Mr. Roberto R. Tobia Profile

Roberto R. Tobia

at Siemens EDA

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Proceedings Article | 28 April 2023 Poster + Paper

CPU time prediction using machine learning for post-tapeout flow runs

Maram Salah, Ahmed Hamed Fathi Hamed, Stephen Kim, Pascal Gilgenkrantz, Wooil Han, Roberto Ramsis, Ken Jantzen

Proceedings Volume 12495, 124951O (2023) https://doi.org/10.1117/12.2657666

KEYWORDS: Data analysis, Data modeling, Machine learning, Feature extraction

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Each day, semiconductor manufacturing companies (fabs) run distributed compute-intensive post-tape-out-flow runs (PTOF) jobs to apply various resolution enhancement technology (RET) techniques to incoming designs and convert these designs into photomask data that is ready for manufacturing. This process is performed on large compute clusters managed by a job scheduler. To minimize the compute cost of each PTOF job, various manual techniques are used to choose the best compute setup that produces the optimum hardware utilization and efficient runtime for that job. We introduce a machine learning (ML) solution that can provide CPU time prediction for these PTOF jobs, which can be used to provide compute cost estimations, provide recommendations for resources, and feed scheduling models. ML training is based on job-specific features extracted from production data, such as layout size, hierarchy, and operations, as well as meta-data like job type, technology node, and layer. The list of input features correlated to the prediction was evaluated, along with several ML techniques, across a wide variety of jobs. Integrating an ML-based CPU runtime prediction module into the production flow provides data that can be used to improve job priority decisions, raise runtime warnings due to hardware or other issues, and estimate compute cost for each job (which is especially useful in a cloud environment). Given the wide variation of expected runtimes for different types of jobs, replacing manual monitoring of jobs in tape-out operation with an integrated ML-based solution can improve both the productivity and efficiency of the PTOF process.

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