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Yield Enhancement for 3D-Stacked Memory by Redundancy Sharing across Dies

Yield Enhancement for 3D-Stacked Memory by Redundancy Sharing across Dies. Li Jiang, Rong Ye and Qiang Xu Presenter : Qiang Xu CU hk RE liable C omputing Laboratory Department of Computer Science & Engineering The Chinese University of Hong Kong. Outline. Introduction Motivation

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Yield Enhancement for 3D-Stacked Memory by Redundancy Sharing across Dies

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  1. Yield Enhancement for 3D-Stacked Memory by Redundancy Sharing across Dies Li Jiang, Rong Ye and QiangXu Presenter: QiangXu CUhkREliableComputing Laboratory Department of Computer Science & Engineering The Chinese University of Hong Kong

  2. Outline • Introduction • Motivation • Redundancy Sharing in 3D-Stacked Memory • Die Matching for Yield Enhancement • Conclusion

  3. Why 3D-stacked Memory? • Small • Die Size • Routing Cost • Reduced • Bus Cap • Latency • Large • Bandwidth Memory Wall CPU-Memory Performance Gap Relative Performance 1980 1990 2000 Now

  4. DRAM TSV 4 Gbit density RD/WR I/O Buffer TSV PCB Interposer 8 strata Peripherals 3 Gbps/pin 3D-Stacked DRAM are Already Here … 2002 Tezzaron: 1Gb,SDRAM NEC:4Gb 2006 Higher Bandwidth SamSung:8Gb 2009 IMEC: DRAM+Logic Closer to Processor Faster GATech+Tezzaron 2010

  5. More self-reparable dies High redundancy cost! To Guarantee Yield for 3D-Stacked Memory … Memory Test: Fault Bitmap × Redundancy Analysis Stack Self-Reparable Dies

  6. Redundancy Analysis for Reparability 1R,2C,Irreparable 1R,2C,Self-Reparable

  7. Redundancy Sharing for Yield Enhancement 1R,2C,Irreparable 0R,3C,Reparable 2R,1C,Reparable 1R,2C,Self-Reparable With the same amount of resources, memory yield can be improved by redundancy sharing!

  8. Redundancy Sharing across Dies Pre-fabricated multiplexor Programmable Decoder Full sharing: NumTSV = NumSpare Row + NumSpare Col What if there are defective TSVs? Repair its own block. Use the rest to repair others

  9. Partial sharing : Less TSVs Redundancy Sharing across Dies Programmable Decoder Pre-fabricated multiplexor

  10. Matching is Critical for the Final Yield Conservative matching: Yield = 50% Self-reparable matching: Yield = 25% Effective matching: Yield = 75% Aggressive matching: Yield = 0%

  11. How to Conduct Die Matching? • Add edges if two • dies are reparable with • redundancy sharing Conduct maximum matching algorithm Construct an undirected graph with each die as an vertex

  12. How to Conduct Die Matching? • How do we know whether two dies are reparable after bonding? • Run final repair algorithm between every pair • Best yield, but time-consuming • We have to estimate whether two dies matched together can form a reparable stack efficiently

  13. Die Matching a.t. Reparability Condition • Fr: faulty bits suitable for row repair • Fc: faulty bits suitable for column repair • Fo: orthogonal faulty bits

  14. Die Matching a.t. Reparability Condition Matched Dies Self-reparable Dies Optimal Matched Dies Matching a.t. reparability condition is rather conservative

  15. Irreparability Condition • Given a bipartite graph G = (V;E), the minimum number of vertices that cover all the edges is equal to the number of edges in any maximum bipartite matching of the graph • Given two memory blocks with redundancy R/C • The maximum bipartite matching of Ga, Gbare |Ma| and |Mb|, the stacked memory is considered to be “reparable” if |Ma| +|Mb| ≤ Ra + Ca +Rb + Cb

  16. Die Matching a.t. Irreparability Condition Reparability is NOT guaranteed due to redundancy configuration!

  17. Die Matching a.t. Irreparability Condition Matched Dies a.t. Irreparability Condition Self-reparable Dies Reparable Dies a.t. Irreparability Condition Optimal Matched Dies Matching a.t. irreparability condition is rather aggressive

  18. Iterative Die Matching • Iterative matching a.t. tightened irreparability condition in each run 0 + |Ma| +|Mb| ≤ Ra + Ca +Rb + Cb Matched Dies a.t. Irreparability Condition Reparable Dies a.t. Irreparability Condition Optimal Matched Dies

  19. Iterative Die Matching • Iterative matching a.t. tightened irreparability condition in each run 1 + |Ma| +|Mb| ≤ Ra + Ca +Rb + Cb Rest of Dies Matched Dies a.t. Irreparability Condition Reparable Dies a.t. Irreparability Condition

  20. Iterative Die Matching • Iterative matching a.t. tightened irreparability condition in each run 2 + |Ma| +|Mb| ≤ Ra + Ca +Rb + Cb

  21. Iterative Die Matching • Iterative matching a.t. tightened irreparability condition in each run k + |Ma| +|Mb| ≤ Ra + Ca +Rb + Cb No more reparable dies found

  22. Experiment Setup • 1000 1Gb Memory, stacked to 2 Layer chips • 4×4 memory blocks, 8k×8k bit-cells • Fault Injection • Poisson distribution with λ = 2.130 • Polya-Eggenberger distribution with λ=2.130 • α = 2.382 (more clustered faults) • α = 0.6232 (evenly-distributed faults) • random TSV faults with faulty rate as 0.1% • six kinds of faults

  23. Experimental Results Poisson Distribution Self-reparable Reparability Matched Irreparability Irreparability Iterative

  24. Experimental Results Case 1 Self Repair Reparability Matched Irreparability Irreparability Iterative Case 2

  25. Experimental Results Polya-Eggenberger Distribution Self Repair Reparability Matched Irreparability Irreparability Iterative α = 0.6232 α = 2.38

  26. Summary • We propose to conduct redundancy sharing across vertical dies in 3D-Stacked Memory • Significant yield enhancement • Minor TSV and routing cost • We present novel solutions for selective die matching to maximize 3D-stacked memory yield

  27. Thank you for your attention !

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