On Effective TSV Repair for 3D-Stacked ICs

1. On Effective TSV Repair for 3D-Stacked ICs Li Jiang†, QiangXu† and Bill Eklow§ †CUhkREliableComputing Laboratory Department of Computer Science & Engineering The Chinese University of Hong Kong § Cisco, CA,US

2. Outline • Introduction • Motivation • TSV Redundancy Architecture • Routing Heuristic for Timing Consideration • Discussion & Conclusion

3. DRAM TSV RD/WR I/O Buffer PCB 3D Product and To appear CMOS Image Sensor Interposer based 2.5D FPGA Memory on Processor 3D stacked DRAM Package More TSV More Complicated Requires manufacturing yield to be commercially viable

4. The Impact of 3D Stacking on Yield Stack Yield Loss Leveraged by KGD test and D2W stacking Assembly Yield Loss Misalignment Impurity Open Short Leak & Delaminating Void & Break

5. Clustered TSV Defects Assembly Yield is dramatically affected by TSV clustered faults Source: IMEC Bond pad short Unsuccessful fill

6. TSV Repair Schemes: Neighboring Repair Signal-Shifting Crossbar Signal-Switching Source: Kang, SAMSUNG Source: H.H-S.Lee, GATech Source: Loi, U. Bologna 1 Spare TSV N TSV Chain M Spare TSV rows N x N TSV grid 2 Spare TSVs 4 Signal TSVs RedundancyRatio

7. Motivation RedundancyRatio: 1/2 Random faults ClusteredTSV faults Due to Surface Roughness, Wafer bow, alignment error Signal-Switching Crossbar Signal-Shifting To overcome: Repair faulty TSV from redundancy far apart

8. Motivation Reduce the cost of spare TSVs High repairing flexibility Repair with TSV far-apart by topology mapping Reduce the router complexity Router based TSV grid Problem: TSV redundancy architecture and repair algorithms

9. Router Based TSV Redundancy • Successively Signal Rerouting • TSV Grid and Signal Routing Infrastructure • Repair faulty TSV with nearest good TSV, and continue until a redundant TSV is used M+N Spare TSVs M X N TSV Grid M+N MxN

10. Switch Design • Direction of Rerouting • North to south, West to east (2 direction) • Bypassing signal • Allow multi-hop signal rerouting • More Complex Design for more routability

11. Rerouting Scheme Edge Disjoint Paths Problem  Maximum Flow Method Repair Channel Repair Path

12. Problem Formulation • Maximum Flow Method with 1 edge capability • Find Repair Path in Flow Graph (edge disjoint) • Timing Constraint  Length Constraint • Decision making in flow graph, affecting following solution • Transfer the problem by finding Repair Channel • Length Bonded Maximum Flow (NP-Hard)

13. Heuristic • Diagonal Direction Grouping • Bounded BFS Search (Length Bound & Maximal Hops) Maximal Hops = 2

14. Experiment Setup Shifting: 2:1 Switching: 4:2 Crossbar: 8:2 router: 4x4:8,8x8:16 Comparison Vary TSV Number: 1000 ~ 10000 ~ 100000 • Fault Injection: • Poisson Distribution varying failure rate • Compound Poisson Distribution varying cluster effect • Timing Constraint: • Assuming equal distances between neighboring TSVs • Length constraint: 3 – 1 times of the distance

15. Experimental Results Compound Poisson Distribution with Fixed TSV Failure Rate as 0.5% Alpha: Clustering Effect 0.4~3 1000 TSV 10000 TSV

16. Experimental Results Compound Poisson Distribution with Fixed TSV Failure Rate as 0.5% Alpha: Clustering Effect 0.4~3 100000 TSV

17. Experimental Results 100000 TSV 1000 TSV

18. Conclusion Cost Effective and scalable Solution to effectively repair clustered TSV faults. • From the cost perspective: • Limited extra Muxes and wires • To achieve the same TSV yield, the required redundant TSVs with the proposed repair scheme is much less than existing solutions

19. Thank you for your attention !