Incremental Multiple-Scan Chain Ordering for ECO Flip-Flop Insertion

1. Incremental Multiple-Scan Chain Ordering for ECO Flip-Flop Insertion Andrew B. Kahng, Ilgweon Kang and Siddhartha Nath VLSI CAD LABORATORY, UC San Diego 32nd IEEE/ACM International Conference on Computer-Aided Design November 20th, 2013

2. Outline • Motivation • Related Work • Problem Formulations • IMSCO Flow • Experimental Results • Conclusions and Future Works

3. Motivation • Engineering Change Orders (ECOs) are IC design changes close to tapeout • The testability of ECO logic is very challenging to the design schedule • To avoid loss of test coverage, ECO flip-flops (FFs) must be added for ECO logic

4. Challenges for ECO FF Insertion • ECO FFs should be distributed among existing scan chains to minimize test time • Only a subset of existing scan chains will be compatible with ECO FF depending on clock domain • Existing routing should be minimally perturbed to minimize impact on timing and existing routing congestion • ECO FF insertion flow should be automated • Manual ECO FF insertion near tapeout can cost days or weeks of design time

5. Why Is The Problem Difficult? How to tradeoff wirelength, test time, and impact to existing timing and routing? Compromise between wirelength and #edges perturbed Many edges perturbed (routing disturbance) Large incremental wirelength (timing impact) Chain 1 Chain 1 Chain 1 ECO FF Insertion Requires Good Heuristics

6. Our Work • New Incremental Multiple-Scan Chain Ordering (IMSCO) formulation • Minimize test time (or scan chain depth) • Minimize incremental wirelength and congestion • Affects setup timing slacks and routability of ECO changes • Minimize disturbance of the existing routing and timing • By minimizing number of edges that are modified • Develop heuristics for ordering scan chains based on • Traveling Salesman Problem (TSP) • Develop Incremental Scan Chain solver (ISC-solver) tool that • implements IMSCO heuristics to minimize test time, wirelength and routing disturbance • shows promising results

7. Outline • Motivation • Related Work • Problem Formulations • IMSCO Flow • Experimental Results • Conclusions and Future Works

8. Relationship to TSP • TSP: Given a set of cities, find a minimum-cost tour that visits every city exactly once • In IMSCO • City :: Scan FF • Cost :: Wirelength • Our problem • Multiple salesmen • Multiple starting points  mTSP Scan chain ordering can be formulated as TSP

9. Prior Works • Two broad classifications • Clustering and assignment of scan FFs • Ordering of assigned scan FFs • Clustering and assignment • Elm et al. [2008] present partitioning heuristics to cluster scan FFs into scan chains • Seok et al. [2006] use placement information to divide a scan chain into multiple chains • Ordering • Feuer and Koo [1983] first use TSP for scan chain optimization • Gupta et al. [2003] propose routing-driven and timing-driven methodology to order scan chains

10. Outline • Motivation • Related Work • Problem Formulations • IMSCO Flow • Experimental Results • Conclusions and Future Works

11. Minimize Test Time Scan depth of chain 2 > scan depth of chain 1 Scan depth of chain 2 = scan depth of chain 1 Not good solution  Good solution  Chain 1 Chain 1 Chain 2 Chain 2

12. Minimize Incremental Wirelength • We can minimize timing impact to existing scan chains • We can reduce routing congestion Smaller incremental wirelength  Potentially less impact to existing routing Larger incremental wirelength Not good solution  Good solution  Chain 1 Chain 2 Chain 1 Chain 2

13. Minimize #Cut Edges • We can minimize the disturbance to existing routing • (Major changes to existing routing may break previously-achieved timing closure) Two cut edges disturb existing routing One cut edge reduces routing disturbance Not good solution  Good solution  Chain 1 Chain 1

14. Outline • Motivation • Related Work • Problem Formulations • IMSCO Flow • Experimental Results • Conclusions and Future Works

15. Overall Flow Input: Original Scan Chains, ECO FFs, Constraints (1) Construction of Initial Clustering (Affinity) (2) Improvement of Initial Clustering (modified FM) (GainWL) (3) Selection of Multiple Cut Edges (k-way clustering, GainWL_byCE) Output: Clustering and Ordering of ECO FFs with Multiple Cut Edges

16. Initial Clustering • Affinity: Weighted sum of scan depth (SD), wirelength (WL) and #cut edges (CE) affinities between ECO FF and original scan chain • Distances for WL affinity are calculated differently for congestion- and non-congestion-aware modes • Non-congestion mode: Manhattan distance between FFs • Congestion mode: Calculate Affinity of ECO FF to all compatible scan chains Assign to scan chain Find and pair with largest affinity Ordered list of ECO FFs per scan chain with min WL Invoke TSP-solver (Concorde) Repeat red blocks for all remaining

17. Improvement of Initial Clusters • Invoke FM (iterative hill-climbing) algorithm for each ECO FF from scan chain to scan chain • Search for ECO FF that maximizes • Move from to • Remove three edges (two from and one from ) • Add three edges (one from and two from ) • Fix to • Invoke TSP-Solver (Concorde) to order FFs to minimize WL 2 2 5 6 Chain 1 Chain 2 Chain 1 7 Chain 2 F 8 11 5

18. Selection of Multiple Cut Edges • Disconnect two longest edges of ECO FFs in a cluster for each chain • Reconnect sub-clusters to nearest original scan FFs • Calculate and find • Retain new connections if • Invoke TSP-solver (Concorde) to order FFs to minimize WL • We can reduce the incremental wirelength even more • We select multiple cut edges by using greedy k-way clustering • For ECO scan FFs in a given scan chain

19. Outline • Motivation • Related Work • Problem Formulations • IMSCO Flow • Experimental Results • Conclusions and Future Works

20. Experimental Setup • Develop ISC-solver to implement three-phase heuristics • Written C++ with several user configurations • Compiled with g++ 4.8.0 • Concorde as TSP-solver • Validated on 12-core HT Intel Xeon E5-2640 2.5GHz, 128GB RAM server • Testcases • Industrial (from industry partners) • Artificial with our configurable scan instance generator • User options include layout size, #scan chains, #scan FFs per scan chain, #ECO FFs, congestion map, ….

21. ISC-Solver User Parameters • psd: Max % increase in scan depth allowed in chain after ECO FFs insertion • mel: Max edge length between scan FFs • To avoid use of high-leakage LVT cells • lel: Min edge length between scan FFs • To avoid the need for excessive hold buffer insertion • mce: Max #cut edges in each individual scan chain • MCE : Max #cut edges in the entire design

22. Option: psd • psd  maximum scan depth 

23. Option: mel • melwirelength 

24. Option: lel • lel  wirelength 

25. ISC-Solver Example Solutions mce is max #cut edges per scan chain Final chain WL is 4116 when mce = 1 Final chain WL is 3818 when mce = 10 ISC-solver reports smaller WL when mce increases

26. ISC-Solver: Congestion-Awareness Congested regions Non-congestion-aware Congestion-aware Cut edges ISC-solvergenerates morecut edges to avoid high congestion area

27. Comparison to Industrial Results • Industrial testcase • 320 scan chains • 634 ECO FFs • 7 compatible scan chain groups • 5.3% reduction in SD (no additional test time) from manual • 45.71% reduction of incremental WL compared to manual RECOMMENDED SOLUTION !!

28. Outline • Motivation • Related Work • Problem Formulations • IMSCO Flow • Experimental Results • Conclusions and Future Works

29. Conclusions and Future Works • IMSCO provides automated flow to improve testability of ECO logic in SOC implementation flow • ISC-solver implements clustering, incremental clustering and ordering heuristics • Compared to manual solutions, ISC-solver achieves • 5.3% of test time reduction • 45.71% reduction in incremental wirelength • Future works • Code optimizations to speedup ISC-solver • Connections to operations research literature, e.g., via dynamic MDVRP with variable number of movable depots

30. Thank You!

31. Affinity Calculation Scalability • Time complexity = #scan FFs; = #ECO FFs = 49300 #scan chains = 100 Memory Usage ≤ 36MB

32. Option: mce • mce  wirelength  • mce  # of cut edges 