalization required to interpret the data • How important are the findings, in terms of overall yield impact and statistical significance? • Is there some supporting data to validate the findings? • Is the problem new, or pre-existing? • Are the results something that FA can reasonably isolate? Korea Test Conference 2013

  18. Automated Volume Diagnostics • With Volume Diagnostics, we are usually trying to answer specific questions. For example: • Is there a systematic metal or via location that is repeatedly failing? • Are there standard cells that are failing above their entitlement? • Are there scan chain that are consistently failing? • Is there a design or IP block that is failing above it’s entitlement? • What is the highest yield impact systematic on the analyzed dataset? • Is there a systematic lithography weakpoint associated with a significant number of fails • Were any of the failures observable inline? • There are a large number of possible questions that can be asked. • A comprehensive and flexible system to quickly configure, analyze large amounts of data, and direct the analysts to next steps is necessary for a production volume diagnostic flow Korea Test Conference 2013

  19. Volume Diagnostics – Analysis • For effective volume diagnostics, should minimally provide: • Identification of the systematic observation to it’s smallest resolvable element • Quantification of the systematics in terms of yield impact • Statistical significance of the systematic • Output information sufficient for failure analysis (wafer diexy and within die coordinates) in a format easily consumed by FA labs • Additional information to help FA teams isolate defects and/or test/design/process teams to investigate possible fixes Korea Test Conference 2013

  20. Agenda Current Challenges Diagnostics vs Volume Diagnostics Analysis Flows with Volume Diagnostics Collaboration between Fab/Fabless Conclusions Korea Test Conference 2013

  21. Volume Diagnostics • What are some examples of volume diagnostic analysis results? • Design Based: • Repeating nets or instances • Std Cell systematics • Design/IP block sensitivity • Routing pattern dependency • Scan Chain failures • Timing slack analysis • Voltage/temperature sensitivity • Process Based • Spatial systematics • FEOL, Metal or Via layer systematic opens/shorts • Lot/Lot, Wafer to Wafer variability • Process equipment/history dependency • Test Based • Test Pattern dependency • Tester/Probecard dependency • Or combinations of any of the above Korea Test Conference 2013

  22. Use Case: Which Nets Fail Systematically? • What is the probability of a randomly occuring 5 die repeater of 1 net on 1000 die sample, in a 10 million net design? • p(1000 dies, 10e6 nets, 5 coincident ) ~8 e-16 • -7.9 sigma event • Likelihood of this being a random event is small A net is a unique element on a design. It only occurs once out of possible 10s or 100s of millions of possible nets on a design. Repetitive failures on a net indicate a strong systematic signatures Korea Test Conference 2013

  23. Use Case – Are any std cells failing systematically? • Early in technology development, FEOL issues are prominent • Important to evaluate std cell failures to characterize FEOL systematics Korea Test Conference 2013

  24. Use Case – Are any std cells failing systematically? • Important to use design data to understand fail entitlement to interpret results #1 cell is actually failing at random baseline entitlement. What appeared to be the #2 item is actually the worst when comparing gap vs entitlement Korea Test Conference 2013

  25. Entitlement Gap Discussion • What is an entitlement gap? • This just means that failures aren’t evaluated on an absolute basis • Unfortunately, there is no 100% yield • There is always some baseline amount of failures expected. Our failures need to be compared against the expected amounts to properly conclude it is systematic Korea Test Conference 2013

  26. Some basic concepts • How should we assess the effect of a factor? • Let’s consider the following general case What is the amount of yield loss for Item X Yield Loss for Factor X Is it ~30% ? Item Number Korea Test Conference 2013

  27. Some basic concepts (cont’d) • What if we had additional information about item X? • For example, comparison against yield loss for other elements for that variable? Now, for item 20, what is the interesting quantity? Yield Loss for Factor X We can say that item 20 has a 20% yield loss above the baseline entitlement of 10% loss for mechanism X Item Number Korea Test Conference 2013

  28. Some basic concepts (cont’d) • Is this a reasonable way to look at yield loss mechanisms? • Actually Yield/Product Engineers do this regularly • Consider the familiar Bin Loss Pareto From this data alone, it would appear that Bins 68, 6, and 41are problematic at ~20% yield loss But with the inclusion of the baseline entitlement, it is clear that only Bin 68 is the excursion, and the amount is ~15% This bin pareto by itself isn’t that useful But the inclusion of a reference to understand what the bin losses should be, can be used as the baseline entitlement for each bin. Korea Test Conference 2013

  29. Entitled Bin Value • From the previous example, what could explain why bin 6, 41 are high but not something that is necessarily unexpected? • Consider the situation, where binning is done by major functional block within design Imagine that Bin 6 and Bin 41 cover functional blocks in large portions of the chip, but Bin 68, covers this smaller portion In this case, if the three bins are failing at the same rate, we would suspect that Bin 68 failures have some unique systematic Bin 6 Coverage Bin 41 Coverage 68 Korea Test Conference 2013

  30. Gap Metrics • General formula for gap of a mechanism • In this case • Observed = measured from test, extracted by diagnostics, expressed as % of total dies • Expected = Entitlement quantity, also expressed in % of total dies • Why Gap: • Gap cannot exceed Observed Fail % • i.e. if observed loss is 1%, even if fail rate is very high, gap cannot exceed 1%. Ensures that focus is on high yield impact issues Korea Test Conference 2013

  31. Gap to Model – Basics • Let us consider another familiar example Device B Device A Both devices are designed and manufactured in the same technology (e.g. 28nm) in the same foundry Do you expect the yields to be the same or different? Area Device A = ½ area of Device B < = > YA YB We know intuitively that the larger die should yield less Korea Test Conference 2013

  32. Gap to Model – Basics • Let’s look at another example of this concept • Imagine we are a foundry. We are running 8 different products in the same fab in the same process during the same time period • Yield Summary per device is as follows What conclusion can we make? Is there some device here that is not behaving properly? What is the missing information? Korea Test Conference 2013

  33. Gap to Model – Basics • Let’s include area to see if that helps you come to some conclusion Korea Test Conference 2013

  34. Gap to Model – Basics • Based on the area of each device, can estimate an expected yield using some yield model, defectivity rate and area of each device Now, it’s more clear that device E is misbehaving Korea Test Conference 2013

  35. Use Case – Are any std cells failing systematically? A volume diagnostic analysis tool should be able to use design normalizations and generate expected entitlements for proper interpretation • Important to use design data to understand fail entitlement to interpret results #1 cell is actually failing at random baseline entitlement. What appeared to be the #2 item is actually the worst when comparing gap vs entitlement Korea Test Conference 2013

  36. Volume Diagnostics – Yield Normalization Volume diagnostic analysis should consider the overall yield data on the wafer to understand the true yield impact of the systematic In addition to design normalization, important to normalize results to wafer yield Does the systematic here have the same yield impact on both wafers? Case A: 14/21 dies systematic Case B: 14/21 dies systematic In this wafer, the effect of this systematic has very small yield impact In this wafer, the effect of this systematic has very large yield impact Korea Test Conference 2013

  37. Physical Verification • Use Cases: • Overlay hotspots to failing diagnostic nets or instances • Localize failure to small point for long failing nets Hotspot Overlay to lithoweakpoint simulation hotspot, narrows down failure location to very specific point on one layer This net would be too long for FA without any additional information Korea Test Conference 2013

  38. DFM Hotspot Correlation • In addition to helping FA, a statistical analysis is also important to quantify effect of different hotspots rules on diagnostics failures • Various metrics such as hotspot fail rate, candidate hit rate, etc. are calculated and visualized Korea Test Conference 2013

  39. DFM Hotspot Correlation Fault location from diagnostic log Reported failing cell matches sensitive viabar hotspot location Hotspot location from hotspot file Korea Test Conference 2013

  40. Inline Defect Correlation • Correlate inline defects with diagnostic candidates • Various metrics such as hotspot fail rate, candidate hit rate, etc. are calculated and visualized Korea Test Conference 2013

  41. Inline Defect Correlation • Use inline observed defects to narrow down source of diagnostic failure • For long nets, FA might be difficult. If net is overlaid to an inline defect, can go directly to that location on that layer to help FA localize defect • For FEOL instances, can identify layer that may be source of defect • Use inline observed defects to disqualify candidates from FA • Source already identified inline, doesn’t need additional FA characterization. Better for FA lab to spend time on finding new defects. Skip FA on this candidate. Korea Test Conference 2013

  42. Case Study: Large fallout at Vddmin • Problem: Large Vddmin fallout observed • Solution: Automated Dft to Parametric correlation study performed • Considerations • 1000 cell x 100 parameters ~ 100,000 possible data pairs • Need an automated algorithm that searches through all pairs to find most significant ones Statistical test automatically finds significant pair of results (Cell and parameter) • Follow-on validation of this hypothesis by: • Analyze Split lots (transistor skew lots to validate this finding), and historical trends • Perform Simulations (verify if this parametric behavior could be related to diagnostic signal) • Perform FA (construction analysis to validate this signal) Korea Test Conference 2013

  43. Physical Verification • Use Cases: • STA data alignment with failing instances • Use some static timing analysis results and assign timing slack to failing transition faults These small slack candidates are likely slow path related, and likely mayhave no visible defect These large slack candidates are unlikely timing issues, and are better candidates for FA Without binning transition candidates by slack, it is possible to confuse mechanisms and generate many NDF *Nelly Feldman, ST Microelectronics, Silicon Debug and Diagnostics Conference 2012 Korea Test Conference 2013

  44. Use Case – Correlation to Memories • Modern SOC enables us opportunity to use other product data to help explain diagnostics Leveraging correlated results from bitmap classification vs logic diagnostics, we have ability to Korea Test Conference 2013

  45. Use Case – Correlation to Memories • Using bit classifications correlation to cell fail results from diagnostics, we can attain better understanding about correlated failures •  In this example, these diagnostic FADDX1 cell failures can be investigated by FA of single bit failures Korea Test Conference 2013

  46. Use Case – Via Analysis • In this experiment, failures on Via12C were injected above a background random via fail rate on all other vias. Note, vias that don’t have significant affect on the yield, will not show results from this method due to statistical significance validation Korea Test Conference 2013

  47. Use Case – Via Analysis • Finally, via fail rate values are converted through a yield model into overall yield impact Yield Model transformation is necessary to understand significance of result. A via may have high fail rate but low usage in design, in which case, yield impact many be small even with high fail rates Korea Test Conference 2013

  48. Diagnostic Considerations • Some things to consider when analyzing diagnostics • Equivalent faults • Correlated failures • Diagnostics are heavily resource constrained • Need to make more intelligent use of upstream data to make diagnostics more targeted, biggest bang for the buck Korea Test Conference 2013

  49. Agenda Current Challenges Diagnostics vs Volume Diagnostics Analysis Flows with Volume Diagnostics Collaboration between Fab/Fabless Conclusions Korea Test Conference 2013

  50. Volume Diagnostics Methodology 1000s of Likely FA Sites • Statistically prioritize the candidates from multiple failing dies • Localize likely failure sites by mask layer and segment/Via using correlations More data into volume diagnostics, enables better characterization Diagnostics T B S S S T Yield Explorer Timing Inline LRC List of Top 10 Sites for PFA DRC LEF DEF and Layout Korea Test Conference 2013

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