Li Jiang Qiang Xu and Bill Eklow CU hk RE liable C omputing Laboratory Department of Computer Science amp Engineering The Chinese University of Hong Kong ID: 557414
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Slide1
On Effective TSV Repair for 3D-Stacked ICs
Li Jiang
†
,
Qiang
Xu
†
and Bill
Eklow
§
†
CU
hk
RE
liable
C
omputing Laboratory
Department of Computer Science & Engineering
The Chinese University of Hong Kong
§
Cisco, CA,USSlide2
OutlineIntroduction
Motivation
TSV Redundancy Architecture
Routing Heuristic for Timing Consideration
Discussion & ConclusionSlide3
3D Product and To appear
3D stacked DRAM Package
PCB
TSV
DRAM
I/O Buffer
RD/
WR
CMOS Image Sensor
Interposer based 2.5D FPGA
Memory on Processor
More TSV
More Complicated
Requires manufacturing yield to be commercially viableSlide4
The Impact of 3D Stacking on Yield
Stack Yield
Loss
Misalignment
Impurity
Open
Short
Leak &
Delaminating
Void &
Break
Assembly Yield
Loss
Leveraged by
KGD
test and D2W stackingSlide5
Clustered TSV Defects
Assembly Yield
is dramatically affected by TSV clustered faults
Source: IMEC
Bond pad short
Unsuccessful fillSlide6
TSV Repair Schemes:
Neighboring Repair
1 Spare TSV
N TSV Chain
2 Spare TSVs
4 Signal TSVs
Redundancy
Ratio
Signal-Switching
Signal-Shifting
Source:
Kang
, SAMSUNG
Source: H.H-
S.Lee
,
GATech
M
Spare TSV rows
N
x
N TSV grid
Crossbar
Source:
Loi
,
U. BolognaSlide7
Motivation
Signal-Shifting
Signal-Switching
Random faults
Clustered
TSV faults
Due to
Surface Roughness,
Wafer bow, alignment error
Redundancy
Ratio: 1/2
Crossbar
To overcome: Repair faulty TSV from redundancy far apartSlide8
Motivation
Repair with TSV far-apart by topology mapping
High repairing flexibility
Router based TSV grid
Reduce the
cost of
spare
TSVs
Reduce the router complexity
Problem: TSV redundancy architecture and repair algorithmsSlide9
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
MxNSlide10
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
routabilitySlide11
Rerouting Scheme
Edge Disjoint Paths Problem
Maximum Flow Method
Repair Channel
Repair PathSlide12
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) Slide13
HeuristicDiagonal Direction Grouping
Bounded
BFS
Search (Length Bound & Maximal Hops)
Maximal Hops
= 2Slide14
Experiment Setup
Shifting: 2:1
Switching: 4:2
Crossbar: 8:2
router: 4x4:8,8x8:16
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
ComparisonSlide15
Experimental Results
Compound Poisson Distribution with Fixed TSV Failure Rate as 0.5%
Alpha: Clustering Effect 0.4~3
1000 TSV
10000 TSVSlide16
Experimental Results
Compound Poisson Distribution with Fixed TSV Failure Rate as 0.5%
Alpha: Clustering Effect 0.4~3
100000 TSVSlide17
100000 TSV
Experimental Results
1000 TSVSlide18
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 solutionsSlide19
Thank you for your attention !