1. Hantao Huang1, Hao Yu1, Cheng Zhuo2 and Fengbo Ren3
ACM ISPD’16
A Compressive-sensing based Testing Vehicle for 3D TSV Pre-bond and Post-bond Testing Data
Hantao Huang1, Hao Yu1, Cheng Zhuo2 and Fengbo Ren3
1Nanyang Technological University, Singapore
2Intel Corporation, USA
3Arizona State University, USA 1

2. 1 2 3 4 5 Outline Introduction TSV I/O Test Test Data Compression
Experiments 5
Conclusion 2

3. Data Analytics Challenge
Data center for future big-data- oriented society:
Leaving data outside a nation will face serious cyber-security concern
Processing data inside a nation by traditional Giga-scale system has high cost
Bandwidth at 100 Gps with Space of 20,000 sq. ft. Power of 68 MW and cost of 100M-USD !!! 3

4. 2.5D and 3D Integration
Memory/logic integrated on one common substrate by through-silicon interposer (TSI) I/O
Traditional 2D integration is non-scalable for bandwidth
3D has best bandwidth scalability but poor thermal dissipation
2.5D provides good scalability of bandwidth and also thermal dissipation 4

5. Challenges on 3D-IC with TSVs
[1] Lee, Hsien-Hsin S., and Krishnendu Chakrabarty. "Test challenges for 3D integrated circuits." Design & Test of Computers, IEEE 26.5 (2009): 5

6. 1 2 3 4 5 Outline Introduction TSV I/O Test Test Data Compression
Experiments 5
Conclusion 6

7. TSV I/O Test TSV Failure Mechanism Pin holes in dielectric
Void due to TSV fabrication
Electron Migrations …
TSV Test Mechanism
TSV Test by probe in pre-bond phase
TSV elevator in post-bond phase
Limited Bandwidth for testing data: Only probe/elevator for data transfer 7

8. Pre-bond TSV Testing
Fig. a gives a simple example to detect open, short and connected faults in TSV interconnection.
Fig. b and c show how the mismatch pitch of probe head is resolved by using scan-chain flip- flop (SF).
In Fig. c only the top TSV has the I/O drive-ability, which means all the data is collected from other TSV SF by the top SF.
Conceptual diagram for pre-bond TSV test 8

9. Post-bond TSV Testing
Fig. right shows a conceptual post-bond testing diagram where built-in-self-test (BIST) circuit is shared between different TSV groups.
The signal generated from BIST is scheduled and sent to router.
Router will send the signal to TSV group driver for testing purposes by a sequence of digital bits similar as in the pre-bond test. 9

10. TSV Test Vehicles with Data Compression
Supported pre-bond die test, post-bond stack test and board level test
Test wrapper provides test access mechanism (TAM) and send/receive test data through I/O
Pad and TSV elevator provides data transfer, which has limited bandwidth
Test Data Compaction (TDC) provides data compression to fulfill high speed data communication requirement. 10

11. Outline 1 2 3 4 5 Introduction TSV I/O Test Test Data Compression
Experiments 5
Conclusion 11

12. Previous Test Data Compressions
Lossy Test Data Compression [1]
XOR: Simple with high aliasing rate (detection failure)
Multi-input Signature Register (MISR): good compression rate with low aliasing rate and no diagnostic information
Lossless Test Data Compression [2]
Length Run Coding: encoding runs of zeros using fixed length codes.
Golomb Coding: encoding runs of zeros using a variable length code words
Test Data compression
Scan out
Scan in 1
Scan in 2
Scan in n
Out 1
Out 2
[1] McCluskey, E. J., et al. "Test data compression." Design & Test of Computers, IEEE 20.2 (2003):
[2] Chandra, Anshuman, and Krishnendu Chakrabarty. "System-on-a-chip test-data compression and decompression architectures based on Golomb codes."Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on 20.3 (2001): 12

13. New Problem Formulation
The main proposed problem here is to fully utilize the compressive sensing to compress the TSV test data with high data compression rate under the given error probability while ensuring a lossless recovery.
where Xr Є RN and Xe Є RN denote the received and expected signal through TSV for pre-bond test or scan chain for post-bond test and Y Є RM is the output result. Φ is the compressive sensing matrix. Efault Є RN is defective TSV index in the pre-bond test, while for the post-bond test, . Efault represents the error bits introduced by faulty ICs. 13

14. Sparsity Analysis of Test-data
TSV Yield Analysis, overall probability of pre-bond have x defective TSV is
Post-bond IC faulty-free probability estimated as Poisson distribution
When defective error is sparse, signal difference between received and expected is sparse, too.
Data here is defined:
Yideal –Yactual
sparse data for yield (Ypre) of 95%
sparse data for yield (Ypre) of 99% 14

15. Sparse Test-Data Compression
Efaulty
The scan chain output and the expected output from the probe pad are XORed to obtain the difference matrix, Efaulty , which is normally sparse with 1 to denote the error/failure.
Fig. above shows how to compress a sparse signal from N to M measurements. The compression rate is calculated as 1-M/N. 15

16. Lossless Compression and Recovery (I)
The lossless recovery can be formulated as L0-norm minimization problem as below
Where Efault is N dimensional sparse signal and Φ is the sensing matrix RMxN and Y is the sparse representation data in low dimension RN (M<<N).
The minimum required sampled data is
Where K is the sparsity of the test data and can be estimated from the yield.
(a) L1 norm solution in 2D example (b) L2 norm solution in 2D example 16

17. Lossless Compression and Recovery (II)
OMP performs three functions:
1. Finding the most correlated column from
the sensing matrix Φ by comparing simple dot multiplication.
2. Adding the largest correlated column to the
selected column
3. Solving a L2 norm minimization to generate the most fitted new signal.
Above procedures will repeat K times to find the expected signal
Orthogonal Matching Pursuit (OMP) complexity analysis at iteration t:
Atom searching: 2nm
L2-norm minimization : mt^3+t^3/3
Update: 2mt
Atom searching is the most time consuming due to the large n but least squares problem is the most complex task. 17

18. Testing flow for proposed testing vehicle18

19. Outline 1 2 3 4 5 Introduction TSV I/O Test Test Data Compression
Experiments 5
Conclusion 19

20. Results and Comparison
Pre-bond Test
TSV
No.
Failure Prob.
Cluster
Proposed LR
Coding
GLC
D=8
D=16
4096
0.5%
α =0
89.45%
81.80%
76.83%
79.98%
α =1
89.70%
81.14%
77.29%
80.18%
α =2
89.32%
81.52%
76.68%
79.76% 1%
65.29%
50.99%
35.57%
44.38%
65.03%
50.73%
43.80%
66.48%
51.52%
37.16%
45.85%
16384
0..5%
89.16%
80.37%
75.95%
79.11%
89.23%
80.15%
73.99%
77.28%
89.35%
80.47%
75.59%
78.89%
64.80%
49.42%
34.08%
43.49%
64.29%
46.93%
29.49%
38.37%
64.86%
50.62%
34.51%
43.84%
65536
89.17%
79.77%
73.48%
76.86%
89.21%
79.73%
73.09%
76.63%
89.24%
79.38%
73.49%
65.32%
50.03%
34.27%
43.35%
64.59%
48.57%
34.19%
43.41%
64.88%
47.08%
36.68%
40.21%
X-axis and Y-axis represent the location of TSV. The average failure probability is 20 % and due to the clustering effect, the failure probability can be as high as 81.52% for the TSVs close to the center.
Compression rate is as high as 89.45% 20

21. Results and Comparison
Post-bond Functional Test for ISCAS 85 benchmark in Verilog and Matlab
Output data compression for pre-bond and post-bond TSV test via compressed sensing is discussed.
Experiment results with benchmarks have shown that 89.70% pre-bond data compression rate can be achieved under 0.5% error probability; and 88.18% post-bond data compression rate can be achieved with 5% error probability.
Error
Probl.
Bench
mark
Output
( bits)
Proposed LR
Coding
GLC
D=8
D=16 5%
c499
1696
88.15%
78.22%
73.01%
76.42%
c432
196
88.18%
77.96%
76.38%
78.57%
c1908
2475
86.89%
73.69%
72.83%
76.34%
c2670
6300
82.56%
73.81%
69.77%
73.74%
c3540
1870
87.76%
80.55%
75.75%
78.72%
c5315
4674
81.80%
75.31%
71.13%
74.97%
c6288
416
85.65%
81.15%
82.21%
84.13%
c7552
7992
80.30%
74.27%
68.26%
72.19%
10%
73.82%
59.53%
50.17%
57.17%
82.19%
68.67%
63.06%
66.94%
70.57%
56.00%
45.86%
53.27%
61.42%
55.17%
44.25%
51.80%
70.82%
56.56%
46.24%
53.76%
63.71$
52.38%
40.88%
49.14%
76.06%
58.51%
49.83%
56.49%
60.99%
54.45%
43.95%
51.55% 21

22. Outline 1 2 3 4 5 Introduction TSV I/O Test Test Data Compression
Experiments 5
Conclusion 22

23. Conclusion
In this paper, the testing data compression is discussed for pre-bond and post-bond TSV testing via compressive-sensing based method.
By exploring the sparsity of the testing data, one can achieve on-chip data compression and lossless off-chip data recovery.
The encoding for compression can be easily implemented on-chip using XOR and AND networks with significantly improved bandwidth for the output of the testing data.
Experiment results with benchmarks have shown:
89.70% pre-bond data compression rate can be achieved under 0.5% failure probability;
88.18% post-bond data compression rate can be achieved with 5% failure probability.
That’s concludes my presentation and thank you for attending this talk. 23 23 23

24. Thank You! http://www.ntucmosetgp.net
That’s concludes my presentation and thank you for attending this talk.
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