Machine learning based generic violation waiver system with application on electromigration sign-off

Manually analyzing the results generated by EDA tools to waive or fix any violations is a tedious, error-prone and time-consuming process. By automating these time-consuming rigorous manual procedures by aggregating key insights across different designs using continuing and prior simulation data, a design team can speed up the tape-out process, optimize resources and significantly minimize the risk of overlooking must fix violations that are prone to cause field failures. In this paper, a machine learning based generic waiver system is proposed which continuously learns to improve with new design data using K-means clustering and nearest neighbor algorithms for risk scoring. The system has been used on new designs to demonstrate on-chip Electromigration (EM) waiver (EMWaiver) mechanism that yielded highly confident results.

[1]  Makoto Nagata,et al.  Simulation techniques for EMC compliant design of automotive IC chips and modules , 2017, 2017 International Symposium on Electromagnetic Compatibility - EMC EUROPE.

[2]  Qing He,et al.  Parallel K-Means Clustering Based on MapReduce , 2009, CloudCom.

[3]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[4]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[5]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[6]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

[7]  Shen Lin,et al.  Challenges in power-ground integrity , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[8]  Andrew T. Yang,et al.  Full-chip vectorless dynamic power integrity analysis and verification against 100uV/100ps-resolution measurement , 2004, Proceedings of the IEEE 2004 Custom Integrated Circuits Conference (IEEE Cat. No.04CH37571).

[9]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[10]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[11]  Craig Chambers,et al.  The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing , 2015, Proc. VLDB Endow..

[12]  Man Lan,et al.  Initialization of cluster refinement algorithms: a review and comparative study , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Chung-Kuan Cheng,et al.  MATEX: A distributed framework for transient simulation of power distribution networks , 2014, 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC).

[14]  Craig Chambers,et al.  FlumeJava: easy, efficient data-parallel pipelines , 2010, PLDI '10.